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Journal articles on the topic 'Intelligent fuzzy controller'
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1
Nowaková, Jana, and Miroslav Pokorný. "Intelligent Controller Design by the Artificial Intelligence Methods." Sensors 20, no. 16 (2020): 4454. http://dx.doi.org/10.3390/s20164454.
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With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the states and processes. Standard controllers are available and successfully implemented. However, with the data-driven era we are facing nowadays, there is an opportunity to use controllers, which can include much information, elusive for common controllers. Our goal is to propose a design of an intelligent controller–a conventional controller, but with a non-conventional method of designing its parameters using approaches of artificial intelligence combining fuzzy and genetics methods. Intelligent adaptation of parameters of the control system is performed using data from the sensors measured in the controlled process. All parts designed are based on non-conventional methods and are verified by simulations. The identification of the system’s parameters is based on parameter optimization by means of its difference equation using genetic algorithms. The continuous monitoring of the quality control process and the design of the controller parameters are conducted using a fuzzy expert system of the Mamdani type, or the Takagi–Sugeno type. The concept of the intelligent control system is open and easily expandable.
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He, Fang. "The Study on Field Programmable Gate Array Based on PID Controllers in Real Time." Applied Mechanics and Materials 66-68 (July 2011): 1923–29. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1923.
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In the present work PID control and the fuzzy logic based intelligent control is used to control the rectilinear plant vibrations. PID controller in real time mode is implemented on FPGA. For implementation of fuzzy logic based intelligent controller the FPGA is used as real time data acquisition (DAQ) platform and the fuzzy controller is implemented on the host PC. The sole reason for using FPGA as data acquisition platform was that the LabVIEW FPGA software module is not supporting the complex mathematics required for the fuzzy logic control and also the rectilinear plant interface with the traditional DAQ was not available. The comparative study of performance of intelligent controllers like fuzzy PD and fuzzy PI+ fuzzy PD is done with the conventional controllers on the basis of above mentioned performance indices. The result of the fuzzy PI + fuzzy PD controller is found to be the best among all.
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S.R. Krishnam Naidu, R., and U. Salma. "Real- time implementation of parallel type fuzzy- PID controller for effective control of hybrid Pole self bearing Switched reluctance motor." International Journal of Engineering & Technology 7, no. 2.21 (2018): 112. http://dx.doi.org/10.14419/ijet.v7i2.21.11847.
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This paper presents a hybrid intelligent and design in a real time for the rapid prototyping of a robust fuzzy controller along with conventional Proportional– Integral and Derivative (PID) controller that allows quick insight of these integrated designs. The design procedure of the parallel fuzzy PID and its combination with the traditional PID in a universal control scheme are extended. The structural design of the parallel fuzzy PID controller is composed of three fuzzy sub controllers which are connected in parallel. These parallel sub controllers are assembled to get the proposed parallel fuzzy type PID controller. The hybrid fuzzy PID gains are expressed in the error domain. Hence, the structural design presents an alternative to control schemes employed so far. This hybrid intelligent controller is formulated and executed in real world hardware for position as well as speed control of a Hybrid Pole Self Bearing Switched Reluctance Motor (HPSBSRM) drive system. The design of the parallel fuzzy PID controller, implementation and finally analysis all are carried out using MATLAB/Simulink environment. Software results concluded that the novel hybrid intelligent parallel fuzzy PID controller generates better control action when compared to traditional PID controller, predominantly in system nonlinearities and in external load variations.
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Borunda, Monica, Raul Garduno, Javier de la Cruz Soto, and Rafael Alfonso Figueroa Díaz. "Intelligent Control of an Experimental Small-Scale Wind Turbine." Energies 17, no. 22 (2024): 5656. http://dx.doi.org/10.3390/en17225656.
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Nowadays, wind turbines are one of the most popular devices for producing clean and renewable electric energy. The rotor blades catch the wind’s kinetic energy to produce rotational energy from the turbine and electric energy from the generator. In small-scale wind turbines, there are several methods to operate the blades to obtain the desired speed of rotation and power outputs. These methods include passive stall, active stall, and pitch control. Pitch control sets the angular position of the blades to face the wind to achieve a predefined relationship between turbine speed or power and wind velocity. Typically, conventional Proportional Integral (PI) controllers are used to set the angular position of the rotor blades or pitch angle. Nevertheless, the quality of speed or power regulation may vary substantially. This study introduces a rotor speed controller for a pitch-controlled small-scale wind turbine prototype based on fuzzy logic concepts. The basics of fuzzy systems required to implement this kind of controller are presented in detail to counteract the lack of such material in the technical literature. The knowledge base of the fuzzy speed controller is composed of Takagi–Sugeno–Kang (TSK) fuzzy inference rules that implement a dedicated PI controller for any desired interval of wind velocities. Each wind velocity interval is defined with a fuzzy set. Simulation experiments show that the TSK fuzzy PI speed controller can outperform the conventional PI controller in the speed and accuracy of response, stability, and robustness over the whole range of operation of the wind turbine prototype.
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Zada, Fatma, Shawket K. Guirguis, and Walied M. Sead. "Hybrid Neural-Fuzzy Controller for Motorized Robot Arm." Advanced Materials Research 403-408 (November 2011): 5068–75. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.5068.
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In this study, a design methodology is introduced that blends the neural and fuzzy logic controllers in an intelligent way developing a new intelligent hybrid controller. In this design methodology, the fuzzy logic controller works in parallel with the neural controller and adjusting the output of the neural controller. The performance of our proposed controller is demonstrated on a motorized robot arm with disturbances. The simulation results shows that the new hybrid neural -fuzzy controller provides better system response in terms of transient and steady-state performance when compared to neural or fuzzy logic controller applications. The development and implementation of the proposed controller is done using the MATLAB/Simulink toolbox to illustrate the efficiency of the proposed method.
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Sun, Hong Xing, Chuang Gao, and Na Li. "Research on Dissolved Oxygen Intelligent Control System in Waste Water Treatment." Advanced Materials Research 591-593 (November 2012): 1461–64. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1461.
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By combining with the research hot spot of intelligent control, the oxygen fuzzy-single neuron PID compound controller is designed based on control increment. It contains the advantages of fuzzy controller and the single neuron PID controller. The problem of causing the oscillation in switching two kinds of controllers is solved. The results of the dissolved oxygen fuzzy-single neurons PID controller and fuzzy controller simulation are compared. The results show that the control effect of dissolved oxygen is more ideal. Thus the algorithm is effective and feasible.
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Salma, S. K., and K. Radha Rani. "Transient Stability Enhancement Using Matrix Converter Based UPFC with Intelligent Controlling Techniques." Journal of Advance Research in Electrical & Electronics Engineering (ISSN: 2208-2395) 2, no. 8 (2015): 01–12. http://dx.doi.org/10.53555/nneee.v2i8.178.
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This paper presents a matrix converter based UPFC for enhancing the transient stability. Conventional methods use a two step based UPFC (DC-AC-DC). In the proposed model the conventional UPFC is replaced by a Matrix Converter which is controlled with two different controllers which include fuzzy logic controller and the other with an added integrator which is referred to be a HYBRID fuzzy logic controller. In this paper, the working of matrix converter based UPFC with the two controller techniques under certain disturbances is observed using the MATLAB software and the results are verified.
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Ramezanifard, Mehdi. "Design of Intelligent Controllers for Double Inverted Pendulum." Ciência e Natura 37 (December 19, 2015): 406. http://dx.doi.org/10.5902/2179460x20802.
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In this paper, first a fuzzy controller is proposed for systems comprising two inverted pendulums placed in the same plane, where one pendulum moves along the X-axis and the other moves along the Y-axis; rules of a Fuzzy-Mamdani controller is improved by considering the behavior of an operator and a the developed expert system and by trial and error in the observation of the system’s behavior. Simulation results obtained in MATLAB are proposed and then two other intelligent methods are presented. In the second proposed method, a TSK controller is designed with Anfis training approach and its simulation results are presented. Finally, a non-fuzzy controller (linearization feedback) is used as the supervisor fuzzy controller and the simulation results are presented. At last, performance of the three controllers is compared and the results are demonstrated.
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Mekrini, Zineb, and Seddik Bri. "Fuzzy Logic Application for Intelligent Control of An Asynchronous Machine." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 1 (2017): 61. http://dx.doi.org/10.11591/ijeecs.v7.i1.pp61-70.
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<p>The aim of this article is propose a method to improve the direct torque control and design a Fuzzy Logic based Controller which can take necessary control action to provide the desired torque and flux of an asynchronous machine. It’s widely used in the industrial application areas due to several features such as fast torque response and less dependence on the rotor parameters. The major problem that is usually associated with DTC control is the high torque ripple as it is not directly controlled. The high torque ripple causes vibrations to the motor which may lead to component lose, bearing failure or resonance. The fuzzy logic controller is applied to reduce electromagnetic torque ripple. In this proposed technique, the two hysteresis controllers are replaced by fuzzy logic controllers and a methodology for implementation of a rule based fuzzy logic controller are presented. The simulation by Matlab/Simulink was built which includes induction motor d-q model, inverter model, fuzzy logic switching table and the stator flux and torque estimator. The validity of the proposed method is confirmed by the simulative results of the whole drive system and results are compared with conventional DTC method. </p>
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Hadi, M. Sukri, Intan Z. Mat Darus, M. Osman Tokhi, and Mohd Fairus Jamid. "Active vibration control of a horizontal flexible plate structure using intelligent proportional–integral–derivative controller tuned by fuzzy logic and artificial bee colony algorithm." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 4 (2019): 1159–71. http://dx.doi.org/10.1177/1461348419852454.
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This paper presents the development of an intelligent controller for vibration suppression of a horizontal flexible plate structure using hybrid Fuzzy–proportional–integral–derivative controller tuned by Ziegler–Nichols tuning rules and intelligent proportional–integral–derivative controller tuned by artificial bee colony algorithm. Active vibration control technique was implemented during the development of the controllers. The vibration data obtained through experimental rig was used to model the system using system identification technique based on auto-regressive with exogenous input model. Next, the developed model was used in the development of an active vibration control for vibration suppression of the horizontal flexible plate system using proportional–integral–derivative controller. Two types of controllers were proposed in this paper which are the hybrid Fuzzy–proportional–integral–derivative controller and intelligent proportional–integral–derivative controller tuned by artificial bee colony algorithm. The performances of the developed controllers were assessed and validated. Proportional–integral–derivative–artificial bee colony controller achieved the highest attenuation for first mode of vibration with 47.54 dB attenuation as compared to Fuzzy–proportional–integral–derivative controller with 32.04 dB attenuation. The experimental work was then conducted for the best controller to confirm the result achieved in the simulation work.
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Joo, Young-Hoon, Yeun-Woo Lee, Dai-Bum Cha, and Jae-Heung Oh. "Intelligent Digitally Redesigned Fuzzy Controller." International Journal of Fuzzy Logic and Intelligent Systems 2, no. 3 (2002): 220–26. http://dx.doi.org/10.5391/ijfis.2002.2.3.220.
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Li, Jie Hui, Chang Jun Chen, Lin Shan Guo, and Bing Cheng Yan. "Intelligent Fuzzy Controller Design and Simulation for Vehicle Speed Control." Applied Mechanics and Materials 43 (December 2010): 62–67. http://dx.doi.org/10.4028/www.scientific.net/amm.43.62.
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Through the study on fuzzy control in the vehicle speed control, the intelligent fuzzy controller with intelligent integral and differential is proposed. In this paper, we take diesel vehicle control system as an example, and the vehicle speed control system’s simulation has been completed. Simulation results show that the intelligent fuzzy controller could adapt to the model whose parameters are non-linear, time-varying, and this controller can also effectively inhibit the environmental interference, so the system with intelligent fuzzy controller has strong robustness, good reliability and higher control precision.
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Narmadha, T. Velayudham, Chackaravarthy Baskaran, and K. Sivakumar. "Comparison of Performance Measures of Speed Control for a DC Motor Using Hybrid Intelligent Controller and Optimal LQR." Applied Mechanics and Materials 622 (August 2014): 23–31. http://dx.doi.org/10.4028/www.scientific.net/amm.622.23.
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-In this paper , performance of fuzzy PD , fuzzy PI , fuzzy PD+I , fuzzy PID controllers are evaluated and compared. This paper also describes the speed control based on Linear Quadratic Regulator (LQR) technique. The comparison is based on their ability of controlling the speed of DC motor, which merely focuses on performance index of the controllers, and also time domain specifications such as rise time, settling time and peak overshoot. The controller is modelled using MATLAB software, the simulation results shows that the fuzzy PID controllers are the best performing candidates in all aspects but it as higher overshoot and IAE in comparison with optimal LQR. The Fuzzy PI controller exhibited null offset but suffers from poor stability and peak overshoot, whereas the fuzzy PD controller has fast rise time, with no overshoots but the IAE is much greater. Thus, the comparative analysis recommends fuzzy PID controller but it is usually associated with complicated rule base and tedious tuning. To circumvent these problems, the proposed LQR controller gives better performance than the other controllers.
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Braz-César, Manuel, and Rui Barros. "Optimization of a Fuzzy Logic Controller for MR Dampers Using an Adaptive Neuro-Fuzzy Procedure." International Journal of Structural Stability and Dynamics 17, no. 05 (2016): 1740007. http://dx.doi.org/10.1142/s0219455417400077.
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Intelligent and adaptive control systems are naturally suitable to deal with dynamic uncertain systems with non-smooth nonlinearities; they constitute an important advantage over conventional control approaches. This control technology can be used to design powerful and robust controllers for complex vibration engineering problems such as vibration control of civil structures. Fuzzy logic based controllers are simple and robust systems that are rapidly becoming a viable alternative for classical controllers. Furthermore, new control devices such as magnetorheological (MR) dampers have been widely studied for structural control applications. In this paper, we design a semi-active fuzzy controller for MR dampers using an adaptive neuro-fuzzy inference system (ANFIS). The objective is to verify the effectiveness of a neuro-fuzzy controller in reducing the response of a building structure equipped with a MR damper operating in passive and semi-active control modes. The uncontrolled and controlled responses are compared to assess the performance of the fuzzy logic based controller.
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Abed, Issa Ahmed, and Samar Hameed Majeed. "DC/DC converter control using suggested artificial intelligent controllers." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (2021): 847. http://dx.doi.org/10.11591/ijai.v10.i4.pp847-857.
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<span>In order to provide constant DC voltage, buck converter is used which is a common converter in different applications. The use of artificial control methods for DC/DC converter will increase the productivity, spend low energy, and able to avoid the changes. Here, in order to control the proposed converter, intelligent regulation is utilized. Different methods have been suggested to satisfy the need of the output. The work uses proportional-integral-derivative (PID) controller which is received the error difference between the output and the desired. Fuzzy logic controller (FLC) is also used. While the fuzzy-PID supervised (FSC-PID) where the parameters of the PID is updated using the fuzzy system. The explanation of the proposed controllers is presented in this paper. PID, fuzzy logic controller, and fuzzy-PID supervised have been designed and implemented using MATLAB in order to settle the output of buck DC/DC converter. The complete system has been built in MATLAB/Simulink. The proposed controller keeps track the output to be exactly the set signal.</span>
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Issa, Ahmed Abed, and Hameed Majeed Samar. "DC/DC converter control using suggested artificial intelligent controllers." International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (2021): 847–57. https://doi.org/10.11591/ijai.v10.i4.pp847-857.
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In order to provide constant DC voltage, buck converter is used which is a common converter in different applications. The use of artificial control methods for DC/DC converter will increase the productivity, spend low energy, and able to avoid the changes. Here, in order to control the proposed converter, intelligent regulation is utilized. Different methods have been suggested to satisfy the need of the output. The work uses proportional-integral-derivative (PID) controller which is received the error difference between the output and the desired. Fuzzy logic controller (FLC) is also used. While the fuzzy-PID supervised (FSC-PID) where the parameters of the PID is updated using the fuzzy system. The explanation of the proposed controllers is presented in this paper. PID, fuzzy logic controller, and fuzzy-PID supervised have been designed and implemented using MATLAB in order to settle the output of buck DC/DC converter. The complete system has been built in MATLAB/Simulink. The proposed controller keeps track the output to be exactly the set signal.
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Botirov, Tulkin, Baxtiyor Sodiqov, Elbek Jumabaev, Aziz Salahiddinov, and Ruxsora Jurayeva. "Design of a controller with an intelligent hybrid structure." E3S Web of Conferences 627 (2025): 04007. https://doi.org/10.1051/e3sconf/202562704007.
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The rapid advancements in control engineering have led to the development of intelligent hybrid controllers that integrate multiple control strategies for improved system performance. Among these, the Fractional- Order Fuzzy PID (FOFPID) controller has gained significant attention due to its enhanced flexibility and robustness in handling complex, nonlinear, and uncertain systems. This paper presents a comprehensive study on the design and implementation of the FOFPID controller, which combines the advantages of fractional-order PID (FOPID) and fuzzy logic control (FLC). The fractional-order components provide better memory effects and adaptability, while the fuzzy logic system enhances the controller’s decision-making capability in dynamic environments. Various optimization techniques, such as metaheuristic algorithms, are employed to fine-tune the controller parameters for superior performance. Simulation and experimental results demonstrate that the FOFPID controller outperforms conventional PID and FOPID controllers in terms of stability, accuracy, and disturbance rejection. The proposed intelligent hybrid control approach is applicable to a wide range of industrial and automatic systems, providing improved efficiency and reliability.
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Zeng, Zhi Hui, and Liang Li. "Research on Anti-Swing Control of Bridge Crane Based on Fuzzy Control." Applied Mechanics and Materials 143-144 (December 2011): 293–96. http://dx.doi.org/10.4028/www.scientific.net/amm.143-144.293.
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In recent years, with the development of intelligent control theory, fuzzy control theory has been applied to automatic control of the crane. This paper designs fuzzy anti-swing controller based on the nonlinear mathematical model of bridge crane, and MATLAB/Simulink simulation was carried out. The Simulink experiment proves that location fuzzy controller and angle fuzzy controller show the good anti-swing effect compared with conventional PID controller. The two controllers not only improve response speed times but also improve control accuracy.
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Dileep, Kumar Appana *. Muhammed Sohaib. "FUZZY LOGIC CONTROL BASED PID CONTROLLER FOR STEP DOWN DC-DC POWER CONVERTER." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 10 (2016): 751–61. https://doi.org/10.5281/zenodo.163300.
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Power converters are vital part of many devices. Intelligent control for such a vital part is a high priority task. The paper describes about the implementation of standalone fuzzy logic controller and self-tuning fuzzy logic based PID controller in order to control a step down power converter. The software has been developed to simulate the real time situation based on the fuzzy logic technology. Simulation results of the fuzzy logic controller are shown for two intelligent control methods, a fuzzy logic control and self-tuning fuzzy PID reveal better performances than the PID conventional controller.
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Li, Gang, Pengfei Shang, Changbing Zheng, and Dehui Sun. "A Lateral Control Method of Intelligent Vehicles Based on Shared Control." Symmetry 14, no. 11 (2022): 2447. http://dx.doi.org/10.3390/sym14112447.
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This paper studies the lateral control problem for intelligent vehicles based on the concept of shared control. Considering the participation of drivers in the control loop, a shared control-based lateral controller is designed, which consists of two differed controllers: one is an LQR-based autonomous driving controller and the other is a driver’s intention-based fuzzy controller. For the vehicle dynamic model with two-degrees of freedom, an autonomous driving controller based on LQR and a driver’s intention-based fuzzy controller are designed. Then, the lateral controller based on shared control is constructed, which integrates the aforementioned two controllers. Finally, the co-simulation by MATLAB/Simulink and Carsim is presented. Furthermore, simulation results show that the designed lateral controller can track the desired path with better performance than the LQR-based autonomous driving controller.
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Elgargani, T. N., A. A. Hudoud, and S. O. Abid. "IMPROVEMENT AND CONTROL OF THE SPEED RESPONSE OF THE PERMANENT MAGNET SYNCHRONOUS MOTOR DRIVE USING A FUZZY – PI CONTROLLER." Journal of Science and Technology 30, no. 5 (2025): 25–36. https://doi.org/10.20428/jst.v30i5.2812.
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High-speed and high-performance electric motors are designed to reach a high level of demand control. The permanent magnet synchronous motors (PMSMs) drive has a non-linear model that is not easy to deal with using traditional control methods when controlling the three phase motors because of their nature, (intricate highly non-linear model). Therefore, neural networks controllers compared with fuzzy logic controllers (FLCs) are getting more attention among researchers, as they can be used for such systems. The neural networks controller relies on training of this mathematical model, and the fuzzy controller also relies on experience. The performance of these two controllers were compared to each other in terms of output response. As all the real systems exhibit non-linear behavior, conventional PI (Proportional-Integral) controllers are unable to provide good and acceptable results. For this reason, when designing intelligent control systems, the corresponding model for simulation should reflect all characteristics of the real system to be controlled. The basic idea of this paper is to apply the fuzzy-PI controller on PMSMs drive and compare the obtained results with the traditional PI. Also, one intelligent controller, which is the NN (Neural Network) controller, is applied and its performance is simulated and studied. MATLAB/SIMULINK environment is used for design, implementation and testing. Therefore, the speed and torque of the PMSMs drive can be controlled satisfactorily. Finally, simulation results have shown decent results in the improvement of the system behavior.
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Leite, M. S., T. L. Fujiki, F. V. Silva, and A. M. F. Fileti. "Online Intelligent Controllers for an Enzyme Recovery Plant: Design Methodology and Performance." Enzyme Research 2010 (December 27, 2010): 1–13. http://dx.doi.org/10.4061/2010/250843.
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This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuzzy PI Mamdani controller, various universes of discourse, rule bases, and membership function support sets were tested. A neurofuzzy inference system (ANFIS), based on Takagi-Sugeno rules, and a model predictive controller, based on neural modeling, were developed and tested as well. Using a Fieldbus network architecture, a coolant variable speed pump was driven by the controllers. The experimental results show the effectiveness of fuzzy controllers in comparison to the neural predictive control. The fuzzy PI controller exhibited a reduced error parameter (ITAE), lower power consumption, and better recovery of enzyme activity.
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Tellez-Belkotosky, Pablo A., Luis E. Cabriales-Ramirez, Manuel A. Gutierrez-Martinez, and Edmundo Javier Ollervides-Vazquez. "Intelligent PIV Fuzzy Navigation and Attitude Controller for an Octorotor Mini-UAV." Machines 11, no. 2 (2023): 266. http://dx.doi.org/10.3390/machines11020266.
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In this research, a proportional plus integral plus velocity (PIV) fuzzy gain scheduling flight controller for an octorotor mini-unmanned aerial vehicle is developed. The designed flight controller scheme, with a PIV term, is combined with a fuzzy gain scheduling approach. The tracking controller PIV fuzzy gain scheduling is based on two controllers connected in cascade with a saturation approach. The Newton–Euler equations of motion are applied to obtain a mathematical model for the octorotor mini-unmanned aerial vehicle (mini-UAV). The flight controller approach is applied to obtain coupling moments and forces with interconnected attitude and navigation tracking trajectory. In the design of a flight navigation controller with two layers, the inner layer consists of a PIV fuzzy gain scheduling controller that is applied to the attitude dynamics, obtaining the references for the coupling outer layer PIV fuzzy gain scheduling controller, which manipulates the translational dynamics. The navigation PIV fuzzy gain scheduling controller is saturated for bounding in translational forces to avoid large deviations of commands to Euler angles pitch and roll, and another saturated controller is implemented for the bounded thrust rotor to avoid the excessive angular speed of these rotors. The octorotor mini-UAV flight navigation simulation is performed to validate the tracking control of a sequence of motions in each axis, which is presented as a validation for the proposed control scheme.
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Guo, Li-Xin, and Dinh-Nam Dao. "A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system." Journal of Vibration and Control 26, no. 13-14 (2019): 1187–98. http://dx.doi.org/10.1177/1077546319890188.
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This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.
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Blahová, Lenka, Ján Dvoran, and Jana Kmeťová. "Neuro-fuzzy control design of processes in chemical technologies." Archives of Control Sciences 22, no. 2 (2012): 233–50. http://dx.doi.org/10.2478/v10170-011-0022-2.
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Neuro-fuzzy control design of processes in chemical technologies The paper presents design of neuro-fuzzy control and its application in chemical technologies. Our approach to neuro-fuzzy control is a combination of the neural predictive controller and the neuro-fuzzy controller (Adaptive Network-based Fuzzy Inference System - ANFIS). These controllers work in parallel. The output of ANFIS adjusts the output of the neural predictive controller to enhance the control performance. Such design of an intelligent control system is applied to control of the continuous stirred tank reactor and laboratory mixing process.
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Zhang, Jing Jun, Xiao Pin Guo, Li Li He, and Rui Zhen Gao. "Study of Fuzzy Control for Intelligent Cantilever Beam Based on Genetic Algorithm." Advanced Materials Research 204-210 (February 2011): 25–30. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.25.
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The design of fuzzy controller is the key of fuzzy control system, while the core of fuzzy controller design lies in fuzzy rules, whose performance determines the control effect of fuzzy system. General fuzzy rules are obtained from expert experience, in which much subjectivity exists. In this paper, a fuzzy controller is designed by taking an intelligent cantilever beam as the research object. And a method using the genetic algorithm to optimize fuzzy rules is proposed and the genetic coding as well as the fitness function is confirmed. Finally, the simulation model of intelligent cantilever beam is built by Matlab/Simulink, and the vibration control effects of fuzzy controller optimized by genetic algorithm are compared with those un-optimized. The simulation results indicate that the vibration amplitude of intelligent cantilever beam has a significant decrease and the vibration decay rate has a significant increase after the fuzzy rules optimized.
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Li, Zheng Qiang, and Jing Zhou Zhao. "Method and Application of Intelligent Reconfigurable Control." Applied Mechanics and Materials 236-237 (November 2012): 289–96. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.289.
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In this paper we propose an intelligent adaptive retrofit reconfigurable controller in which the full system states are reliably measurable and available for feedback and diagnosis. The proposed approach retains the existing nominal controller and adds a suitably chosen signal that assures that the desired closed-loop performance is achieved despite the failure. The approach is based on the properties of the system controlled by the nominal controller, and judiciously chosen adaptation signals. Furthermore, some representative methods of intelligent reconfigurable control are introduced, especially intelligent fault diagnosis and intelligent reconfigurable control strategies based on adaptive neural networks and fuzzy reasoning has been discussed in details, which has ensure that the aircraft has retain its flying qualities to a satisfactory level even to the presence of severe control failures. The ability to control aircraft is shown through simulation results with the intelligent reconfigurable controller using the adaptive fuzzy-neural reasoning of the aircraft.
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Joo, Young-Hoon, Sang-Jun Lee, and Jae-Heung Oh. "Intelligent Fuzzy Controller for Nonlinear Systems." International Journal of Fuzzy Logic and Intelligent Systems 2, no. 2 (2002): 139–45. http://dx.doi.org/10.5391/ijfis.2002.2.2.139.
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Doan, Diem-Vuong, and Ngoc-Khoat Nguyen. "Comparative Evaluation of Different Hybrid Intelligent Load-Frequency Controllers for Interconnected Electric Power Grids." Engineering, Technology & Applied Science Research 14, no. 2 (2024): 13173–80. http://dx.doi.org/10.48084/etasr.6706.
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Network frequency is considered to be one of the most crucial parameters that strongly affect the stability and economic achievements of interconnected electric power grids. System frequency usually fluctuates and deviates from the nominal values due to random and continuous load changes over time, affecting the electric equipment to significantly decrease efficiency and increase instability. A Load-Frequency Control (LFC) strategy has been proposed to solve this problem. This study compared several different control strategies, namely Fuzzy Particle Swarm Optimization (Fuzzy-PSO), Proportional Integral Derivative (PID), Fuzzy-PID, Fuzzy- Proportional Integral (PI), PSO-PI, and FPID to investigate the effectiveness of intelligent hybrid LFC controllers. The above controllers were simulated on a three-area interconnected power network with the participation of renewable energy sources. Taking into account different load cases, the Fuzzy-PSO-PID controller obtained frequency deviations in the range of 0.0015 to 0.002 Hz. The settling time was about 10 s to reach zero frequency error in each area. With the above controller quality parameters, the Fuzzy-PSO-PID controller provided better quality than the other controllers. A comparative numerical simulation in MATLAB/Simulink for various load change scenarios revealed the effectiveness of hybrid smart controllers, such as the Fuzzy-PID-PSO, outperforming the traditional ones.
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Mousa, Norhan M., Yasser I. El-Shaer, and Mohamed I. Abu El-Sebah. "A Proposed Controller for Pitch Angle of Wind Turbine." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 18 (December 31, 2023): 527–39. http://dx.doi.org/10.37394/23203.2023.18.55.
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Wind turbines are complicated non-linear systems with certain random disruptions. The pitch control system is a commonly employed method for regulating the electricity generated by a wind turbine. Many researchers have observed developments in the pitch control field during the last few decades. Traditional PID controllers have the drawback of being slow or imprecise when wind and pitch angles suddenly change. These drawbacks can be solved with artificial intelligent algorithms. However, the algorithms' design and implementation are highly complex. A new pitch-regulated variable-speed control strategy for wind turbines to address their nonlinear properties is presented. To manage the pitch system's control mechanisms with disturbances, this research evolved a mathematical model that illustrates HAWT's pitch angle control system and applied a proposed Simple Optimal Intelligent PID Controller (SOI-PID). Under various operating conditions, the proposed SOI-PID controller was tested with the Traditional PID, Fuzzy Logic Controller (FLC), and Fuzzy-Adaptive-PID controller. For system simulation, the MATLAB/Simulink software was used. According to simulation results, compared to PID, FLC, and Fuzzy-Adaptive-PID controllers, the proposed SOI-PID controller responds faster and has a better rise and settling time. Other benefits of the SOI-PID controller are its simplicity of implementation and design, distinguishing it from other intelligent algorithms.
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Muthukumari, T., T. A. Raghavendiran, R. Kalaivani, and P. Selvaraj. "Intelligent tuned PID controller for wind energy conversion system with permanent magnet synchronous generator and AC-DC-AC converters." IAES International Journal of Robotics and Automation (IJRA) 8, no. 2 (2019): 133–45. https://doi.org/10.11591/ijra.v8i2.pp133-145.
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This paper presents the intelligent tuned PID controller-based Single Ended Primary Inductor Converter (SEPIC) for Maximum Power Point Tracking (MPPT) operation of Wind Energy Conversion System (WECS). As the voltage and frequency of the Permanent Magnet Synchronous Generator (PMSG) varies with the wind speed changes, Intelligent controlled SEPIC is utilized to maintain the constant DC link voltage. The intelligent tuned PID controller combines the advantages of both conventional and soft controllers. The 1.5MW variable speed WECS (VSWECS) with AC-DC-AC converter is developed using MATLAB/Simulink software. PMSG delivers a load/utility grid through an uncontrolled diode rectifier, intelligent controlled SEPIC and three phase inverter. The real time implementation of the proposed system is done by the DSP processor MSP430F5529. The performance of the SEPIC is tested in both simulation and experiment at different wind speed conditions. The performance of the proposed Intelligent MPPT control of SEPIC are compared with the conventional PID controller. Intelligent tuning of PID controller such as Fuzzy-PID, and ANFIS-PID is implemented in the proposed system and results are compared. The simulation and experimental results reveals that the proposed ANFIS method provide improved performance than the conventional PID method in terms of power quality.
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Reshetnikov, Andrey, Olga Tyatyushkina, and Sergey Ulyanov. "Intelligent robust control of unstable dynamic object with remote knowledge base tuning." Robotics and Technical Cybernetics 12, no. 4 (2024): 280–87. https://doi.org/10.31776/rtcj.12405.
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This work is a continuation of series of papers published in previous issues of the electronic journal. The control object is a classical problem of the theory of control «Cart–Pole». In the experiment used a more sophisticated robot with second feedback. In this paper the technology of fuzzy controller design based on the measured physi-cal signal for creating teaching signal is described. In this paper the comparison of the fuzzy controllers is de-scribed using the software tool as «Soft Computing Optimizer» with the classical PID controller. Setting up a knowledge base of fuzzy controller is carried out using a remote connection to the control object in real time.
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Chen, Ching-Han, Chien-Chun Wang, Yi Tun Wang, and Po Tung Wang. "Fuzzy Logic Controller Design for Intelligent Robots." Mathematical Problems in Engineering 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/8984713.
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This paper presents a fuzzy logic controller by which a robot can imitate biological behaviors such as avoiding obstacles or following walls. The proposed structure is implemented by integrating multiple ultrasonic sensors into a robot to collect data from a real-world environment. The decisions that govern the robot’s behavior and autopilot navigation are driven by a field programmable gate array- (FPGA-) based fuzzy logic controller. The validity of the proposed controller was demonstrated by simulating three real-world scenarios to test the bionic behavior of a custom-built robot. The results revealed satisfactorily intelligent performance of the proposed fuzzy logic controller. The controller enabled the robot to demonstrate intelligent behaviors in complex environments. Furthermore, the robot’s bionic functions satisfied its design objectives.
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Zhou, Jun. "Intelligent Information Control for Air System." Applied Mechanics and Materials 310 (February 2013): 502–5. http://dx.doi.org/10.4028/www.scientific.net/amm.310.502.
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PID control has been widely applied in the industrial process control because of its robust and easy realization, but it is difficult to tune the parameters of PID controller, which often leads to oscillation and overshoot. Due to no repetition and random of adaptive fuzzy PID control, the authors propose a method to search for normalization PID controller parameters based on adaptive fuzzy PID control, which can be expected to have higher ability of searching for global optimal PID parameters according to the performance index of control system . The MATLAB simulation of the Adaptive fuzzy PID controller and a PID controller were carried out on the Air Conditioning System temperature. Results showed that: Response time of Adaptive fuzzy PID was 0.46 s., and maximum overshoot didn’t exceed 3.29%.The stability accuracy and rapidity of the system were able to satisfy the Air Conditioning System technical requirements.
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Kareem Abdullah, Ahmed, Karrar Hameed Kadhim Abdul Amir, and Baqer Turki attyia. "Enhancement of AVR response based on Intelligent Fuzzy-Swarm-PID Controller." University of Thi-Qar Journal for Engineering Sciences 8, no. 3 (2017): 30–39. http://dx.doi.org/10.31663/utjes.v8i3.95.
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Automatic voltage regulator is used to fix the output signal by correcting the voltage of the exciter and track the generator’s voltage. The improvement of automatic voltage regulator (AVR) response will reflected on the power system performance. In this paper two intelligent techniques fuzzy logic (FL) and Particle swarm optimization (PSO) are used to enhance the response of the Fourth order AVR system based on the merging between them and then the control action summed and coupled with PID controller action. Fuzzy controller has many benefits in design of AVR compared with conventional controllers, but the response of the AVR system still insufficient, therefore, the PSO and Fuzzy are used together for this purpose to produce Fuzzy-Swarm-PID controller (FSPID).Usually the cognitive (C1) and social acceleration (C2) are taken as a constant values in PSO algorithm, but here the Fuzzy logic system is used to get these accelerations to enhance the response.
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Kareem Abdullah, Ahmed, Karrar Hameed Kadhim Abdul Amir, and Baqer Turki attyia. "Enhancement of AVR Response Based on Intelligent Fuzzy-Swarm-PID Controller." University of Thi-Qar Journal for Engineering Sciences 9, no. 1 (2018): 59–63. http://dx.doi.org/10.31663/utjes.v9i1.43.
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Automatic voltage regulator is used to fix the output signal by correcting the voltage of the exciter and track the generator’s voltage. The improvement of automatic voltage regulator (AVR) response will reflected on the power system performance. In this paper two intelligent techniques fuzzy logic (FL) and Particle swarm optimization (PSO) are used to enhance the response of the Fourth order AVR system based on the merging between them and then the control action summed and coupled with PID controller action. Fuzzy controller has many benefits in design of AVR compared with conventional controllers, but the response of the AVR system still insufficient, therefore, the PSO and Fuzzy are used together for this purpose to produce Fuzzy-Swarm-PID controller (FSPID). Usually the cognitive (C1) and social acceleration (C2) are taken as a constant values in PSO algorithm, but here the Fuzzy logic system is used to get these accelerations to enhance the response.
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Ryu, Seung-Min, Kang-Hyeon Choi, and Hyuk-Jun Chang. "Interval type-2 intelligent fuzzy vehicle speed controller design using headlamp reflection detection and an adaptive neuro–fuzzy inference system." PLOS One 20, no. 6 (2025): e0323913. https://doi.org/10.1371/journal.pone.0323913.
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In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. In situations with limited distance data, we also design a fuzzy controller using the adaptive neuro–fuzzy inference system (ANFIS). To enhance robustness against disturbances, the interval type-2 approach is used. For the distance estimation algorithm, the vehicle is positioned at predefined intervals from the target object, capturing images of the headlights at each point. The region of interest containing the light is extracted from each image and segmented by light intensity. Weighted values are then assigned to each segment based on intensity, producing an image value that correlates with the distance. This image-derived value is then used as distance data for the design of the fuzzy controller. The controller is implemented using the interval type-2 fuzzy logic toolbox in MATLAB/SIMULINK, with vehicle speed and image intensity values as inputs and control torque as the output to adjust vehicle speed. The noise from the vehicle speed sensor is treated as a disturbance, and the performance of the interval type-2 fuzzy controller is evaluated under these disturbance conditions. Additionally, fuzzy controllers are designed for vehicle positions between 41–43 m and 47–49 m, and these controllers are trained using ANFIS to function effectively across the entire 41–49 m range. Simulation results demonstrate that, with the controller integrated into the vehicle system, the vehicle is successfully controlled to reach the target position.
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Areola, R. I., A. O. Adetunmbi, and Thomas O. Okimi. "Intelligent Energy Management System: Harnessing Fuzzy Logic for Charge Control." Journal of Engineering Research and Reports 26, no. 4 (2024): 120–32. http://dx.doi.org/10.9734/jerr/2024/v26i41119.
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Aim: Charge controller that leverages fuzzy logic was developed to enhance the efficiency of traditional charge controllers. A model was constructed and assessed for its performance through MATLAB/Simulink. It allows for flexibility in controlling the variability of renewable energy sources. It also improves the efficiency and lifespan of energy storage systems while minimizing the impact on the grid and environment. 
 Study of the design: The PV system consists of a PV module, PWM inverter, MPPT controller and DC-DC converter which are connected using MATLAB/Simulink environment.
 Methodology: we conducted validation tests to substantiate the advantages of our fuzzy charge controller. The creation of fuzzy rules was based on the system's performance and subsequently translated into precise values with the assistance of a fuzzy inference system. This Research Project was Completed in Two Months.
 Results: Our findings clearly demonstrate that the implementation of fuzzy logic control results in superior charge controller performance. This, in turn, safeguards against battery discharging and overcharging during unpredictable weather conditions.
 Conclusion: These protective measures are made possible through the decision-making capabilities of the DC-DC buck-boost converter, which effectively regulates both the voltage and current output of the PV module.
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Ling, Jing, Jin Che, and Da Ming Liu. "Composite Fuzzy Control Method for Temperature in Intelligent Moisture Analyzer." Applied Mechanics and Materials 401-403 (September 2013): 1010–13. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1010.
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Temperature control system of infrared heating oven in moisture analyzer is characteristic of nonlinear, time-varying and time-lag. A composite fuzzy control (CFC) method is proposed, which combines improved Bang-Bang control with two-stage intelligent fuzzy control. The control algorithm is implemented by MSP430F5438. When the temperature error e between the desired temperature and actual temperature in heating oven is larger than threshold value, the improved Bang-Bang controller is employed in rapidly reducing the error; to decrease the system overshoot, the basic fuzzy controller is used; to reduce the steady-state error of basic fuzzy controller, the auxiliary fuzzy controller is applied. The steady-state error of improved fuzzy controller for oven temperature is less than 0.5°C, which is better than the Chinese National Standards for moisture content measurement.
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A-Amir, Ahlam Najm, and Hanan A. R. Akkar. "Artificial Intelligent Fuzzy Logic Controller Applied on 6DOF Robot Arm Using LabVIEW and FPGA." European Journal of Engineering Research and Science 3, no. 5 (2018): 1. http://dx.doi.org/10.24018/ejers.2018.3.5.661.
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In this work an efficient Artificial Intelligent Robotic Fuzzy Logic Controller (AIRFC) system have been constructed to control the robot arm. A serial link Robot manipulator with 6 Degree of Freedom (DOF) from DFROBOT of code ROB0036 is used as a case study. A fuzzy logic type1 controller is implemented on LabVIEW to control each joint of the robot arm for nonlinearity measurements and a fuzzy logic type2 controller is applied which is more suitable for uncertainty. The hardware design is implemented and finally downloaded using the Field Programmable Gate Array (FPGA) kit named PCI-7833R from National Instrument. By using the LabVIEW FPGA MODEL the target board can be detected for software implementation of the controllers’ systems. The work shows that in case of type2 fuzzy logic the rise time is less than that of type1 fuzzy logic for the shoulder, wrist roll and the gripper angles and it is higher for base, elbow and wrist pitch angles. The settling time is the same in elbow and wrist pitch angles and for the type2 fuzzy controller it is less for other angles.
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A-Amir, Ahlam Najm, and Hanan A. R. Akkar. "Artificial Intelligent Fuzzy Logic Controller Applied on 6DOF Robot Arm Using LabVIEW and FPGA." European Journal of Engineering and Technology Research 3, no. 5 (2018): 1–8. http://dx.doi.org/10.24018/ejeng.2018.3.5.661.
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In this work an efficient Artificial Intelligent Robotic Fuzzy Logic Controller (AIRFC) system have been constructed to control the robot arm. A serial link Robot manipulator with 6 Degree of Freedom (DOF) from DFROBOT of code ROB0036 is used as a case study. A fuzzy logic type1 controller is implemented on LabVIEW to control each joint of the robot arm for nonlinearity measurements and a fuzzy logic type2 controller is applied which is more suitable for uncertainty. The hardware design is implemented and finally downloaded using the Field Programmable Gate Array (FPGA) kit named PCI-7833R from National Instrument. By using the LabVIEW FPGA MODEL the target board can be detected for software implementation of the controllers’ systems. The work shows that in case of type2 fuzzy logic the rise time is less than that of type1 fuzzy logic for the shoulder, wrist roll and the gripper angles and it is higher for base, elbow and wrist pitch angles. The settling time is the same in elbow and wrist pitch angles and for the type2 fuzzy controller it is less for other angles.
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Kato, Shigeru, and Kok Wai Wong. "Intelligent Automated Guided Vehicle Controller with Reverse Strategy." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 3 (2011): 304–12. http://dx.doi.org/10.20965/jaciii.2011.p0304.
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This paper describes the intelligent Automated Guided Vehicle (AGV) control system using Fuzzy Rule Interpolation (FRI) method. The AGV used in this paper is a virtual vehicle simulated using computer. The purpose of the control system is to control the simulated AGV by moving along the given path towards a goal. Some obstacles can be placed on or near the path to increase the difficulties of the control system. The intelligent AGV should follow the path by avoiding these obstacles. This system consists of two fuzzy controllers. One is the original FRI controller that mainly controls the forward movement of the AGV. Another one is the proposed reverse movement controller that deals with the critical situation. When the original FRI controller faces the critical situation, our proposed reverse controller will control the AGV to reverse and move forward towards the goal. Our proposed reverse controller utilizes the advantage of FRI method. In our system, we also develop a novel switching system to switch from original to the developed reverse controller.
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Huang, Hsu-Chih, Chin-Wang Tao, Chen-Chia Chuang, and Jing-Jun Xu. "FPGA-Based Mechatronic Design and Real-Time Fuzzy Control with Computational Intelligence Optimization for Omni-Mecanum-Wheeled Autonomous Vehicles." Electronics 8, no. 11 (2019): 1328. http://dx.doi.org/10.3390/electronics8111328.
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This study presents a field-programmable gate array (FPGA)-based mechatronic design and real-time fuzzy control method with computational intelligence optimization for omni-Mecanum-wheeled autonomous vehicles. With the advantages of cuckoo search (CS), an evolutionary CS-based fuzzy system is proposed, called CS-fuzzy. The CS’s computational intelligence was employed to optimize the structure of fuzzy systems. The proposed CS-fuzzy computing scheme was then applied to design an optimal real-time control method for omni-Mecanum-wheeled autonomous vehicles with four wheels. Both vehicle model and CS-fuzzy optimization are considered to achieve intelligent tracking control of Mecanum mobile vehicles. The control parameters of the Mecanum fuzzy controller are online-adjusted to provide real-time capability. This methodology outperforms the traditional offline-tuned controllers without computational intelligences in terms of real-time control, performance, intelligent control and evolutionary optimization. The mechatronic design of the experimental CS-fuzzy based autonomous mobile vehicle was developed using FPGA realization. Some experimental results and comparative analysis are discussed to examine the effectiveness, performance, and merit of the proposed methods against other existing approaches.
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Torabi, Amir, Amin Adine Ahari, Ali Karsaz, and Seyyed Hossin Kazemi. "Intelligent Pitch Controller Identification and Design." International Journal of Mathematics and Computers in Simulation 15 (November 27, 2021): 134–40. http://dx.doi.org/10.46300/9102.2021.15.25.
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This paper exhibits a comparative assessmentbased on time response specification performance between modern and classical controller for a pitch control system of an aircraft system. The dynamic modeling of pitch control system is considered on the design of an autopilot that controls the pitch angle It starts with a derivation of a suitable mathematical model to describe the dynamics of an aircraft. For getting close to actual conditionsthe white noise disturbance is applied to the system.In this paper it is assumed that the modelpitch control systemis not available. So using the identification system and Box-Jenkins model estimator we identify the pitch control system System’s identification is a procedure for accurately characterizing the dynamic response behavior of a complete aircraft, of a subsystem, or of an individual component from measureddata.To study the effectiveness of the controllers, the LQR Controller and PID Controller and fuzzy controller is developed for controlling the pitch angle of an aircraft system. Simulation results for the response of pitch controller are presented instep’s response. Finally, the performances of pitch control systems are investigated and analyzed based on common criteria of step’s response in order to identify which control strategy delivers better performance with respect to the desired pitch angle. It is found from simulation, that the fuzzy controller gives the best performance compared to PID and LQR controller.
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Vasudevan, M., R. Arumugam, and S. Paramasivam. "High-performance adaptive intelligent Direct Torque Control schemes for induction motor drives." Serbian Journal of Electrical Engineering 2, no. 1 (2005): 93–116. http://dx.doi.org/10.2298/sjee0501093v.
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This paper presents a detailed comparison between viable adaptive intelligent torque control strategies of induction motor, emphasizing advantages and disadvantages. The scope of this paper is to choose an adaptive intelligent controller for induction motor drive proposed for high performance applications. Induction motors are characterized by complex, highly non-linear, time varying dynamics, inaccessibility of some states and output for measurements and hence can be considered as a challenging engineering problem. The advent of torque and flux control techniques have partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Intelligent controllers are considered as potential candidates for such an application. In this paper, the performance of the various sensor less intelligent Direct Torque Control (DTC) techniques of Induction motor such as neural network, fuzzy and genetic algorithm based torque controllers are evaluated. Adaptive intelligent techniques are applied to achieve high performance decoupled flux and torque control. This paper contributes: i) Development of Neural network algorithm for state selection in DTC; ii) Development of new algorithm for state selection using Genetic algorithm principle; and iii) Development of Fuzzy based DTC. Simulations have been performed using the trained state selector neural network instead of conventional DTC and Fuzzy controller instead of conventional DTC controller. The results show agreement with those of the conventional DTC.
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Pushparajesh V. and Narayana Swamy Ramaiah. "Artificial Intelligent Controller-Based Speed Control of Switched Reluctance Motor." International Journal of Organizational and Collective Intelligence 11, no. 3 (2021): 1–13. http://dx.doi.org/10.4018/ijoci.2021070101.
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A new control methodology for controlling the speed of switched reluctance motor (SRM) drive using an intelligent controller is proposed in this paper. The control technology consists of an outer loop fuzzy controller as a speed controller and hysteresis current controller as the inner control loop along with control of switching angles for the four-phase, 8/6 SRM. In this proposed method, the speed control is optimized using the randomly determined fuzzy parameters. Fuzzy interfaced speed control of SRM is simulated using MATLAB/SIMULINK software. The robust performance of the fuzzy logic controller is valued using the least combinations (matrix) of rules for wide ranges of speed and is compared with the proportional-integral (PI) controller. Simulation results reveal that fuzzy-based speed controller gives enhanced performance in the form of quick speed response varies between 0.02sec to 0.12 sec over an extensive range of speed thereby improving the dynamic efficiency of the SRM drive.
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Zhang, Ju Ping, Ya Li Lu, Wei Xue, and Qing Hua Wu. "Fuzzy Neural Network Control of Cold Tandem Rolling Thickness Based on the Intelligent Integration." Applied Mechanics and Materials 278-280 (January 2013): 1585–92. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1585.
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Based on the analysis of thickness control theory, a controller combined fuzzy neural network with intellectual integration was designed in consideration of the nonlinear, large time delay, varying time characteristics of tandem cold mill thickness control. The controller adopted three layers BP network to realize fuzzy control and linked with intelligent integration in parallel for forming a three-dimensional full intelligent composite controller which enables the cold tandem rolling thickness fuzzy neural network control based on intelligent integration. Simulation results show that, compared with the conventional PID control, the compound intelligent control strategy can remarkable improved performance of control system of cold tandem rolling thickness.
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Tien-Loc Le, Minh-Triet Nguyen, Trong-Hien Chiem, et al. "Intelligent Controller Design for Precise Trajectory Control in Magnetic Levitation Systems." Journal of Technical Education Science 19, SI02 (2024): 14–23. http://dx.doi.org/10.54644/jte.2024.1426.
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As a form of soft computing technique, the application of fuzzy controllers for managing uncertain nonlinear systems has garnered significant attention from researchers. Although many fuzzy control methods have been proposed, most of them exhibit obvious limitations in weight learning and optimizing network structure. This paper aims to propose a design of a type-2 fuzzy cerebellar model articulation controller for uncertain nonlinear systems, which achieves high stability and accuracy for controlling magnetic levitation systems. The proposed controller is a combination of a type 2 fuzzy logic system and a cerebellar model articulation controller. A self-organizing algorithm is utilized to automatically construct the network structure. The adaptation laws based on the gradient descent method are derived to online update the network parameters. To ensure system stability, a Lyapunov stability function is employed. Finally, the numerical simulation results on trajectory tracking control of the magnetic levitation systems are given to illustrate the effectiveness and practicability of the proposed control method.
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Nikoo, Seied Yasser, Behrooz Rezaie, Zahra Rahmani, and Seied Jalil Sadati. "AN INTELLIGENT NEURO-FUZZY TERMINAL SLIDING MODE CONTROL METHOD WITH APPLICATION TO ATOMIC FORCE MICROSCOPE." IIUM Engineering Journal 17, no. 2 (2016): 185–204. http://dx.doi.org/10.31436/iiumej.v17i2.569.
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In this paper, a neuro-fuzzy fast terminal sliding mode control method is proposed for controlling a class of nonlinear systems with bounded uncertainties and disturbances. In this method, a nonlinear terminal sliding surface is firstly designed. Then, this sliding surface is considered as input for an adaptive neuro-fuzzy inference system which is the main controller. A proportinal-integral-derivative controller is also used to asist the neuro-fuzzy controller in order to improve the performance of the system at the begining stage of control operation. In addition, bee algorithm is used in this paper to update the weights of neuro-fuzzy system as well as the parameters of the proportinal-integral-derivative controller. The proposed control scheme is simulated for vibration control in a model of atomic force microscope system and the results are compared with conventional sliding mode controllers. The simulation results show that the chattering effect in the proposed controller is decreased in comparison with the sliding mode and the terminal sliding mode controllers. Also, the method provides the advantages of fast convergence and low model dependency compared to the conventional methods.
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Ambarapu, Sudhakar, Daniel Ravuri, Puli Sreekanth, Mummana Satyanarayana, Rao Kolukula Nitalaksheswara, and Venkata Rajanna Bodapati. "Analysis of fuzzy and neural controllers in direct torque controlled synchronous motors." Analysis of fuzzy and neural controllers in direct torque controlled synchronous motors 32, no. 2 (2023): 676–87. https://doi.org/10.11591/ijeecs.v32.i2.pp676-687.
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In this study, multiple intelligent control systems for direct torque-controlled Synchronous motors are implemented and compared. Using a lookup table to pick a vector through the inverter voltage space, the direct torque control (DTC) system can be obtained. To replicate the state selector in relation to the look-up table, intelligent controllers are deployed. Intelligent logic controllers like fuzzy and neural are used to regulate the performance of permanent magnet synchronous motors (PMSM). In steady-state applications, neural and fuzzy controllers reduce the torque ripple and stator current harmonic distortion. These outcomes are compared with those obtained when the synchronous motor was put under the basic direct torque control method using a proportional integral (PI) controller. The accuracy and effectiveness of the suggested control topologies have been verified using computer simulation software like MATLAB/Simulink.