Journal articles: 'Automated fuzzy controllers'

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Kropyvnytska, V. B., O. V. Yefremov, and H. N. Sementsov. "The improvement of the method for developing the knowledge data base for the intelligent support system of decision making on the Fuzzy Logic principles." Oil and Gas Power Engineering, no. 1(29) (April 30, 2018): 26–41. http://dx.doi.org/10.31471/1993-9868-2018-1(29)-26-41.

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The article deals with the issue of Fuzzy-simulation of controllers for solving practical problems of automated control. The peculiarities of Fuzzy-simulation of cascade controllers in the Matlab environment are studied. The presentation is accompanied by examples of the development of individual Fuzzy models and an illustration of conducting all necessary operations with fuzzy sets.

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Kharola, Ashwani, and Pravin P. Patil. "Automated Control and Optimisation of Overhead Cranes." International Journal of Manufacturing, Materials, and Mechanical Engineering 7, no. 3 (July 2017): 41–68. http://dx.doi.org/10.4018/ijmmme.2017070103.

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This study considers a fuzzy based computing technique for control and optimising performance of overhead gantry crane. The objective is to minimise load swing and stabilise crane position in the least possible time. The fuzzy controllers were designed using nine gaussian and triangular shape membership functions. The results clearly confirmed the effect of shape of memberships on performance of fuzzy controllers. Performance of overhead crane was measured in terms of settling time and overshoot. The study also demonstrates the influence of varying mass of the load, mass of crane and length of crane bar on stability of the crane. A mathematical model of the crane system has been derived to develop a simulink model of proposed system and performing simulations.

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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 (May 20, 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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Vesselenyi, Tiberiu, Simona Dzițac, Ioan Dzițac, and Mișu-Jan Manolescu. "Fuzzy and Neural Controllers for a Pneumatic Actuator." International Journal of Computers Communications & Control 2, no. 4 (December 1, 2007): 375. http://dx.doi.org/10.15837/ijccc.2007.4.2368.

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There is a great diversity of ways to use fuzzy inference in robot control systems, either in the place where it is applied in the control scheme or in the form or type of inference algorithms used. On the other hand, artificial neural networks ability to simulate nonlinear systems is used in different researches in order to develop automated control systems of industrial processes. In these applications of neural networks, there are two important steps: system identification (development of neural process model) and development of control (definition of neural control structure). In this paper we present some modelling applications, which uses fuzzy and neural controllers, developed on a pneumatic actuator containing a force and a position sensor, which can be used for robotic grinding operations. Following the simulation one of the algorithms was tested on an experimental setup. The paper also presents the development of a NARMA-L2 neural controller for a pneumatic actuator using position feedback. The structure had been trained and validated, obtaining good results.

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Karar, Mohamed E., and Mohamed A. El-Brawany. "Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller." Biomedical Engineering and Computational Biology 3 (January 2011): BECB.S6495. http://dx.doi.org/10.4137/becb.s6495.

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This paper presents a fuzzy neural network (FNN) control system to automatically manage the hemodynamic variables of patients with hypertension and congestive heart failure (CHF) via simultaneous infusion of cardiac drugs such as vasodilators and inotropic agents. The developed system includes two FNN sub-controllers for regulating cardiac output (CO) and mean arterial pressure (MAP) by cardiac drugs, considering interactive pharmacological effects. The adaptive FNN controller was tested and evaluated on a cardiovascular model. Six short-term therapy conditions of hypertension and CHF are presented under different sensitivities of a vasodilator drug. The results of the automated system showed that root mean square errors were ≤ 5.56 mmHg and ≤ 0.22 L min-1 for regulating MAP and CO, respectively, providing short settling time responses of MAP (≤ 10.9 min) and CO (≤ 8.22 min) in all therapy conditions. The proposed FNN control scheme can significantly improve the performance of cardiac drug infusion System.

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Shahsavari Pour, N., H. Asadi, and M. Pour Kheradmand. "Fuzzy Multiobjective Traffic Light Signal Optimization." Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/249726.

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Traffic congestion is a major concern for many cities throughout the world. In a general traffic light controller, the traffic lights change at a constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers in current use are based on the “time-of-the-day” scheme, which use a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. In this paper, the fuzzy traffic light controller is used to optimize the control of fluctuating traffic volumes such as oversaturated or unusual load conditions. The problem is solved by genetic algorithm, and a new defuzzification method is introduced. The performance of the new defuzzification method (NDM) is compared with the centroid point defuzzification method (CPDM) by using ANOVA. Finally, an illustrative example is presented to show the competency of proposed algorithm.

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Miraftab, V., and R. R. Mansour. "Fully Automated RF/Microwave Filter Tuning by Extracting Human Experience Using Fuzzy Controllers." IEEE Transactions on Circuits and Systems I: Regular Papers 55, no. 5 (June 2008): 1357–67. http://dx.doi.org/10.1109/tcsi.2008.916614.

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Batayneh, Wafa, and Yusra AbuRmaileh. "Decentralized Motion Control for Omnidirectional Wheelchair Tracking Error Elimination Using PD-Fuzzy-P and GA-PID Controllers." Sensors 20, no. 12 (June 22, 2020): 3525. http://dx.doi.org/10.3390/s20123525.

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The last decade observed a significant research effort directed towards maneuverability and safety of mobile robots such as smart wheelchairs. The conventional electric wheelchair can be equipped with motorized omnidirectional wheels and several sensors serving as inputs for the controller to achieve smooth, safe, and reliable maneuverability. This work uses the decentralized algorithm to control the motion of omnidirectional wheelchairs. In the body frame of the omnidirectional wheeled wheelchair there are three separated independent components of motion including rotational motion, horizontal motion, and vertical motion, which can be controlled separately. So, each component can have its different sub-controller with a minimum tracking error. The present work aims to enhance the mobility of wheelchair users by utilizing an application to control the motion of their attained/unattained smart wheelchairs, especially in narrow places and at hard detours such as 90˚ corners and U-turns, which improves the quality of life of disabled users by facilitating their wheelchairs’ maneuverability. Two approaches of artificial intelligent-based controllers (PD-Fuzzy-P and GA-PID controllers) are designed to optimally enhance the maneuverability of the system. MATLAB software is used to simulate the system and calculate the Mean Error (ME) and Mean Square Error (MSE) for various scenarios in both approaches, the results showed that the PD-Fuzzy-P controller has a faster convergence in trajectory tracking than the GA-PID controller. Therefore, the proposed system can find its application in many areas including transporting locomotor-based disabled individuals and geriatric people as well as automated guided vehicles.

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Dayev, Zh A., G. E. Shopanova, and B. A. Toksanbayeva. "Experience of fuzzy sets organizing on programmable logic controllers for developing automated control systems." Automation, Telemechanization and Communication in Oil Industry, no. 8 (2020): 45–49. http://dx.doi.org/10.33285/0132-2222-2020-8(565)-45-49.

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Mashadi, B., A. Kazemkhani, and R. Baghaei Lakeh. "An automatic gear-shifting strategy for manual transmissions." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, no. 5 (August 1, 2007): 757–68. http://dx.doi.org/10.1243/09596518jsce253.

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Based on two different criteria, namely the engine working conditions and the driver's intention, the governing parameters in decision making for gear shifting of an automated manual transmission are discussed. The gear-shifting strategy was designed by taking into consideration the effects of these parameters, with the application of a fuzzy control method. The controller structure is formed in two layers. In the first layer, two fuzzy inference modules are used to determine the necessary outputs. In the second layer a fuzzy inference module makes the decision of shifting by upshift, downshift, or maintain commands. The behaviour of the fuzzy controller is examined by making use of ADVISOR software. It is shown that at different driving conditions the controllers make correct decisions for gear shifting accounting for the dynamic requirements of the vehicle. It is also shown that the controller based on both the engine state and the driver's intention eliminates unnecessary shiftings that are present when the intention is overlooked. A microtrip is designed in which a required speed in the form of a step function is demanded for the vehicle on level or sloping roads. Both strategies for the vehicle to reach the maximum speed starting from rest allow the gear shift to be made consecutively. Considerable differences are observed between the two strategies in the deceleration phase. The engine-state strategy is less sensitive to downshift, taking even unnecessary upshift decisions. The state intention strategy, however, interprets the driver's intention correctly for decreasing speed and utilizes engine brake torque to reduce the vehicle speed in a shorter time.

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Ridwan, Firman, and Xun Xu. "Realization CNC Controller Enable Machine Condition Monitoring Architecture Based on STEP-NC Data Model." Advanced Materials Research 383-390 (November 2011): 990–94. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.990.

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The drive for increased productivity, reduced production time and cost, reduced defective parts and relaxed machine design constraints, pushes for real-time control and optimization for machining operations. This vision can be realized if the current CNC controllers can be adaptable, portable, interoperable and intelligent in responding quickly and efficiently in the product lifecycle domain. In this study, the development of a realization CNC controller enabled machine condition monitoring architecture based on STEP-NC data model to support an automated and intelligent machining environment is introduced. The controller allows canonical machine commands to be executed with a fuzzy feed-rate optimization modul to serve the purpose of allowing in-process optimization to be automatically incorporated during on-going machining operation. This research has shown that it is more prolific to utilize high-level data structure as well as a universal interface for machine execution.

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Marcano, Mauricio, José A. Matute, Ray Lattarulo, Enrique Martí, and Joshué Pérez. "Low Speed Longitudinal Control Algorithms for Automated Vehicles in Simulation and Real Platforms." Complexity 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/7615123.

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Advanced Driver Assistance Systems (ADAS) acting over throttle and brake are already available in level 2 automated vehicles. In order to increase the level of automation new systems need to be tested in an extensive set of complex scenarios, ensuring safety under all circumstances. Validation of these systems using real vehicles presents important drawbacks: the time needed to drive millions of kilometers, the risk associated with some situations, and the high cost involved. Simulation platforms emerge as a feasible solution. Therefore, robust and reliable virtual environments to test automated driving maneuvers and control techniques are needed. In that sense, this paper presents a use case where three longitudinal low speed control techniques are designed, tuned, and validated using an in-house simulation framework and later applied in a real vehicle. Control algorithms include a classical PID, an adaptive network fuzzy inference system (ANFIS), and a Model Predictive Control (MPC). The simulated dynamics are calculated using a multibody vehicle model. In addition, longitudinal actuators of a Renault Twizy are characterized through empirical tests. A comparative analysis of results between simulated and real platform shows the effectiveness of the proposed framework for designing and validating longitudinal controllers for real automated vehicles.

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Nantogma, Sulemana, Keyu Pan, Weilong Song, Renwei Luo, and Yang Xu. "Towards Realizing Intelligent Coordinated Controllers for Multi-USV Systems Using Abstract Training Environments." Journal of Marine Science and Engineering 9, no. 6 (May 22, 2021): 560. http://dx.doi.org/10.3390/jmse9060560.

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Unmanned autonomous vehicles for various civilian and military applications have become a particularly interesting research area. Despite their many potential applications, a related technological challenge is realizing realistic coordinated autonomous control and decision making in complex and multi-agent environments. Machine learning approaches have been largely employed in simplified simulations to acquire intelligent control systems in multi-agent settings. However, the complexity of the physical environment, unrealistic assumptions, and lack of abstract physical environments derail the process of transition from simulation to real systems. This work presents a modular framework for automated data acquisition, training, and the evaluation of multiple unmanned surface vehicles controllers that facilitate prior knowledge integration and human-guided learning in a closed-loop. To realize this, we first present a digital maritime environment of multiple unmanned surface vehicles that abstracts the real-world dynamics in our application domain. Then, a behavior-driven artificial immune-inspired fuzzy classifier systems approach that is capable of optimizing agents’ behaviors and action selection in a multi-agent environment is presented. Evaluation scenarios of different combat missions are presented to demonstrate the performance of the system. Simulation results show that the resulting controllers can achieved an average wining rate between 52% and 98% in all test cases, indicating the effectiveness of the proposed approach and its feasibility in realizing adaptive controllers for efficient multiple unmanned systems’ cooperative decision making. We believe that this system can facilitate the simulation, data acquisition, training, and evaluation of practical cooperative unmanned vehicles’ controllers in a closed-loop.

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Ryabchikov, M. Yu, E. S. Ryabchikova, and S. A. Filippov. "A Fuzzy Logic-Based System for Controlling the Temperature of Steam Exiting a Superheater for the Purpose of Preemptive Perturbation Compensation." Mekhatronika, Avtomatizatsiya, Upravlenie 22, no. 4 (April 5, 2021): 181–90. http://dx.doi.org/10.17587/mau.22.181-190.

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This paper discusses the issue of adjusting the temperature of steam exiting a superheater in an environment that is affected by perturbations due to the sudden and significant fluctuations in the inlet steam temperature. Using the superheater at the Magnitogorsk Iron & Steel Works as an example, we highlight that a slow response to the aforementioned perturbations in the systems that adjust for deviations leads to undesired rises and drops in the outlet steam temperature. We review the current suggestions on adjusting the temperature of steam exiting a superheater and determine the main reasons behind the drop in adjustment quality. These reasons are related to a significant lag and the variability of the control object’s features, which make preemptive perturbation control difficult. In order to control such environments, we propose a system with two degrees of freedom, which combines a proportional-integral controller and a fuzzy logic-based controller. In the system that we are proposing, the changes in the controlled parameter (depending on the input value) are adjusted within the main loop that has a standard controller and negative feedback, while the perturbations are removed by using a secondary loop, which also has negative feedback, a fuzzy logic-based controller, and a simulation of the object without the component that accounts for the lag. For situations when the information on the object’s features is precise, we describe the specifics of the loops’ interaction, specifically in cases when the task processing loop does not respond to the perturbations in the inlet steam temperature, thus allowing for setting up the loops’ controllers separately. In situations when the inlet steam temperature is experiencing perturbations, the impact of the lag on adjustment quality only becomes evident when the trajectory of the transition process shifts along the time scale by a lag value, which is completely in line with the Smith predictor principles. The system is focused on synthesizing the fuzzy logic rules and refining the parameters of the simulation used for adjustment purposes, based on the results of automated computer-aided control simulation. We propose a structural modification of the control system that makes it possible to compensate for any residual control errors caused by the non-linear structure of the fuzzy controller; this reduces the number of requirements for those set-up parameters where the value selection is based on the needs of simulation modeling, which requires a lot of computing resources. We demonstrate the results of simulation experiments that compare the efficiency of control using the system suggested and the efficiency of control using a system with a standard controller only. The computer simulation was performed in the MATLAB Simulink environment. We reaffirm that a combined control system performs better when adjusting the steam temperature.

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Madhava, Meghna, N. Meghana, Mulpuru Supriya, Div ya, and Siddalingesh S. Navalgund. "Automatic Train Control System Using Fuzzy Logic Controller." Bonfring International Journal of Research in Communication Engineering 6, Special Issue (November 30, 2016): 56–61. http://dx.doi.org/10.9756/bijrce.8201.

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Xu, Hong Hua, Li Li, and Hong Yu Zhai. "A Fuzzy PID Controller Design and Application." Advanced Materials Research 952 (May 2014): 279–82. http://dx.doi.org/10.4028/www.scientific.net/amr.952.279.

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Control system of microcomputer controlled electro-hydraulic servo rock three triaxial test instrument adopts closed loop control and the control strategy is based on PID control. Because the conventional PID control cannot meet the control accuracy and stability of system, the intelligent fuzzy controller is introduced to realize the automatic processing in control process. The fuzzy PID controller's design procedure is given to determine the fuzzy domain, fuzzy rules, calculating the reference method and defuzzification process of membership function.

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Kozlov, Serhii, Olena Levon, Nataliya Kuzmenko, and Serhii Rymar. "FEATURES OF DESIGNING FUZZY REGULATORS IN THE COMPOSITION OF AUTOMATIC CONTROL SYSTEMS OF SEMICONDUCTOR COMPENSATORS." Energy saving. Power engineering. Energy audit., no. 11-12(153-154) (May 9, 2021): 47–52. http://dx.doi.org/10.20998/2313-8890.2020.11.06.

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The article discusses the design features of fuzzy controllers as part of automatic control systems for semiconductor compensators. Tendencies to the use of fuzzy logic and neural networks to solve problems with a large number of variables that are random are analyzed. The functional scheme of the automatic control system on the basis of fuzzy logic is considered. Some features of the fuzzy regulator are given. Questions of optimization of parameters of digital fuzzy controllers are investigated. The importance of the choice of the method of parametric adjustment of the fuzzy controller is shown. The options for choosing the quality criterion for fuzzy controllers are considered. The market of commercial software products for working with fuzzy logic is analyzed.

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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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Pilla, Azar, and Gorripotu. "Impact of Flexible AC Transmission System Devices on Automatic Generation Control with a Metaheuristic Based Fuzzy PID Controller." Energies 12, no. 21 (November 2, 2019): 4193. http://dx.doi.org/10.3390/en12214193.

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The present work proposes a teaching–learning-based optimization (TLBO)-tuned fuzzy proportional-integral-derivative (PID) controller of two-area hydro-thermal generating units for automatic generation control (AGC). The proposed system takes into account the physical constraints such as transport delay (TD), generation rate constraint (GRC), and governor dead band (GDB) nonlinearities. Firstly, fuzzy PID controllers were designed for both the areas and their gains were optimized using various minimization objective function criteria. Furthermore, applications of flexible alternating current transmission system (FACTS) devices such as static synchronous series compensator (SSSC), thyristor-controlled series capacitor (TCSC), thyristor-controlled phase shifter (TCPS), and unified power flow controller (UPFC) were investigated by integrating FACTS devices in appropriate locations of the system. The simulation results revealed that the minimum objective values were attained when the UPFC was placed in the system. Lastly, robustness analysis was done to observe the capability of the proposed controller with UPFC by changing system parameters and considering random load disturbances.

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Espitia, Helbert, Iván Machón, and Hilario López. "Optimization of a Fuzzy Automatic Voltage Controller Using Real-Time Recurrent Learning." Processes 9, no. 6 (May 27, 2021): 947. http://dx.doi.org/10.3390/pr9060947.

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The automatic voltage regulator is an important component in energy generation systems; therefore, the tuning of this system is a fundamental aspect for the suitable energy conversion. This article shows the optimization of a fuzzy automatic voltage controller for a generation system using real-time recurrent learning, which is a technique conventionally used for the training of recurrent neural networks. The controller used consists of a compact fuzzy system based on Boolean relations, designed having equivalences with PI, PD, PID, and second order controllers. For algorithm implementation, the training equations are deduced considering the structure of the second order compact fuzzy controller. The results show that a closed-loop fuzzy control strategy was successfully implemented using real-time recurrent learning. In order to implement the controllers optimization, different weighting values for error and control action are used. The results show the behavior of the configurations used and its performance considering the steady state error, overshoot, and settling time.

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Yang, Jie, Yingkai Guo, and Xin Huang. "A software development system for fuzzy control." Robotica 18, no. 4 (July 2000): 375–80. http://dx.doi.org/10.1017/s0263574799002398.

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Fuzzy control has been widely applied in industrial controls and domestic electrical equipment. The automatic learning of fuzzy rules is a key technique in fuzzy control. In this paper, a software development system for fuzzy control is presented. Since the learning of fuzzy rules can be seen as finding the best classifications of fuzzy memberships of input-output variables in a fuzzy controller, it can also be seen as the combination optimization of input-output fuzzy memberships. Multi-layer feedforward network and genetic algorithms (GA) can be used for the automatic learning of fuzzy rules. The algorithms and their characteristics are described. The software development system has been successfully used for the design of some fuzzy controllers.

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Chu, Pengzi, Yi Yu, Danyang Dong, Hui Lin, and Jianjun Yuan. "NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System." Mathematical Problems in Engineering 2020 (March 24, 2020): 1–20. http://dx.doi.org/10.1155/2020/3731749.

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Automatic train operation (ATO) system is one of the important components in advanced train operation control systems. Ideal controllers are expected for the automatic driving function of ATO systems. Aiming at the intelligence requirements of the systems, an NSGA-II-based parameter tuning method for the fuzzy immune PID (FI-PID) controller and a grey model GM(1,1)-based fuzzy grey immune PID (FGI-PID) controller were proposed. Taking a maglev train’s model as the control object and a velocity-time curve as the input, the feasibility of the parameter tuning method for the FI-PID controller and the applicability of the FI-PID controller and the FGI-PID controller for the ATO system were tested. The results showed that the optimized parameters were ideal, the two controllers all showed good performance on the indicators of traceability and comfort level, and the FGI-PID controller performed better than the FI-PID controller. The results exhibited the effectiveness of the proposed methods.

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Su, Zhi-Gang, Pei-Hong Wang, and Yu-Fei Zhang. "Automatic T-S fuzzy model with application to designing predictive controller." Computer Science and Information Systems 9, no. 4 (2012): 1577–601. http://dx.doi.org/10.2298/csis120222059s.

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A novel methodology is proposed to automatically extract T-S fuzzy model with enhanced performance using VABC-FCM algorithm, a novel Variable string length Artificial Bee Colony algorithm (VABC) based Fuzzy C-Mean clustering technique. Such automatic methodology not requires a priori specification of the rule number and has low approximation error and high prediction accuracy with appreciate rule number. Afterward, a new predictive controller is then proposed by using the automatic T-S fuzzy model as the dynamic predictive model and VABC as the rolling optimizer. Some experiments were conducted on the superheated steam temperature in power plant to validate the performance of the proposed predictive controller. It suggests that the proposed controller has powerful performance and outperforms some other popular controllers.

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He, Xiao Feng, Wei Chen, Bi Hai Zhu, Zheng Yi Jiang, and Christopher Cook. "Multifactor Optimization of a Fuzzy-PID Controller Using Genetic Algorithm." Advanced Materials Research 422 (December 2011): 268–75. http://dx.doi.org/10.4028/www.scientific.net/amr.422.268.

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The design of a Fuzzy-PID controller involves setting the fuzzy rules, membership functions and its associated scaling factors. How to obtain a better control result and how these scaling factors affect the controller’s performance are still a challenge. In this paper, the automatic position control system of a Hille 100 experimental rolling mill was used as a research testbed. Based on the mathematical control model of the rolling mill, a Fuzzy-PID controller was developed, and the process of implementing global optimization considering all these factors simultaneously by using genetic algorithm is introduced in detail. Through simulation, the performance of the control system with multifactor optimized Fuzzy-PID controller is given, and compared with that with only the fuzzy rules optimized in the controller. By simulation tests, it is found that these factors will influence the control performance of the controller, and that they are highly coupled with each other. The more factors for a Fuzzy-PID controller are optimized, the better the solution will be. It can also be inferred from the study that asymmetrical membership functions have more potential in improving a fuzzy controller’s performance than symmetrical ones. The multifactor optimization method presented in this paper can in principle also be used to solve other complicated optimization issues.

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El, Emary, Walid Emar, and Musbah Aqel. "The adaptive fuzzy designed PID controller using wavelet network." Computer Science and Information Systems 6, no. 2 (2009): 141–63. http://dx.doi.org/10.2298/csis0902141e.

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During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of the fuzzy set theory, especially in the realm of the industrial processes, which do not lend themselves to control by conventional methods because of a lack of quantitative data regarding the inputoutput relations i.e., accurate mathematical models. The fuzzy logic controller based on wavelet network provides a means of converting a linguistic control strategy based on expert knowledge into an automatic strategy. In the available literature, one can find scores of papers on fuzzy logic based controllers or fuzzy adaptation of PID controllers. However, relatively less number of papers is found on fuzzy adaptive control, which is not surprising since fuzzy adaptive control is relatively new tool in control engineering. In this paper, fuzzy adaptive PID controller with wavelet network is discussed in subsequent sections with simulations. An adaptive neural network structure was proposed. This structure was used to replace the linearization feedback of a second order system (plant, process). Also, in this paper, it is proposed that the controller be tuned using Adaptive fuzzy controller where Adaptive fuzzy controller is a stochastic global search method that emulates the process of natural evolution. It is shown that Adaptive fuzzy controller be capable of locating high performance areas in complex domains without experiencing the difficulties associated with high dimensionality or false optima as may occur with gradient decent techniques. From the output results, it was shown that Adaptive fuzzy controller gave fast convergence for the nonparametric function under consideration in comparison with conventional Neural Wavelet Network (NWN).

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Ma, Jing Qing, and Hai Bo Chen. "Research on the Application of the Fuzzy Control Algrithm in the HAPC System." Applied Mechanics and Materials 220-223 (November 2012): 157–60. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.157.

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The HAPC(Hydraulic Automatic Position Control) requires quick dynamic response and high control accuracy. Based on the research of the HAPC system, I build the HAPC mathematical model, then design both the Conventional PID controller and fuzzy PID controllers, simulate the two control methods using the MATLAB software, analyze the main factors which influence the results. The simulation results show that the fuzzy PID controller has the better effect in the dynamic response and the control accuracy than the former.

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Wang, Li-Xin. "Automatic design of fuzzy controllers." Automatica 35, no. 8 (August 1999): 1471–75. http://dx.doi.org/10.1016/s0005-1098(99)00044-8.

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Guo, Bei Tao, Hong Yi Liu, Yang Jiang, Fei Wang, and Zhong Luo. "Adaptive Fuzzy PID Designed for Solenoid Valve Test System." Applied Mechanics and Materials 16-19 (October 2009): 910–14. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.910.

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The performance of solenoid valve is directly related to the security and reliability of industrial system. This paper presents the design of the automatic test platform for solenoid valve. The architecture of the hydraulic subsystem and the control model of providing precise pressure for testing solenoid valve were built up. A self-adaptive fuzzy PID controller, which can dynamically modify the controller’s parameters by using fuzzy rules presented in this paper, was designed with considering the dynamic characteristics of hydraulic system. Simulation results show that the self-adaptive fuzzy PID controller, which compared with conventional PID controller, can obtain better dynamic performance and fast response.

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Shanmugasundaram, V., and . "Analysis of Energy Storage Device in Hydro-Thermal Power Systems Using Fuzzy Logic Controller." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 446. http://dx.doi.org/10.14419/ijet.v7i4.5.20203.

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This work presents the Automatic generation control in an interconnected hydro-thermal power system to stabilize the frequency oscillations due to load changes. Advantage of facts devices are utilized here to improve the stability of the system. Thyristor controlled phase shifter (TCPS) is added in the tie line whose input is the area control error. The output of the phase shifter is the change in phase angle based on the error. The TCPS-RFB (Redox flow battery) and TCPS - SMES (Superconducting magnetic energy storage) combinations are compared against each other in terms of peak overshoot and settling time. The results proves that SMES is most effective than RFB. Then the LFC of hydrothermal plant with TCPS in tie line and SMES in one area is analyzed with different controllers like P, PI, PID and Fuzzy logic controller to find the best controller for these specific applications. The criterion for comparison remains to be the same. And finally fuzzy logic controller is found to best among the ones under consideration.

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Sahu, Prakash Chandra, and Ramesh Chandra Prusty. "Stability Analysis in RECS Integrated Multi-area AGC System with Modified- SOS Optimized Fuzzy Controller." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 12, no. 6 (November 22, 2019): 532–42. http://dx.doi.org/10.2174/2352096511666180904113130.

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Background: Automatic Generation Control (AGC) of multi-area nonlinear power system integrated with wind energy based Renewable Energy Conversion System (RECS). Methods: A fuzzy PID controller has been proposed for AGC of a three equal area thermal system integrated with RECS. Different physical nonlinear constraints like Governor Dead Band (GDB) and boiler dynamics are introduced in the model for realization of non linear and realistic of proposed multi area power system. To determine the optimum gain parameter, a Modified Symbiotic Organism Search (M-SOS) algorithm has been used along with a fitness function which based on Integral of Time Multiplied Absolute Error (ITAE). Results: For performance analysis, the performance of proposed M-SOS optimized fuzzy-PID controller is compared with PI, PID and fuzzy PI controllers. For technique comparison, performance of proposed M-SOS technique is compared with original SOS and conventional PSO algorithms. Robustness of proposed controller has also been verified by varying applied load and system parameters. Conclusion: It is observed that M-SOS technique exhibits improved performance over original SOS and PSO algorithms. It is also observed that proposed Fuzzy-PID controller provides better system performance than PI, PID and fuzzy PI controllers. It has been observed that the proposed M-SOS tuned fuzzy PID controller improves settling time of frequency response in area 1 by 11.30%, 15% and 17.75% compared to M-SOS tuned fuzzy PI, PID and PI controllers respectively. Significant improvements in settling time, peak overshoot and peak undershoot of the frequency response in area 2 and tie line power are observed with the implementation this proposed approach.

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Sheikh, M. R. I., R. Takahashi, and J. Tamura. "Robust Stabilizing Controllers to Automatic Generation Control for Load Frequency Control Application." Journal of Scientific Research 2, no. 2 (April 26, 2010): 285–93. http://dx.doi.org/10.3329/jsr.v2i2.3063.

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Since superconducting magnetic energy storage (SMES) unit with a self-commutated converter is capable of controlling both the active and reactive powers simultaneously and quickly, increasing attention has been focused recently on power system stabilization by SMES control. This study presents the effects of novel control strategies of self-tuned fuzzy proportional integral (FPI) controller and fuzzy frequency (FF) controller associated with the automatic generation control (AGC) including SMES unit. The effects of the self-tuning configuration with FPI controller in AGC is also compared with that of FF controlled AGC on SMES control. The simulation results show that both self tuning control schemes of AGC are very effective in damping out of the oscillations caused by load disturbances and it is also seen that the FF controlled AGC with SMES perform better primary frequency control compared to FPI controlled AGC with SMES. Keywords: Load frequency control; Single area power system; FPI controller; FF controller; SMES unit. © 2010 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. DOI: 10.3329/jsr.v2i2.3063 J. Sci. Res. 2 (2), 285-293 (2010)

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Salmaninejad, Amir, and Rene V. Mayorga. "Sensor-less Brushed DC Motor Speed Control with Intelligent Controllers." WSEAS TRANSACTIONS ON SYSTEMS 20 (July 12, 2021): 140–48. http://dx.doi.org/10.37394/23202.2021.20.16.

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A Direct Current (DC) Motor is usually supposed to be operated at a desired speed even if the load on the shaft is exposed to changes. One of its applications is in automatic door controllers like elevator automatic door drivers. Initially, to achieve this aim, a closed loop control can be applied. The speed feedback is usually prepared by a sensor (encoder or tachometer) coupled to the motor shaft. Most of these sensors do not always perform well, especially in elevator systems, where high levels of noise, physical tensions of the mobile car, and maintenance technicians walking on the car, make this environment too noisy. This Paper presents a new approach for precise closed loop control of the DC motor speed without a feedback sensor, while the output load is variable. The speed here is estimated by the Back EMF (BEMF) voltage obtained from the armature current. First, it is shown that a PID controller cannot control this process alone, and then intelligent controllers, Fuzzy Logic Controller (FLC) and Adaptive Neuro Fuzzy Inference Systems (ANFIS), assisting PID are applied to control this process. Finally, these controllers’ performance subjected to a variable mechanical load on the motor shaft are compared.

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Zhao, Yu Chi, and Jing Liu. "The Application of Fuzzy Control in Computer Control." Advanced Materials Research 756-759 (September 2013): 349–53. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.349.

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Fuzzy control theory is a computer numerical control theory based on fuzzy set theory, fuzzy language variable and fuzzy logic reasoning. It is widely used for it doesnt require exact mathematical model of controlled object in system design, so that fuzzy control has an advantage in researching high nonlinear system like inverted pendulum. However, rule explosion problem is unavoidable when we use fuzzy control theory to solve some multivariable system control problems such as inverted pendulum. This paper presents the application of the optimal control theory to reduce the input variable dimensions and the rules of the fuzzy controller through designing a fusion function, solving rule explosion problem successfully. The paper also discusses the control effect influenced by quantification factors, promoting performance quality of the fuzzy controller by setting threshold value to make quantification factors automatic regulation.

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Soetedjo, Aryuanto, Yusuf Nakhoda, and Choirul Saleh. "Embedded Fuzzy Logic Controller and Wireless Communication for Home Energy Management Systems." Electronics 7, no. 9 (September 10, 2018): 189. http://dx.doi.org/10.3390/electronics7090189.

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Energy management systems in residential areas have attracted the attention of many researchers along the deployment of smart grids, smart cities, and smart homes. This paper presents the implementation of a Home Energy Management System (HEMS) based on the fuzzy logic controller. The objective of the proposed HEMS is to minimize electricity cost by managing the energy from the photovoltaic (PV) to supply home appliances in the grid-connected PV-battery system. A fuzzy logic controller is implemented on a low-cost embedded system to achieve the objective. The fuzzy logic controller is developed by the distributed approach where each home appliance has its own fuzzy logic controller. An automatic tuning of the fuzzy membership functions using the Genetic Algorithm is developed to improve performance. To exchange data between the controllers, wireless communication based on WiFi technology is adopted. The proposed configuration provides a simple effective technology that can be implemented in residential homes. The experimental results show that the proposed system achieves a fast processing time on a ten-second basis, which is fast enough for HEMS implementation. When tested under four different scenarios, the proposed fuzzy logic controller yields an average cost reduction of 10.933% compared to the system without a fuzzy logic controller. Furthermore, by tuning the fuzzy membership functions using the genetic algorithm, the average cost reduction increases to 12.493%.

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Chertilin, K. E., and V. D. Ivchenko. "Configuring adaptive PID-controllers of the automatic speed control system of the GTE." Russian Technological Journal 8, no. 6 (December 18, 2020): 143–56. http://dx.doi.org/10.32362/2500-316x-2020-8-6-143-156.

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For non-stationary objects with parameters, which could be changed significantly during operation, using conventional controllers in the form of proportional-integraldifferential regulators may not provide the required quality of the system. Therefore, it is desirable to create an adaptive automatic control system with the structure and parameters of the control regulator that are purposefully changed to ensure the system adaptation, that is based on information about the properties of the object of regulation and external influences, to the changing operating conditions. The problem of designing adaptive systems is one of the most important in control theory and related fields. This is conditioned by two factors: the complexity of solving the problem as a whole and the presence of a large number of technically diverse situations that need to be adapted and optimized. In the paper, an adaptive system for the automatic control of the speed of a gas turbine engine, which includes a magnetic amplifier, a DC motor with a gearbox, a fuel supply valve and a tachogenerator, is developed. For adaptive control execution, three proportional-integral-differential controllers were proposed: "classic", fuzzy and neurofuzzy. The parameters of the "classic" controller were optimized using linear programming methods. The membership functions and the rule base were proposed for the fuzzy controller. An adaptation algorithm was selected for the neuro-fuzzy controller. Three controllers were used for three engine-operating modes: low-gas, cruiser and maximum during the computer simulation of the system. A comparative analysis of the quality of the three regulators was performed and it is based on the obtained transient characteristics. The derived results can be used in the development of automatic control systems for gas turbine engines.

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Mei, Hong. "Control of Automobile's Automatic Parking." Advanced Materials Research 339 (September 2011): 28–31. http://dx.doi.org/10.4028/www.scientific.net/amr.339.28.

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An automatic parking controller is proposed. Fuzzy control is taken to simulate the action of experienced driver as an alternative to conventional methods. The angle between the midline of the car and ideal path and the distance between the midpoint of the car and the ideal path are taken as the inputs of the fuzzy controller. The angle of the steering wheel is taken as the output of the fuzzy controller. A set of fuzzy logic rules are build for reasoning. With sensors installed in the car to replace people’s eyes and computer to replace people’s brain, the automatic parking system is more precise and quicker than human’s parking. At last, simulation is made and proved the validity of the proposed method.

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Qian, Zheng Zai, Gong Cai Xin, and Jin Niu Tao. "Predictive Control Based on Fuzzy Expert PID Tuning Control." Advanced Materials Research 466-467 (February 2012): 1207–11. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1207.

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In decade years, several simple methods for the automatic tuning of PID controllers have been proposed. There have been different approaches to the problem of deriving a PID-like adaptive controller. All of these can be classified into two broad categories: model-based; or expert systems. In this paper a new expert adaptive controller is proposed in which the underlying control law is a PID structure. The design is based on the fuzzy logic and the generalized predictive control theory. The proposed controller can be applied to a large class of systems which is model uncertainty or strong non-linearity. Simulation results have also been illustrated. It shows that the proposed expert PID-like controller performed well than generally used PID.

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Vrba, Josef, and Ywetta Purová. "A contribution to the identification in fuzzy-logic control." Collection of Czechoslovak Chemical Communications 55, no. 4 (1990): 951–63. http://dx.doi.org/10.1135/cccc19900951.

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A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.

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Koondhar, Mohsin A., Muhammad U. Keerio, Rameez A. Talani, Kamran A. Samo, Muhammad S. Bajwa, Noor H. Mugheri, and Irfan A. Channa. "Optimal Tuning of Fuzzy Logic Controller Based Speed Control of DC Motor." Quaid-e-Awam University Research Journal of Engineering Science & Technology 19, no. 1 (June 30, 2021): 77–80. http://dx.doi.org/10.52584/qrj.1901.11.

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Fuzzy logic controller (FLC) has become popular in the speed control application of DC motors with automatic adjustment function. In this article, the performance of a specific FLC controlled DC motor is studied. The exceed speed is observed with a stabilization time, thus confirming the FLC behavior. Therefore, FLC must be set to obtain the required performance by applying appropriate expert rules, the minimum overshoot and installation time can be maintained within the required values. With the help of FLC, the manual adjustment function is gradually eliminated, and the intelligent adjustment function is at the center position, and the performance is satisfactory. FLC DC motor speed control is implemented in MATLAB environment. The results show that the FLC method has the smallest bypass, smallest transient and steady-state error, and shows higher FLC efficiency as compared with other conventional controllers.

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Zhang, Ling, Jun Peng Shao, Gui Tao Sun, Bing Wei Gao, Zhao Hui Jin, Jian Zhu, and Xiao Ning Mu. "Research on Automatic Leveling System of Railway Rescue Crane." Advanced Materials Research 909 (March 2014): 241–46. http://dx.doi.org/10.4028/www.scientific.net/amr.909.241.

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Automatic leveling system used in railway rescue crane has characteristics such as time variance, nonlinearity and strong interference, which bring a variety of difficulties in control process into existence. This paper introduces the working principle and mathematical model of automatic leveling system used in railway rescue crane. A fuzzy PID controller based on fuzzy logic control algorithm and conventional PID algorithm is designed, MATLAB simulation results show that the effectiveness of the proposed fuzzy PID controller, and the fuzzy PID control strategy has better effect on the automatic leveling system used in railway rescue crane.

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Al-mousa, Amjed A., Ali H. Nayfeh, and Pushkin Kachroo. "Control of Rotary Cranes Using Fuzzy Logic." Shock and Vibration 10, no. 2 (2003): 81–95. http://dx.doi.org/10.1155/2003/746542.

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Rotary cranes (tower cranes) are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them has become difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control attractive.In this work a fuzzy logic controller is introduced with the idea of “split-horizon”; that is, fuzzy inference engines (FIE) are used for tracking the position and others are used for damping the load oscillations. The controller consists of two independent sub-controllers: radial and rotational. Each of these controllers has two fuzzy inference engines (FIE). Computer simulations are used to verify the performance of the controller. Three simulation cases are presented. In the first case, the crane is operated in the gantry (radial) mode in which the trolley moves along the jib while the jib is fixed. In the second case (rotary mode), the trolley moves along the jib and the jib rotates. In the third case, the trolley and jib are fixed while the load is given an initial disturbance. The results from the simulations show that the fuzzy controller is capable of keeping the load-oscillation angles small throughout the maneuvers while completing the maneuvers in relatively reasonable times.

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Tripathy, Debasis, Nalin Behari Dev Choudhury, and Binod Kumar Sahu. "Performance Improvement Using GOA-Based Fuzzy-2D-PIDF Controller for AGC of Multi-Area Power System." International Journal of Social Ecology and Sustainable Development 12, no. 2 (April 2021): 1–20. http://dx.doi.org/10.4018/ijsesd.2021040101.

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Automatic generation control (AGC) is an automation scheme that regulates the output of several generators employed at different areas of an interconnected power system simultaneously in response to load variation in the most economical way. This article implements a fuzzy-two degree of freedom-PID controller considering derivative filter (F-2D-PIDF) optimally tuned through grasshopper optimization algorithms (GOA) for AGC of a three unequal area interconnected power system. Initially, comparative performance analysis is carried out for conventional PID controllers optimally designed by particle swarm optimization, teaching learning-based optimization and GOA techniques. After ensuring better performance from GOA based PID controller, the study extended to establish dominance of the proposed F-2D-PIDF controller over others like PID, PID with derivative filter (PIDF), two degree of freedom-PIDF, and fuzzy-PIDF for the same power system in presence and absence of nonlinearities with GOA framework. In all these above studies, a load perturbation of 0.01 p.u. is applied in area-1. Comparative performance analysis reveals that GOA based F-2D-PIDF controller outperforms other controllers in all aspects. Finally, robustness of the proposed controller verified by varying system parameters and loading condition.

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Mattas, Konstantinos, George Botzoris, and Basil Papadopoulos. "Safety aware fuzzy longitudinal controller for automated vehicles." Journal of Traffic and Transportation Engineering (English Edition) 8, no. 4 (August 2021): 568–81. http://dx.doi.org/10.1016/j.jtte.2020.12.006.

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Al Tahtawi, Adnan Rafi, and Robi Kurniawan. "PH control for deep flow technique hydroponic IoT systems based on fuzzy logic controller." Jurnal Teknologi dan Sistem Komputer 8, no. 4 (October 13, 2020): 323–29. http://dx.doi.org/10.14710/jtsiskom.2020.13822.

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In hydroponic cultivation sites, pH control is still carried manually by checking the pH level with a pH meter and providing a pH balancing liquid manually. This study aims to design an automatic pH control system in the Deep Flow Technique (DFT) hydroponic system that uses the Internet of Things (IoT) based Fuzzy Logic Controller (FLC). The SKU SEN0161 sensor detects the pH value as FLC inputs in an error value and its changes. These inputs are processed using Mamdani FLC embedded in the Arduino Mega 2560 microcontroller. The FLC produces an output in a pH liquid feeding duration using the peristaltic pump. The results showed that FLC could maintain the pH value according to the set point with a settling time of less than 50 seconds, both with disturbance by adding pH liquid and without disturbance. The pH value can also be displayed on the website interface system as a monitoring system.

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Prusik, Jacek, and Tomasz Rogalski. "Automatic removal of the plane from a spin using fuzzy logic controller." Transportation Overview - Przeglad Komunikacyjny 2019, no. 2 (February 1, 2019): 45–53. http://dx.doi.org/10.35117/a_eng_19_02_04.

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The paper presents a concept of automatic control system recovering an aircraft from the spin using fuzzy logic controller. Control system causing: stall, spin, spin recovery, dive recovery and switching on classic heading and altitude autopilots, was created in Matlab – Simulink software, which was connected to the flight simulator X-Plane. During tests developed control algorithms were checked and tuned. At the end graphs of flight parameters recorded during simulation were analyzed, and properties of designed control system were evaluated. Particular attention was paid to the design of a fuzzy logic controller stopping autorotation of the aircraft. On the output it controlled the position of the rudder, while on input it received a signal being a function of the angular velocity of the aircraft.

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Gheisarnejad, Meysam, and Mohammad Hassan Khooban. "Design an optimal fuzzy fractional proportional integral derivative controller with derivative filter for load frequency control in power systems." Transactions of the Institute of Measurement and Control 41, no. 9 (January 21, 2019): 2563–81. http://dx.doi.org/10.1177/0142331218804309.

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In this article, a novel fuzzy proportional integral derivative (PID) controller with filtered derivative action and fractional order integrator (fuzzy PIλDF controller) is proposed to solve automatic generation control (AGC) problem in power system. The optimization task for fine-tuning parameters of the proposed controller structure is accomplished by cuckoo optimization algorithm (COA). To appraise the usefulness and practicability of proposed COA optimized fuzzy PIλDF controller, four extensively used interconnected test systems, that is, two-area non-reheat thermal, two-area multi-source, three-area thermal and three-area hydro-thermal power plants, are considered. Different nonlinearity such as generation rate constraint (GRC) and governor dead band (GDB) as a source of physical constraints are taken into account in the model of the three-area power systems to examine the ability of the proposed technique to handle practical challenges. The acceptability and novelty of COA-based fuzzy PIλDF controller to solve aforesaid test systems are evaluated in comparison with some recently reported approaches. The consequences of time domain simulation reveal that designed secondary controllers provide a desirable level of performance and stability compared with other existing strategies. Additionally, to explore the robustness of the proposed technique, sensitivity analysis is conducted by varying the operating loading conditions and system parameters within a specific tolerable range.

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Huang, Song Wei, Ge Peng, and Li Fang He. "Automatic Control of Pulp pH Value." Applied Mechanics and Materials 644-650 (September 2014): 684–88. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.684.

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Control of pulp pH value is the key step in the process of washing zinc oxide. Due to the characteristics of large time delay and strong nonlinear, the control of pulp pH value is very difficult. In this paper, aiming at the above problem and according to the features of washing operation of zinc oxide powder, design a control system of a fuzzy controller, secondary fuzzy controller and PI controller.

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Abdullah, Muhammad Amirul, Salmiah Ahmad, and Siti Fauziah Toha. "Design and Development of a Solar Based Air Conditioning Blower System for Vehicle." Advanced Materials Research 1115 (July 2015): 446–49. http://dx.doi.org/10.4028/www.scientific.net/amr.1115.446.

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A solar based air conditioning blower model of vehicle has been presented together with the development of its intelligent controller in this report. The model is dependent on its own battery that is recharged by the energy collected from solar panel system. An automatic variable speed blower is introduced into the system. Vehicle cabin temperature is controlled by varying the blower speed. The intelligent controller is embedded with fuzzy logic strategy. A control algorithm is obtained with a design of the model and the system. The results satisfied the project objectives. The system is able to run using a lead acid battery and a solar panel with power consumption of 98.4 W. The fuzzy logic controller performed well with a percentage error less than 1.64%.

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Krzysztofik, Izabela. "Fuzzy Controller for Controlling the Observation and Tracking Head." Applied Mechanics and Materials 817 (January 2016): 140–49. http://dx.doi.org/10.4028/www.scientific.net/amm.817.140.

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The Observation and Tracking Head is used for automatic searching, observing and tracking targets that are to be destroyed. The paper presents the designed fuzzy controller used to control the location of head axis. The dynamics of the controlled head during the search and tracking of the target was analysed. The results of numerical research are presented in a graphical form.

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Arulmozhi, N. "Bioreactor Control Using Fuzzy Logic Controllers." Applied Mechanics and Materials 573 (June 2014): 291–96. http://dx.doi.org/10.4028/www.scientific.net/amm.573.291.

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Bioreactors are characterized by high nonlinearities and are often subjected to parameter uncertainties and disturbances. The control of such processes is often difficult to achieve with traditional linear control techniques. In the present work, a Fuzzy logic controller is designed in two versions to a Bioreactor which exhibits input multiplicities in dilution rate on productivity. Fuzzy controller and Fuzzy tuned PI controller is designed to translate the information obtained from the operator’s experiences for designing an automatic control system The Performance of proposed Fuzzy logic controller versions and conventional PI controller have been analyzed and evaluated. The two Fuzzy controller versions provide stable and faster responses than conventional PI controller. Thus, Fuzzy control is found to overcome the control problems of PI controller due to the input multiplicities near optimal productivity. It is interesting to note that the present fuzzy logic controller is giving superior performance. The process is tested with the MATLAB/SIMULINK and Fuzzy Logic Toolbox. The simulation results were presented which illustrate the validity of the method.

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Journal articles on the topic 'Automated fuzzy controllers'

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