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Journal articles on the topic 'Model based adaptive controller'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Hassan, Aliaa Adnan Flaih, Ekhlas Hameed Karam, and Muaayed F. Al-Rawi. "Model based adaptive controller with grasshopper optimization algorithm for upper-limb rehabilitation robot." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 723–31. https://doi.org/10.11591/ijeecs.v26.i2.pp723-731.

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Model based adaptive controllers (MBACs) are considered one of the most common adaptive controllers that are used with robotic systems due to their ensuring nonlinear robust scheme with global asymptotic stability for controlling nonlinear systems. However, this controller requires precise mathematical models of the controlled systems. In this paper, an optimal model-based adaptive controller (OMBAC) is suggested for controlling a two-link upper limb rehabilitation robot. This controller, in the presence of model uncertainties, can guarantee the robustness of the rehabilitation robot. Although

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Adnan, Aliaa, Ekhlas H. Karam, and Muaayed F. Al-Rawi. "Model based adaptive controller with grasshopper optimization algorithm for upper-limb rehabilitation robot." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 723. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp723-731.

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<span>Model based adaptive controllers (MBACs) are considered one of the most common adaptive controllers that are used with robotic systems due to their ensuring nonlinear robust scheme with global asymptotic stability for controlling nonlinear systems. However, this controller requires precise mathematical models of the controlled systems. In this paper, an optimal model-based adaptive controller (OMBAC) is suggested for controlling a two-link upper limb rehabilitation robot. This controller, in the presence of model uncertainties, can guarantee the robustness of the rehabilitation rob

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3

Yang, Xinhao, and Ze Li. "Congestion Control Based on Multiple Model Adaptive Control." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/714320.

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The congestion controller based on the multiple model adaptive control is designed for the network congestion in TCP/AQM network. As the conventional congestion control is sensitive to the variable network condition, the adaptive control method is adopted in our congestion control. The multiple model adaptive control is introduced in this paper based on the weight calculation instead of the parameter estimation in past adaptive control. The model set is composed by the dynamic model based on the fluid flow. And three “local” congestion controllers are nonlinear output feedback controller based

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Zuo, Yuefei, Shushu Zhu, Yebing Cui, Chuang Liu, and Xiaogang Lin. "Adaptive PI Controller for Speed Control of Electric Drives Based on Model Reference Adaptive Identification." Electronics 13, no. 6 (2024): 1067. http://dx.doi.org/10.3390/electronics13061067.

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In this paper, to achieve auto-setting of PI controller gains when mechanical parameters are unknown, two adaptive PI controllers for speed control of electric drives are developed based on model reference adaptive identification. The adaptive linear neuron (ADALINE) neural network is used to interpret the proposed adaptive PI controller. The effect of the low-pass filter used for the feedback speed and the Coulomb friction torque on parameter identification is analysed, and a new motion equation using filtered speed is given. Additionally, a parameter identification method based on unipolar s

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LEE, T. H., J. X. XU, and M. WANG. "A model-based adaptive sliding controller." International Journal of Systems Science 27, no. 1 (1996): 129–40. http://dx.doi.org/10.1080/00207729608929195.

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Mirwald, Jonas, Johannes Ultsch, Ricardo de Castro, and Jonathan Brembeck. "Learning-Based Cooperative Adaptive Cruise Control." Actuators 10, no. 11 (2021): 286. http://dx.doi.org/10.3390/act10110286.

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Traffic congestion and the occurrence of traffic accidents are problems that can be mitigated by applying cooperative adaptive cruise control (CACC). In this work, we used deep reinforcement learning for CACC and assessed its potential to outperform model-based methods. The trade-off between distance-error minimization and energy consumption minimization whilst still ensuring operational safety was investigated. Alongside a string stability condition, robustness against burst errors in communication also was incorporated, and the effect of preview information was assessed. The controllers were

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Li, Xian-Sheng, Yuan-Yuan Ren, and Xue-Lian Zheng. "Model-Free Adaptive Control for Tank Truck Rollover Stabilization." Mathematical Problems in Engineering 2021 (August 20, 2021): 1–16. http://dx.doi.org/10.1155/2021/8417071.

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Influenced by lateral liquid sloshing in partially filled tanks, tank vehicles are apt to encounter with rollover accidents. Due to its strong nonlinearity and loading state uncertainty, it has great challenges in tank vehicle active control. Based on the model-free adaptive control (MFAC) theory, the roll stability control problem of tank trucks with different tank shapes and liquid fill percentages is explored. First, tank trucks equipped with cylinder or elliptical cylinder tanks are modelled, and vehicle dynamics is analyzed. This dynamic model is used to provide I/O data in the controlled

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8

Chou, Sidney. "Controller Tuning Based on Stochastic Control Theory." Journal of Dynamic Systems, Measurement, and Control 110, no. 1 (1988): 100–104. http://dx.doi.org/10.1115/1.3152638.

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A practical controller tuning method is proposed for selecting controller gains in the face of design difficulties such as poor repeatability, long delay, nonlinearity, conflicting control objectives, model inaccuracy, and system complexity. Unlike many adaptive schemes striving to acquire knowledge about the system being controlled, the proposed approach is aimed at designing nonadaptive, or at best, gain scheduling controllers in a quantitative, systematic way while meeting design specifications.

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Humaidi, Amjad, and Mustafa Hameed. "Development of a New Adaptive Backstepping Control Design for a Non-Strict and Under-Actuated System Based on a PSO Tuner." Information 10, no. 2 (2019): 38. http://dx.doi.org/10.3390/info10020038.

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In this work, a new adaptive block-backstepping control design algorithm was developed for an under-actuated model (represented by a ball–arc system) to enhance the transient and steady-state behaviors and to improve the robustness characteristics of the controlled system against parameter variation (load change and model uncertainty). For this system, the main mission of the proposed controller is to simultaneously hold the ball at the top of the arc and retain the cart at the required position. The stability of a controlled system based on the proposed adaptive controller was analyzed, and i

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Kasparian, Vicken, and Celal Batur. "Model reference based neural network adaptive controller." ISA Transactions 37, no. 1 (1998): 21–39. http://dx.doi.org/10.1016/s0019-0578(98)00002-0.

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11

Li, Yu Qing, and Chong Lei. "Adaptive Fuzzy Controller Design Based on Aircraft Model." Applied Mechanics and Materials 556-562 (May 2014): 2470–73. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2470.

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According to the attitude control equation and the equations of fluid mechanics, build dynamic model .The system simulation method and the MATLAB software were used to study on the aircraft model for several different controller system, which including PID controller, T-S fuzzy controller, The adaptive fuzzy controller design. Analysis of interference signals on the performance of the aircraft control system, consider the overshoot, steady-state error, resistance to load disturbance and parameter changes adaptability, robustness. Finally, It is concluded that different controller of vehicle pe

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Kadjoudj, Mohamed, Noureddine Golea, and Hachemi Benbouzid. "Fuzzy rule: Based model reference adaptive control for PMSM drives." Serbian Journal of Electrical Engineering 4, no. 1 (2007): 13–22. http://dx.doi.org/10.2298/sjee0701013k.

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The objective of the model reference adaptive fuzzy control (MRAFC) is to change the rules definition in the direct fuzzy logic controller (FLC) and rule base table according to the comparison between the reference model output signal and system output. The MRAFC is composed by the fuzzy inverse model and a knowledge base modifier. Because of its improved algorithm, the MRAFC has fast learning features and good tracking characteristics even under severe variations of system parameters. The learning mechanism observes the plant outputs and adjusts the rules in a direct fuzzy controller, so that

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G.S.S.S.S.V., Krishna Mohan. "Auto-tuning Smith-predictive Control of Delayed Processes based on Model Reference Adaptive Controller." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 1224–30. http://dx.doi.org/10.5373/jardcs/v12sp4/20201597.

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Al-Dujaili, Ayad Q., Amjad J. Humaidi, Ziyad T. Allawi, and Musaab E. Sadiq. "Earthquake Hazard Mitigation for Uncertain Building Systems Based on Adaptive Synergetic Control." Applied System Innovation 6, no. 2 (2023): 34. http://dx.doi.org/10.3390/asi6020034.

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This study presents an adaptive control scheme based on synergetic control theory for suppressing the vibration of building structures due to earthquake. The control key for the proposed controller is based on a magneto-rheological (MR) damper, which supports the building. According to Lyapunov-based stability analysis, an adaptive synergetic control (ASC) strategy was established under variation of the stiffness and viscosity coefficients in the vibrated building. The control and adaptive laws of the ASC were developed to ensure the stability of the controlled structure. The proposed controll

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Lin, Fen, Yuke Chen, Youqun Zhao, and Shaobo Wang. "Path tracking of autonomous vehicle based on adaptive model predictive control." International Journal of Advanced Robotic Systems 16, no. 5 (2019): 172988141988008. http://dx.doi.org/10.1177/1729881419880089.

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In most cases, a vehicle works in a complex environment, with working conditions changing frequently. For most model predictive tracking controllers, however, the impacts of some important working conditions, such as speed and road conditions, are not concerned. In this regard, an adaptive model predictive controller is proposed, which improves tracking accuracy and stability compared with general model predictive controllers. First, the proposed controller utilizes the recursive least square algorithm to estimate tire cornering stiffness and road friction coefficient online. Then, the estimat

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Zhong, Bin, and Xiao Qing Zhao. "Adaptive Fuzzy Sliding-Mode Control Based on Generalized T-S Fuzzy Model." Advanced Materials Research 433-440 (January 2012): 7387–93. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.7387.

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In order to obtain the controlled object’s better tracking performance within the reference signals’ boundary region, an adaptive fuzzy sliding-mode controller is designed aiming at the uncertain second-order nonlinear system. After taking account of the control sensitivity’s requirement for the membership function, the controller capitalizes on the generalized membership function’s adaptability of the generalized T-S fuzzy model without the supervisory control and boundary control that may compensate the system’s modeling error. So, the generalized T-S fuzzy logic system can better approximat

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Baz, Rachida, Khalid El Majdoub, Fouad Giri, and Ossama Ammari. "Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 2 (2024): 745. http://dx.doi.org/10.11591/ijeecs.v34.i2.pp745-755.

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Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was base

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Baz, Rachida, Khalid El Majdoub, Fouad Giri, and Ossama Ammari. "Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 2 (2024): 745–55. https://doi.org/10.11591/ijeecs.v34.i2.pp745-755.

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Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was base

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19

Raghuraman, R. "PSO Based Model Reference Adaptive PI Controller for a Conical Tank Level Process." Asian Journal of Electrical Sciences 8, no. 2 (2019): 29–33. http://dx.doi.org/10.51983/ajes-2019.8.2.2362.

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Conical tanks are mostly used in various process industries, such as metallurgical industries, food processing industries, concrete mixing industries wastewater treatment industries etc. A conical tank is basically a nonlinear process as its area of cross section varies with respect to level. This paper describes the implementation of PSO based Model Reference Adaptive PI controller for a nonlinear Conical Tank Level System (CTLS). The mathematical model of CTLS is developed and PSO based Model Reference Adaptive (MRA) PI Controller is proposed for this level system. A result of proposed contr

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WANG, Hui, and Wei Cun Zhang. "Weighted Multiple Model Adaptive Control Based on ADRC." Applied Mechanics and Materials 313-314 (March 2013): 412–17. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.412.

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Weighted multiple model adaptive control is a combination of off-line design and on-line decision, which combines a finite number of simple controllers by weighting algorithm. Its an effective means to solve the control problems of complex and uncertain systems. Active Disturbance Rejection Control (ADRC) is a new digital control technology with high accuracy and strong robustness. This paper introduces ADRC as local controller in the weighted multiple model adaptive control system. The simulation results show that the proposed system has strong robustness in a wide range.

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Nguyen Viet Phuong and Nguyen Duy Khanh. "Synthesis of adaptive control algorithms based on output feedback with an implicit reference model for application on aerial vehicles." Journal of Military Science and Technology 102 (April 15, 2025): 23–30. https://doi.org/10.54939/1859-1043.j.mst.102.2025.23-30.

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This paper addresses adaptive control for an aircraft's longitudinal motion under parameter uncertainty. A mathematical model is developed, and simulations validate the proposed approach. A linear modal controller is designed for nominal parameters, while an adaptive controller with an implicit reference model ensures stability under uncertainties. MATLAB/Simulink simulations on the UAV-70V model show that the linear modal controller performs well with known parameters but becomes unstable with variations. In contrast, the adaptive controller maintains robust stability, rapid response, and pre

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22

Toroman, Amel, and Samir Vojić. "Adaptive car control system based on a predictive model." IOP Conference Series: Materials Science and Engineering 1208, no. 1 (2021): 012040. http://dx.doi.org/10.1088/1757-899x/1208/1/012040.

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Abstract An adaptive control is a control, which by pre-setting the parameters of the controller, enables the control of processes whose parameters are time-varying or are initially uncertain. The possibilities and benefits of adaptive control are versatile and can be best demonstrated by applying the system while driving a car, or maintaining the optimal speed and distance between cars, which is shown in this paper. As the car’s weight decreases while driving due to fuel consumption, the control algorithm has to be adapted to the changed driving conditions. Accordingly, an adaptive control sy

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23

Zhang, Xuelin, Xiaobin Xu, Xiaojian Xu, Pingzhi Hou, Haibo Gao, and Feng Ma. "Intelligent Adaptive PID Control for the Shaft Speed of a Marine Electric Propulsion System Based on the Evidential Reasoning Rule." Mathematics 11, no. 5 (2023): 1145. http://dx.doi.org/10.3390/math11051145.

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To precisely and timely control the shaft speed for a marine electric propulsion system under normal sea conditions, a new shaft speed control technique combining the evidential reasoning rule with the traditional PID controller was proposed in this study. First, an intelligent adaptive PID controller based on the evidential reasoning rule was designed for a marine electric propulsion system to obtain the PID parameters KP, KI, and KD. Then, a local iterative optimization strategy for model parameters was proposed. Furthermore, the parameters of the adaptive PID controller model were optimized

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Sokolov, Vladimir, Oleg Krol, Vladislav Andriichuk, Irina Chernikova, and Tatiana Shevtsova. "Improvement of HVAC systems based on adaptive predictive control." E3S Web of Conferences 420 (2023): 07020. http://dx.doi.org/10.1051/e3sconf/202342007020.

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The paper considers the issue of approbation of adaptive predictive control for heating, ventilation and air conditioning systems, shows the possibility of improving the regulation processes by its application on example of ventilation system. The idea of control using predictive model is presented, the principles of control using MPC controller are noted, the controller structure and the criterion for choosing the optimal values of control signal are considered. The feature of adaptive predictive control is the presence of the mathematical model for control object, which accurately describes

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Lian, Xiaobin, Jiafu Liu, Chuang Wang, Tiger Yuan, and Naigang Cui. "RBF network based adaptive sliding mode control for solar sails." Aircraft Engineering and Aerospace Technology 90, no. 8 (2018): 1180–91. http://dx.doi.org/10.1108/aeat-04-2017-0112.

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Purpose The purpose of this paper is to resolve complex nonlinear dynamical problems of the pitching axis of solar sail in body coordinate system compared with inertial coordinate system. And saturation condition of controlled torque of vane in the orbit with big eccentricity ration, uncertainty and external disturbance under complex space background are considered. Design/methodology/approach The pitch dynamics of the sailcraft in the prescribed elliptic earth orbits is established considering the torques by the control vanes, gravity gradient and offset between the center-of-mass (cm) and ce

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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-l

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Ma, Mark S. "Simulation of an expert model-based adaptive controller." ACM SIGSIM Simulation Digest 20, no. 4 (1990): 81–87. http://dx.doi.org/10.1145/99637.99646.

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Pal, A. K., Indrajıt Naskar, and Sampa Paul. "Fuzzy-based Gain Adaptive Scheme for Set-Point Modulated Model Reference Adaptive Controller." International Journal of Natural Computing Research 7, no. 4 (2018): 1–19. http://dx.doi.org/10.4018/ijncr.2018100101.

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In model reference adaptive controller (MRACs), the adaptive gain of the controller is varied according to the process dynamic variation as it is directly related with the system stability. In MRAC, there is no provision of an automatic selection of adaptive gain and adaptation rate. To get rid of this problem and for the automatic selection of adaptive gain, a fuzzy-based scheme is presented in this article. In the proposed fuzzy-based technique, the controller output gain is illustrated as the function of input process parameters, which is continuously amended for any process parameter varia

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Halima, Ikaouassen, Raddaoui Abderraouf, Rezkallah Miloud, and Ibrahim Hussein. "Improved predictive current model control based on adaptive PR controller for standalone system based DG set." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 1905–14. https://doi.org/10.11591/ijece.v10i2.pp1905-1914.

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This paper investigates an improved current predictive model control (PCMC) strategy with a prediction horizon of one sampling time for voltage regulation in standalone system based on diesel engine driven fixed speed synchronous generator. An adaptive PR controller with anti-windup scheme is employed to achieve high performance regulation without saturation issues. In addition, new method to obtain the optimal parameters of the adaptive PR controller to achieve high performance during the transition and in steady state, is provided. Furthermore, to balance the power at the point of common cou

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Xie, Ye Hai. "Adaptive Controller for ROV Based on Hybrid Theory." Advanced Materials Research 989-994 (July 2014): 2965–69. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2965.

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In order to deal with the model uncertainties of ROV (remotely operated vehicles) caused by current disturbances, umbilical cable and robotic manipulators, an adaptive backstepping controller and an adaptive PD controller are applied to control the ROV. The adaptive backstepping controller is used to control the horizontal motion, and the adaptive PD controller is applied to control the Vertical motion. Finally, the method is verified effective by the computer simulation.

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Mohd, Ashraf Ahmad, Ishak Haszuraidah, Nor Kasruddin Nasir Ahmad, and Abd Ghani Normaniha. "Data-based PID control of flexible joint robot using adaptive safe experimentation dynamics algorithm." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 79–85. https://doi.org/10.11591/eei.v10i1.2472.

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This paper proposes the data-based PID controller of flexible joint robot based on adaptive safe experimentation dynamics (ASED) algorithm. The ASED algorithm is an enhanced version of SED algorithm where the updated tuning variable is modified to adapt to the change of the objective function. By adopting the adaptive term to the updated equation of SED, it is expected that the convergence accuracy can be further improved. The effectiveness of the ASED algorithm is verified to tune the PID controller of flexible joint robot. In this flexible joint control problem, two PID controllers are utili

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Henmi, Tomohiro. "Control Parameters Tuning Method of Nonlinear Model Predictive Controller Based on Quantitatively Analyzing." Journal of Robotics and Mechatronics 28, no. 5 (2016): 695–701. http://dx.doi.org/10.20965/jrm.2016.p0695.

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[abstFig src='/00280005/11.jpg' width='300' text='ANMPC controller' ] The parameter-tuning method we discuss is for an Adaptive Nonlinear Model Predictive Controller (ANMPC). The MPC is optimization-based controller and decides control input to realize system output that tracks a reference trajectory through “optimal computation.” The reference trajectory is ideal trajectory of system output to converge on a desired value, i.e. controlled system performance depends on the reference trajectory. As a MPC controller which applies to the nonlinear systems, our group has already proposed an adaptiv

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Al-Darraji, Izzat, Dimitrios Piromalis, Ayad Kakei, et al. "Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model." Electronics 10, no. 7 (2021): 831. http://dx.doi.org/10.3390/electronics10070831.

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Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural

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Piñón, Alejandro, Antonio Favela-Contreras, Francisco Beltran-Carbajal, Camilo Lozoya, and Graciano Dieck-Assad. "Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model." Actuators 11, no. 1 (2022): 21. http://dx.doi.org/10.3390/act11010021.

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Many industrial processes include MIMO (multiple-input, multiple-output) systems that are difficult to control by standard commercial controllers. This paper describes a MIMO case of a class of SISO-APC (single-input, single-output adaptive predictive controller) based upon an ARX (autoregressive with exogenous variable) model. This class of SISO-APC based on ARX models has been successfully and extensively used in many industrial applications. This approach aims to minimize the barriers between the theory of predictive adaptive control and its application in the industrial environment. The pr

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Truong, Hoa Thi, Xuan Bao Nguyen, and Cuong Mai Bui. "Singularity-Free Adaptive Controller for Uncertain Hysteresis Suspension Using Magnetorheological Elastomer-Based Absorber." Shock and Vibration 2022 (January 11, 2022): 1–17. http://dx.doi.org/10.1155/2022/2007022.

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The magnetorheological elastomer (MRE) is a smart material widely used in recent vibration systems. A system using these materials often faces difficulties designing the controller such as unknown parameters, hysteresis state, and input constraints. First, a model is designed for the MRE-based absorber to portray the behavior of MRE and predict the appropriate electric current supplied. The conventional adaptive controller often suffers from so-called control singularities. The singularity-free adaptive controller is proposed to eliminate the singularity with parametric uncertainty. The propos

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Abuhussain, Mohammed Awad, Badr Saad Alotaibi, Muhammad Saidu Aliero, Muhammad Asif, Mohammad Abdullah Alshenaifi, and Yakubu Aminu Dodo. "Adaptive HVAC System Based on Fuzzy Controller Approach." Applied Sciences 13, no. 20 (2023): 11354. http://dx.doi.org/10.3390/app132011354.

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Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort

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Ning, Xizhan, Wei Huang, Guoshan Xu, et al. "A Novel Model-Based Adaptive Feedforward-Feedback Control Method for Real-Time Hybrid Simulation considering Additive Error Model." Structural Control and Health Monitoring 2023 (December 6, 2023): 1–25. http://dx.doi.org/10.1155/2023/5550580.

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Adaptive control methods have been widely adopted to handle the variable time delay in real-time hybrid simulation (RTHS). Nevertheless, the initial parameter settings in adaptive control law, the parameter estimation method, and the testing system nonlinearity will affect RTHS’s accuracy and stability at different levels. To this end, this study proposes a novel model-based adaptive feedforward-feedback control method that considers an additive error model. In the proposed method, the time delay and amplitude discrepancy are roughly compensated by a feedforward controller and then finely redu

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Gao, Qin He, and Wen Liang Guan. "Predictive Controller Design and Simulation of Time-Varying System Based on IGPC." Advanced Materials Research 291-294 (July 2011): 2647–51. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.2647.

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An adaptive predictive controller is proposed to solve the time-varying characteristics of the industrial process control system. The arithmetic of implicit expression generalized predictive control（IGPC） is put forward to compute the optimal control signal increment. In order to decrease the computing work and increase the computing speed, the system input/output data are used to identify the controller parameters directly and the plant model parameters are unnecessary. Simulation results show that the controller can track the change of setting value excellently even though without any prior

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Zhou, Pei, Renming Yang, Guangyuan Zhang, and Yaozhen Han. "Adaptive Robust Simultaneous Stabilization of Two Dynamic Positioning Vessels Based on a Port-Controlled Hamiltonian (PCH) Model." Energies 12, no. 20 (2019): 3936. http://dx.doi.org/10.3390/en12203936.

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In this paper, the adaptive robust simultaneous stabilization problem of two ships is studied. Firstly, the water surface three-degree-of-freedom ship models are transformed into port-controlled Hamiltonian (PCH) models. Using a single output feedback controller, the two PCH systems are combined to generate an enhanced PCH system based on Hamiltonian structural attributes. Then, considering the situation with both external interference and structural parameter perturbation in the systems, an adaptive robust output feedback controller is designed to stabilize the two systems simultaneously. Fin

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Li, Wei. "Neuron Network Model-Based Control System Model." Applied Mechanics and Materials 339 (July 2013): 143–46. http://dx.doi.org/10.4028/www.scientific.net/amm.339.143.

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When the control object complicate conventional PID control accuracy will be significantly reduced. In recent years, with the gradual improvement of the people of artificial intelligence theory, analog neural networks has been rapid development, the emergence of a large number of excellent algorithm and the means of achieving, from single neuron PID algorithm and with gain control neuron system PID algorithm, two aspects discusses the process of adaptive neuron PID algorithm to achieve accuracy improved adaptive neuron system controller PID algorithm based on this analysis.

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Karthikeyan, R., K. Manickavasagam, Shikha Tripathi, and K. V. V. Murthy. "Neuro-Fuzzy-Based Control for Parallel Cascade Control." Chemical Product and Process Modeling 8, no. 1 (2013): 15–25. http://dx.doi.org/10.1515/cppm-2013-0002.

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Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptatio

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Zhao, Li, Jing Wang, and Weicun Zhang. "Smooth Adaptive Internal Model Control Based on U Model for Nonlinear Systems with Dynamic Uncertainties." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/2926914.

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An improved smooth adaptive internal model control based on U model control method is presented to simplify modeling structure and parameter identification for a class of uncertain dynamic systems with unknown model parameters and bounded external disturbances. Differing from traditional adaptive methods, the proposed controller can simplify the identification of time-varying parameters in presence of bounded external disturbances. Combining the small gain theorem and the virtual equivalent system theory, learning rate of smooth adaptive internal model controller has been analyzed and the clos

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Tsay, Tain-Sou. "Model Based Adaptive Piecewise Linear Controller for Complicated Control Systems." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/120419.

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A model based adaptive piecewise linear control scheme for industry processes with specifications on peak overshoots and rise times is proposed. It is a gain stabilized control technique. Large gain is used for large tracking error to get fast response. Small gain is used between large and small tracking error for good performance. Large gain is used again for small tracking error to cope with large disturbance. Parameters of the three-segment piecewise linear controller are found by an automatic regulating time series which is function of output characteristics of the plant and reference mode

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Liu, Jing, and Guo Xin Wang. "The Optimization of Adaptive PID Control Algorithm Based on RBF Neural Network." Advanced Materials Research 998-999 (July 2014): 943–46. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.943.

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As the earliest practical controller, PID controller has more than 50 years of history, and it is still the most widely used and most common industrial controllers. PID controller is simple to understand and use, without a prerequisite for an accurate model of the physical system, thus become the most popular, the most common controller. The reason why PID controller is the first developed one is that its simple algorithm, robustness and high reliability. It is widely used in process control and motion control, especially for accurate mathematical model that can be established deterministic co

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Zhao, Guoliang, Kaibiao Sun, and Hongxing Li. "Tensor Product Model Transformation Based Adaptive Integral-Sliding Mode Controller: Equivalent Control Method." Scientific World Journal 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/726963.

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This paper proposes new methodologies for the design of adaptive integral-sliding mode control. A tensor product model transformation based adaptive integral-sliding mode control law with respect to uncertainties and perturbations is studied, while upper bounds on the perturbations and uncertainties are assumed to be unknown. The advantage of proposed controllers consists in having a dynamical adaptive control gain to establish a sliding mode right at the beginning of the process. Gain dynamics ensure a reasonable adaptive gain with respect to the uncertainties. Finally, efficacy of the propos

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Colbaugh, R., and K. Glass. "Decentralized adaptive compliance control of robot manipulators." Robotica 13, no. 5 (1995): 485–98. http://dx.doi.org/10.1017/s0263574700018324.

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SummaryThis paper presents two adaptive schemes for controlling the end-effector compliance of robot manipulators. Each controller possesses a decentralized structure, in which the control input for each configuration degree-offreedom (DOF) is computed based on information concerning only that DOF. The first scheme is developed using an adaptive impedance control approach and consists of two subsystems: a simple “filter” which modifies the end-effector position trajectory based on the sensed contact force and the desired dynamic relationship between the position and force, and an adaptive cont

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Zhang, Jianhua, Yang Li, Wenbo Fei, and Xueli Wu. "U-Model Based Adaptive Neural Networks Fixed-Time Backstepping Control for Uncertain Nonlinear System." Mathematical Problems in Engineering 2020 (March 23, 2020): 1–7. http://dx.doi.org/10.1155/2020/8302627.

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Under U-model control design framework, a fixed-time neural networks adaptive backstepping control is proposed. The majority of the previously described adaptive neural controllers were based on uniformly ultimately bounded (UUB) or practical finite stable (PFS) theory. For neural networks control, it makes the control law as well as stability analysis highly lengthy and complicated because of the unknown ideal weight and unknown approximation error. Moreover, there has been very limited research focus on adaptive law for neural networks adaptive control in finite time. Based on fixed-time sta

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Ajel, Ahmed R., Amjad J. Humaidi, Ibraheem Kasim Ibraheem, and Ahmad Taher Azar. "Robust Model Reference Adaptive Control for Tail-Sitter VTOL Aircraft." Actuators 10, no. 7 (2021): 162. http://dx.doi.org/10.3390/act10070162.

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This study presents a control design of roll motion for a vertical take-off and landing unmanned air vehicle (VTOL-UAV) design based on the Model Reference Adaptive Control (MRAC) scheme in the hovering flight phase. The adaptive laws are developed for the UAV system under nonparametric uncertainty (gust and wind disturbance). Lyapunov-based stability analysis of the adaptive controlled UAV system under roll motion has been conducted and the adaptive laws have been accordingly developed. The Uniform Ultimate Boundness (UUB) of tracking error has been proven and the stability analysis showed th

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Ikaouassen, Halima, Abderraouf Raddaoui, Miloud Rezkallah, and Hussein Ibrahim. "Improved predictive current model control based on adaptive PR controller for standalone system based DG set." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 1905. http://dx.doi.org/10.11591/ijece.v10i2.pp1905-1914.

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This paper investigates an improved current predictive model control (PCMC) strategy with a prediction horizon of one sampling time for voltage regulation in standalone system based on diesel engine driven fixed speed of a synchronous generator. An adaptive PR controller with anti-windup scheme is employed to achieve high performance regulation without saturation issues. In addition, new method to obtain the optimal parameters of the adaptive PR controller to achieve high performance during the transition and in steady state is provided. To balance the power at the point of common coupling (PC

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Sanae, El Bouassi, El Afou Youssef, Chalh Zakaria, Mehdi Mellouli El, and Haidi Touria. "Optimized triangular observer based adaptive supertwisting sliding mode control for wind turbine system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4229–40. https://doi.org/10.11591/ijai.v13.i4.pp4229-4240.

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This paper presents a modified adaptive supertwisting sliding mode controller (AST-SMC) that dynamically adjusts control settings without prior knowledge of uncertainty limits, thereby removing chattering and putting reliability first while maintaining the original benefits of sliding mode control (SMC). First, we model and build the wind turbine system using three different controllers: the AST-SMC, the supertwisting sliding mode controller (ST-SMC), and the first-order sliding mode controller (FOSMC). A second comparison is necessary. Only the rotor speed is available to the control law beca

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