Iterative Data-Driven Fractional Model Reference Control of Industrial Robot for Repetitive Precise Speed Tracking

2019 ◽  
Vol 24 (3) ◽  
pp. 1041-1053 ◽  
Author(s):  
Yuanlong Xie ◽  
Xiaoqi Tang ◽  
Wei Meng ◽  
Bosheng Ye ◽  
Bao Song ◽  
...  
Keyword(s):  
Industrial Robot ◽  
Data Driven ◽  
Model Reference ◽  
Fractional Model ◽  
Speed Tracking ◽  
Model Reference Control

Data-driven model reference control design by prediction error identification

2017 ◽  
Vol 354 (6) ◽  
pp. 2628-2647 ◽  
Author(s):  
Lucíola Campestrini ◽  
Diego Eckhard ◽  
Alexandre Sanfelice Bazanella ◽  
Michel Gevers
Keyword(s):  
Prediction Error ◽  
Control Design ◽  
Data Driven ◽  
Error Identification ◽  
Model Reference ◽  
Model Reference Control

scholarly journals Proper closed-loop specifications for data-driven model-reference control

2021 ◽  
Vol 54 (9) ◽  
pp. 46-51
Author(s):  
Valentina Breschi ◽  
Simone Formentin
Keyword(s):  
Closed Loop ◽  
Data Driven ◽  
Model Reference ◽  
Model Reference Control

scholarly journals A Novel Non-integer Indirect Adaptive Control for Non-integer Order Systems with Non-prior Knowledge

2020 ◽  
Vol 10 (1) ◽  
pp. 5186-5190
Author(s):  
B. Bourouba ◽  
S. Ladaci
Keyword(s):  
Adaptive Control ◽  
Control Method ◽  
Reference Model ◽  
Model Reference ◽  
Fractional Model ◽  
Integer Order ◽  
Model Reference Control ◽  
Indirect Adaptive Control ◽  
The Stability ◽  
Model Reference Adaptive

In this study, a new non-integer indirect adaptive control method with reference model is suggested for the class of non-integer order systems. The objective of model reference control is to include the output of the given reference fractional model in tracking the output of a controlled plant by using the concept of on-line goal adaptation. The stability of the closed-loop system is analyzed via the Lyapunov method. Finally, Matlab simulation results are presented to illustrate the effectiveness of the proposed method of indirect fractional model reference adaptive control.

Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning

2018 ◽  
Vol 73 ◽  
pp. 227-238 ◽  
Author(s):  
Mircea-Bogdan Radac ◽  
Radu-Emil Precup ◽  
Raul-Cristian Roman
Keyword(s):  
Data Driven ◽  
Model Reference ◽  
Q Learning ◽  
Model Free ◽  
Model Reference Control

Optimal Model Reference Control Scheme Design for Nonlinear Strict-Feedback Systems

2020 ◽  
Vol 38 (9A) ◽  
pp. 1342-1351
Author(s):  
Musadaq A. Hadi ◽  
Hazem I. Ali
Keyword(s):  
Reference Model ◽  
Feedback System ◽  
Optimization Method ◽  
Feedback Systems ◽  
Model Reference ◽  
Scheme Design ◽  
Model Reference Control ◽  
Control Scheme ◽  
Lyapunov Stability Analysis ◽  
Strict Feedback

In this paper, a new design of the model reference control scheme is proposed in a class of nonlinear strict-feedback system. First, the system is analyzed using Lyapunov stability analysis. Next, a model reference is used to improve system performance. Then, the Integral Square Error (ISE) is considered as a cost function to drive the error between the reference model and the system to zero. After that, a powerful metaheuristic optimization method is used to optimize the parameters of the proposed controller. Finally, the results show that the proposed controller can effectively compensate for the strictly-feedback nonlinear system with more desirable performance.

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