Research on Model Reference Robust Control for Multivariable Linear System

2013 ◽  
Vol 712-715 ◽  
pp. 2761-2767
Author(s):  
Xu Jiang ◽  
Jing Yu Hua ◽  
Qin Ling Zhang

This paper studies the output-feedback model reference robust control for MIMOlinear systems with generalized relative degree one. A new robust controlscheme is proposed within the framework of model reference control. Under theassumption that the high-frequency gain matrix of the plant can be transformedto a glass of main diagonal dominant matrix via full rank transformation, it isshown that all signals of the closed-loop system are globally uniformly boundedand meanwhile, the tracking errors converge to a residual set that can be madearbitrarily small by properly choosing some design parameters. Simulationresults are presented to illustrate the effectiveness of the proposed scheme.

2013 ◽  
Vol 281 ◽  
pp. 121-126
Author(s):  
Xu Jiang ◽  
Zhi Tao Feng

This paper studies the output-feedback variable structure control for MIMO (multi-input multi-output) linear systems with generalized relative degree one. A new variable structure control scheme is proposed within the framework of model reference control. Under the assumption that the high frequency gain matrix of the plant can be transformed to a main diagonal dominant matrix via full rank transformation, it is shown that all signals of the closed-loop system are globally uniformly bounded and meanwhile, the tracking error converges to zero exponentially. Simulation results are presented to illustrate the effectiveness of the proposed scheme.


Automatica ◽  
2008 ◽  
Vol 44 (4) ◽  
pp. 1036-1044 ◽  
Author(s):  
Lin Yan ◽  
Liu Hsu ◽  
Ramon R. Costa ◽  
Fernando Lizarralde

2020 ◽  
Vol 38 (9A) ◽  
pp. 1342-1351
Author(s):  
Musadaq A. Hadi ◽  
Hazem I. Ali

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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