Explicit Construction of H Control Law for a Class of Nonminimum Phase Nonlinear Systems

2007 ◽  
Author(s):  
Weiyao Lan ◽  
Ben M. Chen
Keyword(s):  
Nonlinear Systems ◽  
Explicit Construction ◽  
Control Law ◽  
Nonminimum Phase

scholarly journals Adaptive MIMO Supervisory Control Design Using Modeling Error

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Zhi-Ren Tsai ◽  
Yau-Zen Chang
Keyword(s):  
Nonlinear Systems ◽  
Supervisory Control ◽  
Fuzzy Model ◽  
Control Law ◽  
Modeling Error ◽  
Nonminimum Phase ◽  
Phase Dynamics ◽  
Control Scheme ◽  
The Face ◽  
Aircraft System

This paper proposes an adaptive control scheme for nonlinear systems with significant nonminimum phase dynamics. The scheme is composed of an inner-level adaptive fuzzy PD control law and an outer-level supervisory control law. Importantly, the inner-level controller of the two-level scheme is designed based on a fuzzy model, which takes nonminimum phase phenomenon and modeling error explicitly into account. The scheme is both much simpler in design and more applicable to general nonlinear systems when compared with most existing nonlinear controllers. Effectiveness of the proposed control strategy is demonstrated by numerical simulation of the control of a five-degree-of-freedom aircraft system in the face of bursting disturbances.

scholarly journals Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

2021 ◽  
Vol 11 (5) ◽  
pp. 2312
Author(s):  
Dengguo Xu ◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Policy Iteration ◽  
Control Law ◽  
Globally Asymptotically Stable ◽  
Control Approach ◽  
Input Uncertainties ◽  
Theoretical Results

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.

A Design Method of LS-SVM Based Stable Adaptive Controller for a Class of Nonlinear Systems

2011 ◽  
Vol 48-49 ◽  
pp. 17-20
Author(s):  
Chun Li Xie ◽  
Tao Zhang ◽  
Dan Dan Zhao ◽  
Cheng Shao
Keyword(s):  
Nonlinear Systems ◽  
Design Method ◽  
Nonlinear Function ◽  
Lyapunov Stability Theory ◽  
Adaptive Controller ◽  
Control Law ◽  
Continuous Systems ◽  
Control Schemes ◽  
Closed Systems ◽  
Simulation Results

A design method of LS-SVM based stable adaptive controller is proposed for a class of nonlinear continuous systems with unknown nonlinear function in this paper. Due to the fact that the control law is derived based on the Lyapunov stability theory, the scheme can not only solve the tracking problem of this class of nonlinear systems, but also it can guarantee the asymptotic stability of the closed systems, which is superior to many LS-SVM based control schemes. The effectiveness of the proposed scheme is demonstrated by simulation results.

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