A nonlinear switching control method for a class of non-minimum-phase nonlinear systems

2010 ◽  
Author(s):  
Yajun Zhang ◽  
Tianyou Chai ◽  
Hong Wang ◽  
Jun Fu
Keyword(s):  
Nonlinear Systems ◽  
Control Method ◽  
Switching Control ◽  
Minimum Phase ◽  
Nonlinear Switching

Neural Network Direct Adaptive Control Strategy for a Class of Switched Nonlinear Systems

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Lei Yu ◽  
Xiefu Jiang ◽  
Shumin Fei ◽  
Jun Huang ◽  
Gang Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Control Method ◽  
Average Dwell Time ◽  
Switching Control ◽  
Network Control ◽  
Switched Nonlinear Systems ◽  
Output Tracking ◽  
Direct Adaptive Control ◽  
Adaptive Control Strategy

This paper deals with the adaptive neural network (NN) switching control problem for a class of switched nonlinear systems. Radial basis function (RBF) NNs are utilized to approximate the unknown switching control law term which includes a neural network control term, a supervisory control term, and a compensation control term. Also, based on the average dwell-time, a direct adaptive neural switching controller is designed to heighten the robustness of switching system. We can prove to ensure stability of the resulting closed-loop system such that the output tracking performance can be well obtained and all the signals are kept bounded. Simulation results validate the tracking control performance and investigate the effectiveness of the proposed switching control method.

Disturbance Attenuating Adaptive Controllers for Parametric Strict Feedback Nonlinear Systems With Output Measurements

1999 ◽  
Vol 121 (1) ◽  
pp. 48-57 ◽  
Author(s):  
I. Egemen Tezcan ◽  
Tamer Bas¸ar
Keyword(s):  
Nonlinear Systems ◽  
Output Feedback ◽  
Robust Adaptive Control ◽  
Disturbance Attenuation ◽  
Parameter Estimates ◽  
Minimum Phase ◽  
Feedback Form ◽  
Output Measurement ◽  
Adaptive Controllers ◽  
Strict Feedback

We present a systematic procedure for designing H∞-optimal adaptive controllers for a class of single-input single-output parametric strict-feedback nonlinear systems that are in the output-feedback form. The uncertain nonlinear system is minimum phase with a known relative degree and known sign of the high-frequency gain. We use soft projection on the parameter estimates to keep them bounded in the absence of persistent excitations. The objective is to obtain disturbance attenuating output-feedback controllers which will track a smooth bounded trajectory and keep all closed-loop signals bounded in the presence of exogenous disturbances. Two recent papers (Pan and Bas¸ar, 1996a; Marino and Tomei, 1995) addressed a similar problem with full state information, using two different approaches, and obtained asymptotically tracking and disturbance-attenuating adaptive controllers. Here, we extend these results to the output measurement case for a class of minimum phase nonlinear systems where the nonlinearities depend only on the measured output. It is shown that arbitrarily small disturbance attenuation levels can be obtained at the expense of increased control effort. The backstepping methodology, cost-to-come function based H∞ -filtering and singular perturbations analysis constitute the framework of our robust adaptive control design scheme.

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

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