Adaptive neural control for MIMO nonlinear systems with state time-varying delay  

作　　者：Ruliang WANG Kunbo MEI Chaoyang CHEN Yanbo LI Hebo MEI Zhifang YU 

机构地区：[1]Computer and Information Engineering College, Guangxi Teachers Education University, Nanning Guangxi 530023, China [2]School of Mathematical Sciences, Guangxi Teachers Education University, Nanning Guangxi 530023, China [3]Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan Hubei 430074, China [4]Information Engineering College, Capital Normal University, Beijing 100048, China [5]School of Education Sciences, Guangxi Teachers Education University, Nanning Guangxi 530023, China

出　　处：《控制理论与应用（英文版）》2012年第3期309-318,共10页

基　　金：supported by the National Natural Science Foundation of China(Nos.60864001,61074124)

摘　　要：In this paper, adaptive neural control is proposed for a class of multi-input multi-output （MIMO） nonlinear unknown state time-varying delay systems in block-triangular control structure. Radial basis function （RBF） neural net- works （NNs） are utilized to estimate the unknown continuous functions. The unknown time-varying delays are compensated for using integral-type Lyapunov-Krasovskii functionals in the design. The main advantage of our result not only efficiently avoids the controller singularity, but also relaxes the restriction on unknown virtual control coefficients. Boundedness of all the signals in the closed-loop of MIMO nonlinear systems is achieved, while The outputs of the systems are proven to converge to a small neighborhood of the desired trajectories. The feasibility is investigated by two simulation examples.In this paper, adaptive neural control is proposed for a class of multi-input multi-output （MIMO） nonlinear unknown state time-varying delay systems in block-triangular control structure. Radial basis function （RBF） neural net- works （NNs） are utilized to estimate the unknown continuous functions. The unknown time-varying delays are compensated for using integral-type Lyapunov-Krasovskii functionals in the design. The main advantage of our result not only efficiently avoids the controller singularity, but also relaxes the restriction on unknown virtual control coefficients. Boundedness of all the signals in the closed-loop of MIMO nonlinear systems is achieved, while The outputs of the systems are proven to converge to a small neighborhood of the desired trajectories. The feasibility is investigated by two simulation examples.

关 键 词：Adaptive control Backstepping technique Time-varying delay MIMO Neural network 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TP13[自动化与计算机技术—控制科学与工程]