Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time

被引：155

作者：

Ge, SS ^{[1
]}

Zhang, J ^{[1
]}

Lee, TH ^{[1
]}

机构：

[1] Natl Univ Singapore, Singapore 119260, Singapore

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2004年 / 34卷 / 04期

关键词：

discrete-time systems; high-order neural networks; MIMO systems; neural networks;

D O I：

10.1109/TSMCB.2004.826827

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, adaptive neural network (NN) control is investigated for a class of multiinput and multioutput (MIMO) nonlinear systems with unknown bounded disturbances in discrete-time domain. The MIMO system under study consists of several subsystems with each subsystem in strict feedback form. The inputs of the MIMO system are in triangular form. First, through a coordinate transformation, the MIMO system is transformed into a sequential decrease cascade form (SDCF). Then, by using high-order neural networks (HONN) as emulators of the desired controls, an effective neural network control scheme with adaptation laws is developed. Through embedded backstepping, stability of the closed-loop system is proved based on Lyapunov synthesis. The output tracking errors are guaranteed to converge to a residue whose size is adjustable. Simulation results show the effectiveness of the proposed control scheme.

引用

页码：1630 / 1645

页数：16

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