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| RuliangWANG,KunboMEI,ChaoyangCHEN,YanboLI,HeboMEI,ZhifangYU |
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| (Computer and Information Engineering College, Guangxi Teachers Education University;School of Mathematical Sciences, Guangxi Teachers Education University;Department of Control Science and Engineering, Huazhong University of Science and Technology;Information Engineering College, Capital Normal University;School of Education Sciences, Guangxi Teachers Education University) |
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| Received:November 15, 2010Revised:May 12, 2011 |
| 基金项目:This work was partly supported by the National Natural Science Foundation of China (Nos. 60864001, 61074124). |
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| Adaptive neural control for MIMO nonlinear systems with state time-varying delay |
| Ruliang WANG,Kunbo MEI,Chaoyang CHEN,Yanbo LI,Hebo MEI,Zhifang YU |
| (Computer and Information Engineering College, Guangxi Teachers Education University;School of Mathematical Sciences, Guangxi Teachers Education University;Department of Control Science and Engineering, Huazhong University of Science and Technology;Information Engineering College, Capital Normal University;School of Education Sciences, Guangxi Teachers Education University) |
| Abstract: |
| 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 networks (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. |
| Key words: Adaptive control Backstepping technique Time-varying delay MIMO Neural network |
