Abstract
This paper is concerned with the adaptive control problem for a class of strict-feedback nonlinear systems, in which unknown virtual control gain function is the main feature. Based on the neural network approximate ability and backstepping control design technique, adaptive neural network based dynamic surface control technique is developed. The advantage is that it does not require priori knowledge of virtual control gain function sign, which is usually demanded in many designs. At the same time, by dynamic surface control scheme, the explosion of computation is circumvented. The control performance of closed-loop systems is improved by adaptive modifying the estimated error upper bound. By theoretical analysis, the signals of closed-loop systems are globally ultimately bounded and the control error converges to a small residual set around the origin.
This work was supported by the National Natural Science Foundation of China (60904017, 60804006), the Fundamental Research Funds for the Central Universities (N100404025).
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Wang, Y., Hui, G., Wang, Z., Zhang, H. (2011). Neural Network-Based Dynamic Surface Control of Nonlinear Systems with Unknown Virtual Control Coefficient. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21105-8_39
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DOI: https://doi.org/10.1007/978-3-642-21105-8_39
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