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Study On Stable Adaptive Neural Networks Control Of Uncertain Nonlinear Systems

Posted on:2007-08-19Degree:MasterType:Thesis
Country:ChinaCandidate:J D MeiFull Text:PDF
GTID:2178360185961109Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
As an important branch in the field of intelligent control, neural network modeling based robust adaptive control for nonlinear systems has been received a great deal of attention in recent years. Some correlative issues in this area are studied in this paper, such as the controller design problem of uncertain nonlinear systems, which have the form of nonlinear standard or strict feedback. The design and analysis procedure is based on a series of control theories, which include Lyapunov stability theory, adaptive control theory, neural network control theory, integral variable structure control theory, and so on. The main work in this paper is summarized as follows.Firstly, a new supervisory control scheme of adaptive neural network control is proposed for a class of uncertain nonlinear dynamical systems with unknown constant control gains. The adaptive compensation term of the optimal approximation error is adopted to minify the influence of the modeling error. By introducing integral switching function, the approach does not require to solve Lyapunov equation. By theoretical analysis, the closed-loop system is proven to be globally stable in the sense that all signals involved are bounded, with tracking error converging to zero. Moreover, adaptive neural network control is investigated for a class of uncertain nonlinear dynamical systems with unknown dead-zone and unknown constant control gain. Also the closed-loop system is proved to be globally stable, with tracking error converging to zero.Secondly, the problem of adaptive neural network control is discussed for a class of uncertain nonlinear systems with unknown dead-zone and unknown function control gain. Two design schemes of adaptive neural network control are proposed for dead-zone models with equal slop and unequal slop. Base on the principle of sliding mode control and the property of Nussbaum, they do not require a priori knowledge of the design of the control gain and the upper bound and lower bound of dead-zone parameter to be known a priori. The adaptive compensation term of the optimal approximation error is adopted to minify the influence of the modeling errors and parameter estimation errors. For the case of equal slop, it is shown that the closed-loop system is semi-globally uniformly ultimately bounded, with tracking error converging to zero. For the case of unequal slop, the closed-loop system can only be proven to be semi-globally uniformly ultimately bounded.Lastly, base on backstepping technique, the property of Nussbaum and integral-type Lyapunov function, a novel design scheme of adaptive neural network controller is proposed for a class of strict-feedback uncertain nonlinear systems with unknown dead-zone. By introducing characteristic function for the dead-zone model in the systems, a simplified description of dead-zone model is developed. The approach removes the condition of the equal slope with defined region. In addition, it does not require a priori knowledge of the sign of the control gain and the upper bound and lower...
Keywords/Search Tags:nonlinear systems, adaptive control, neural network control, Nussbaum function, backstepping, dead-zone model, stability
PDF Full Text Request
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