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Adaptive neural network control for a class of nonlinear discrete-time systems. (Chinese. English summary) Zbl 1212.93183

Summary: For a class of single-input-single-output nonlinear discrete-time systems with unknown control direction, an adaptive neural network control is developed by incorporating a conventional incremental digital PID (proportional-integral-derivative) controller in an adaptive neural network term to guarantee the stability of the closed-loop systems. The conventional PID controller is utilized to stabilize the approximate linear system, while the adaptive neural network is introduced to deal with the influence of nonlinear terms on closed-loop systems. A discrete Nussbaum gain is introduced into the adaptation law of the weights in neural network to resolve the unknown control direction problem. It is proved that all signals of the closed-loop system are bounded with the tracking error converges on a compact set. Simulation results verify the effectiveness of the proposed control method.

MSC:

93C40 Adaptive control/observation systems
93C10 Nonlinear systems in control theory
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