Direct Adaptive Neural Dynamic Surface Control of Uncertain Nonlinear Systems with Input Saturation
In this paper, we present a new scheme to design direct adaptive neural network controller for uncertain nonlinear systems in the presence of input saturation. By incorporating dynamic surface control (DSC) technique into a neural network based adaptive control design framework, the control design is achieved. With this technique, the problem of “explosion of complexity” inherent in the conventional backstepping method is avoided, and the controller singularity problem is removed, and the effect of input saturation constrains is considered. In addition, it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, simulation studies are given to demonstrate the effectiveness of the proposed scheme.
KeywordsAdaptive control dynamic surface control input saturation
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- 1.Krstic, M., Kanellakopoulos, I., Kokotovic, P.V.: Nonlinear and Adaptive Control Design. Wiley, New York (1995)Google Scholar
- 7.Chen, M., Ge, S.S., Choo, Y.: Neural network tracking control of ocean surface vessels with input saturation. In: Proc. of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009, pp. 85–89 (2009)Google Scholar
- 8.Li, J.F., Li, T.S.: Design of ship’s course autopilot with input saturation. ICIC Express Letters 5(10), 3779–3784 (2011)Google Scholar
- 9.Zhou, J., Er, M.J., Zhou, Y.: Adaptive neural network control of uncertain nonlinear systems in the presence of input saturation. In: ICARCV 2006, pp. 1–5 (2006)Google Scholar