A novel decentralised adaptive NN tracking control for double inverted pendulums
by Tieshan Li, Wei Li, Renxiang Bu
International Journal of Modelling, Identification and Control (IJMIC), Vol. 13, No. 4, 2011

Abstract: Adaptive trajectory-tracking control of double inverted pendulums (DIPs) connected by a spring is considered in this paper. By incorporating 'dynamic surface control (DSC)' approach and 'minimal learning parameters (MLP)' algorithm, a systematic procedure for the synthesis of a novel decentralised robust adaptive neural control scheme is developed. Two main advantages of the developed scheme are that: 1) The RBF neural networks (NNs) are only used to approximate those unstructured system functions rather than the unknown virtual control gain functions. Consequently, the potential controller singularity problem can be overcome. 2) Only one parameter needs to be updated online for each subsystem, both problems of 'dimension curse' and 'explosion of complexity' are avoided. The computational burden has thus been greatly reduced. In addition, the stability in the sense of semi-globally uniform ultimate boundedness (SGUUB) of the closed-loop system is established via Lyapunov stability analysis, and the tracking error can be made arbitrarily small. Simulation results for the trajectory tracking of the DIPs are presented to demonstrate the effectiveness and good transient performance of the proposed scheme.

Online publication date: Sat, 21-Mar-2015

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