A novel decentralised adaptive NN tracking control for double inverted pendulums Online publication date: Sat, 21-Mar-2015
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com