A neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of uncertain nonlinear systems with unknown deadzones and magnitude constraints on the input. The NN controller consists of two NNs: the first NN for compensating the unknown deadzones; and the second NN for compensating the uncertain nonlinear system dynamics. The magnitude constraints on the input are modeled as saturation nonlinearities and they are dealt with in the Lyapunov-based controller design. The uniformly ultimate boundedness (UUB) of the closed-loop tracking errors and the neural network weights estimation errors is demonstrated via Lyapunov stability analysis.
P. He et al., "Neuro Control of Nonlinear Discrete Time Systems with Deadzone and Input Constraints," Proceedings of the 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at http://dx.doi.org/10.1109/CACSD-CCA-ISIC.2006.4777088
2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control
Electrical and Computer Engineering
Article - Conference proceedings
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