State feedback and output feedback tracking control of discrete-time nonlinear system using Chebyshev neural networks

This paper deals with both state feedback and output feedback tracking control of discrete-time nonlinear system using CNN. Firstly, state feedback control is presented via backstepping, applied to a strict feedback form. In this CNN is used to approximate unknown functions to design control law by the backstepping technique and solves the non-causal problem in discrete-time system. After this output feedback control is presented by converting strict feedback form into cascade form (Brunovsky form). This paper also presents the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system. A single layer functional link CNN is used where the need of hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials and approximation of complex nonlinear systems becomes easier. A simulation example is given to show the effectiveness of control schemes.