Adaptive Backstepping Controller of Discrete-Time Nonlinear Systems with Input Saturation
DOI:
https://doi.org/10.18486/ijcsnt/1.2.008Keywords:
Backstepping Controller, Chebyshev Neural Network (CNN), Actuator Saturation, Lyapunov StabilityAbstract
This paper proposes a backstepping controller for the class of discrete-time nonlinear system in the presence of saturation constraints. In this paper, hyperbolic tangent function is used to limit the amplitude saturation constraints. The actuator saturation is assumed to be unknown and compensated by a pre compensator using Chebyshev neural network (CNN). The unknown nonlinear functions are also approximated by CNN. Weight update laws, based on Lyapunov theory are derived to make this scheme adaptive and the convergence properties are shown. Simulation results validate the effectiveness of proposed scheme.
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