Abstract:
The main objective of this thesis is the development of adaptive control laws for particular classes of uncertain SISO and MIMO nonlinear systems. The cases of the affine and non-affine systems, the presence of the constraints on the outputs and/or the states as well as the external perturbations are studied. Furthermore, the saturation and dead-zone inputs nonlinearities of the dynamical systems are investigated. In all the proposed approaches, the neural networks are used as universal approximators to estimate the unknown nonlinearities of the dynamical systems. The structures of the proposed controllers deal with the famous problems of explosion of complexity while avoiding the use of the traditional back-stepping technique. The proposed adaptive neural controllers have few online learning parameters compared to similar works. In addition, stability analysis of all control loops is carried out by using the Lyapunov method, and for each scheme, simulation results are given to show its effectiveness.
