Hybrid fuzzy direct/indirect adaptive controller for uncertain nonlinear systems

This paper describes a new approach to adaptive control of uncertain nonlinear systems. A fuzzy logic controller is used to combine both direct and indirect methods. Based on the fuzzy neural networks, the plant unknown nonlinear functions are estimated, and then combined to form the indirect control law. In parallel, another fuzzy neural network approximates the direct adaptive control. According to the modelling error and its derivatives, the fuzzy logic controller modulates between direct and indirect adaptive controllers. The global stability of the overall system is shown by constructing a Lyapunov function. The simulation results show that within this scheme, the control objectives can be achieved with a fast convergence and optimal control for different dynamic regimes.