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This paper investigates a fuzzy model reference adaptive controller (FMRAC) for continuous-time multiple-input-multiple-output (MIMO) nonlinear systems. The proposed adaptive scheme uses a Takagi-Seguno (TS) fuzzy adaptive system, which allows for the inclusion of a priori information in terms of qualitative knowledge about the plant operating points or analytical regulators (e.g., state feedback) for those operating points. A proportional-integral update law is used to obtain a fast parameters adaptation. Stability and robustness of this adaptive scheme are established using Lyapunov stability tools. The simulation results, for a two-link robot, confirm the performance of the proposed approach.

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Metaheuristic optimisation techniques such as the Grey Wolf optimiser (GWO) and artificial bee colony (ABC) algorithms have been developed for enhancing the dynamic behaviour of wind energy conversion system. The stand-alone doubly fed induction generator (DFIG) control system based on the direct voltage control is experimentally validated for the robust independent control of the stator voltage amplitude and the consequent rotor current regulation. The GWO and ABC are used for selecting the optimal gains of the proportional-integral (PI) regulator to improve the dynamic performance and the robust stability of the DFIG system in the presence of step voltage variation and sudden load operation. Through, MATLAB™/ Simulink numerical simulations, the dynamic performances of the GWO-PI and ABC-PI applied to stand-alone DFIG systems are compared with the conventional PI controller under different disturbances. Using a 3 kW DFIG test bench DSPACE DS1104 card prototype, the experimental validation justifies the superiority of proposed novel GWO-PI and ABC-PI controllers over the conventional PI control in terms of steady-state errors, maximum overshoots, settling time and rise time.