Observer-based adaptive fuzzy robust controller with self-adjusted membership functions for a class of uncertain MIMO nonlinear systems: A PSO-SA method

In this paper, a novel adaptive fuzzy robust controller with a state observer approach based on the hybrid particle swarm optimization-simulated annealing (PSO-SA) technique for a class of multi-input multi-output (MIMO) nonlinear systems with disturbances is proposed. Firstly, particle swarm optimization-simulated annealing (PSO-SA) is used to adjust the fuzzy membership functions, while adaptive laws are used to approximate nonlinear functions and the unknown upper bounds of disturbances, respectively. Secondly, a state observer is applied for estimating all states which are not available for measurement in the system. By using the strictly-positive-real (SPR) stability theorem, the proposed adaptive fuzzy robust controller not only guarantees the stability of a class of MIMO nonlinear systems, but also maintains good tracking performance. Thirdly, we propose a novel auxiliary compensation, the item is designed to suppress the influence of external disturbance and remove fuzzy approximation error, respectively. The intelligence algorithm consists of the adaptive fuzzy robust method, the individual enhancement scheme and particle swarm optimization-simulated annealing structure which generates new optimal parameters for the control scheme. Finally, one simulation example is given to illustrate the effectiveness of the proposed approach.