This paper presents a wavelet cerebellar model articulation controller (WCMAC) based adaptive control design for nonlinear systems using a particle swarm optimization (PSO). The WCMAC is the main tracking controller and a robust compensation controller is used to compensate for the residual error. In the WCMAC, the adaptive laws of controller parameters are derived using the gradient descent method. However, the initial values of learning rates of these adaptive laws are very important and they affect much to the performance of control systems. In this paper, the particle swarm optimization algorithm is applied to find the optimal learning rates of the parameter adaptation laws. To show the effectiveness of the proposed approach, numerical simulations of magnetic levitation system and inverted pendulum are provided to confirm the applications of the proposed PSO-WCMAC-based control system. The superiority of the proposed control scheme is also evaluated by quantitative comparison with other control schemes.
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