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SoPC-Based Function-Link Cerebellar Model Articulation Control System Design for Magnetic Ball Levitation Systems

This paper proposes a more efficient intelligent control algorithm that can be applied to unknown nonlinear systems. First, a more general neural network (NN) referred to as a function-link cerebellar model articulation controller (FLCMAC) is proposed. In some cases, this FLCMAC can be reduced to an NN and a function-link NN. An FLCMAC-based control system is then developed. The proposed control system comprises an FLCMAC and a robust controller. The FLCMAC is the principal tracking controller used to mimic an ideal controller, and the parameters of FLCMAC are online tuned using derived adaptation laws that use the Lyapunov function. The robust controller can eliminate the approximation error so that the asymptotic stability of the system is achieved. The proposed control system is then applied to a magnetic ball levitation system (MBLS), which is an intricate and highly nonlinear system. For practical experiment, the proposed control scheme is implemented using a system-on-programmable-chip technique. Finally, the simulations and experiments are performed for an MBLS in order to illustrate the effectiveness of the proposed control system for achieving precise trajectory tracking.