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Because the nonlinear and time-varying characteristics of the V-belt continuously variable transmission system driven by a permanent magnet synchronous motor (PMSM) are unknown, improving the control performance of the linear control design is time-consuming. To overcome difficulties in the design of a linear controller for the PMSM servo-driven V-belt continuously variable transmission system with lumped nonlinear load disturbances, a composite recurrent Laguerre orthogonal polynomial neural network (NN) control system with ameliorated particle swarm optimization (PSO), which has the online learning capability to respond to the nonlinear time-varying system, was developed. The composite recurrent Laguerre orthogonal polynomial NN control system can perform inspector control, recurrent Laguerre orthogonal polynomial NN control which involves an adaptation law, and recouped control which involves an estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of ameliorated particle swarm optimization yielded two optimal learning rates for the parameters, which helped improve convergence. Finally, comparison of the experimental results of the present study with those of previous studies demonstrated the high control performance of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this study, an adaptive control scheme with a pre-specified H∞ property is proposed for the tracking control of permanent magnet (PM) synchronous motor drives. Under the influence of uncertainties and external disturbances, by using feed-forward compensation and the introduction of a parameter tuner, the robust performance control problem is formulated as a nonlinear H∞ problem and solved by a quadratic storage function. This new design method is able to track both the step and sinusoidal commands with improved performance in face of parameter perturbations and external disturbances. Simulation and experimental results are provided to demonstrate the effectiveness of the proposed adaptive H∞ control. [Copyright &y& Elsevier]