Abstract: To address the control challenge of variable speed constant frequency(VSCF) operation in pump-motor systems under slow time-varying hydraulic parameters and external disturbances, a composite control strategy integrating feedback linearization with adaptation of the radial basis function(RBF) neural network is proposed. Firstly, a nonlinear mathematical model of the pump-motor system is established. System nonlinearities are addressed through feedback linearization theory, transforming them into a linear form. Secondly, an RBF neural network is utilized for the online approximation of unknown function terms within the system, combined with an adaptive algorithm designed to dynamically adjust the neural network weight matrix and controller parameters in real time, thereby accommodating the slow time-varying parameter characteristics. The closed-loop pump-motor VSCF system studied in this paper is applied in a wind power generation system. The simulation results demonstrate that, under combined external wind speed disturbances and internal parameter variations, the proposed control strategy effectively maintains the speed of the variable displacement motor within the standard range of national grid connection(1 500±6) r/min, ensuring successful integration to grid. Finally, experimental validation is conducted on a 24 kW hydraulic wind turbine semi-physical simulation platform. The experimental results further confirm the correctness of the theoretical analysis and simulation studies, indicating that the investigated control strategy exhibits excellent anti-disturbance capability and control precision in practical applications. This research provides robust technical support for the stable operation and efficient energy capture of VSCF pump-motor systems in power generation equipment.

Key words: pump-motor system, variable-speed constant-frequency control, slow time-varying parameters, feedback linearization, radial basis function neural network, adaptive control