Abstract:As the traditional PID neural network could not effectively control the realtime nonlinear multivariable system, this paper proposed a new type of multivariable adaptive PID neural network controller. This control system could put out feedback and activation feedback, with the function of proportion, integration and differentiation. We used the Particle Swarm Algorithm which is based on the solution space division to optimize the parameters of the controller. It also could eliminate effect of initial values on the accuracy of the controller and can be applied to the parallel mechanism control system. As the simulation results shown, controller had higher precision, better robustness and adaptability. This research provided a theoretical basis for the optimization design and performance analysis of the parallel mechanism.