The Research Of Adaptive Inverse Control Strategy For Permanent Magnet Synchronous Motor Servo System

The permanent magnet synchronous motor control system has been widely used in industry, agriculture, aerospace and various fields of social life. With the continuous progress of science and technology, servo system requirements have become increasingly stringent. So study the high-performance permanent magnet synchronous motor servo system control strategy and development high-performance permanent magnet synchronous motor servo system products has extremely important practical significance and practical value to improve industrial levels and promote the development of defense industry.The permanent magnet synchronous motor servo system as a nonlinear, multivariable, strong coupling varying systems is difficult to describe in precise mathematical model. In the process, the external disturbance and other nonlinear factors will lead to poor system performance. Therefore, to improve the performance of the control system of permanent magnet synchronous motor servo system, advanced control strategies must be used to overcome parameter variations, external disturbances and make the system has good tracking performance and strong robustness.This paper proposed composite adaptive inverse control strategy for permanent magnet synchronous motor servo control system based on nonlinear signal processing method. The system adopts the position loop, speed loop and current loop control structure. The speed loop and current loop based on id=0control method. The id=0control method can make permanent magnet synchronous motor achieves wide speed range and good torque performance.The key performance of permanent magnet synchronous motor servo system is position accuracy. In order to improve it, the adaptive inverse control strategy is used in the position loop. The adaptive inverse control strategy combined with the idea of inverse control and adaptive control. When the controller is the inverse of the plant, the output of position can track the input precisely. Meanwhile the whole disturbance between the plant and model derive both the plant and model, and this disturbance was subtracted from the input of plant by driving the inverse of the model, and the noise and disturbance of system can be eliminated ultimately. This strategy solves the problem of high performance control of AC servo system preferably.To further enhance the performance of adaptive inverse control, an improved variable step size LMS algorithm based on dependent errors was proposed. This variable step size LMS algorithm improves the convergence speed and overcome the impact of parameter disturbances.Nonlinear filter as a key component of the nonlinear adaptive inverse control directly impact the performance of adaptive inverse control strategy. In order to better achieve the nonlinear adaptive inverse control, an improved nonlinear adaptive filter based on dynamic RBF neural network and FIR filter is proposed. Meanwhile, the chaos multi-population particle swarm optimization(CMPSO) algorithm is used to training of the nonlinear filter weights offline, and the training results as the initial weights of the nonlinear filter. This method improves the control performance of adaptive inverse control strategy.Combined with the proposed nonlinear filter and variable step size LMS algorithm, the permanent magnet synchronous motor servo system based on adaptive inverse control is realized. Further, a composite adaptive inverse control strategy is proposed based on the conventional PI control strategy and adaptive inverse control strategy. Simulation and testing results show that the composite adaptive inverse control strategy for permanent magnet synchronous motor servo system has good dynamic response, steady-state precision and disturbance-rejection ability. The validity and advantage of the proposed control strategy is proved.