Research Of Control Parameter Self-Tuning For Speed Control Of Servo System

High precision AC servo system requires good controllability,high stability and strong adaptability,and the controller parameters has a great impact on system performance.In this paper,the speed loop of AC servo system is taken as the research object,and the method of parameter off-line and on-line self-tuning of controller is put forward.The specific research is as follows:Firstly,the mathematical model of permanent magnet synchronous motor(PMSM)is introduced.On this basis,the principle of vector control is analyzed in detail.In this paper,PI-IP controller is used to control the speed loop of servo system.For off-line self-tuning of control parameters,recursive least squares algorithm is used to obtain the parameters of the controlled model of speed loop offline.On this basis,genetic algorithm is used to directly optimize the PI-IP control parameters,and the initial population,selection replication operator,crossover operator and mutation operator of genetic algorithm are optimized.The simulation results show that the method can quickly and effectively obtain PI-IP control parameters,and the speed control accuracy is significantly improved.For on-line self-tuning of control parameters,an just in time learning algorithm is proposed to identify the parameters of the controlled model of speed loop on-line in order to quickly and real-time characterize the characteristics of servo operation.The online updating strategy of the traditional just in time learning algorithm is optimized.On this basis,the prediction speed error is established,and the mapping relationship between working condition characteristics and PI-IP control parameters is realized by Lyapunov method.The simulation results show that,compared with the recursive least squares algorithm,the improved instant learning algorithm has higher convergence speed and better matching of control parameters.Finally,the hardware and software experimental platform of AC servo system is built,and the off-line self-tuning method of control parameters is verified,which proves its effectiveness and consistency.