Research on PID Adaptive Control of Electric Drive System
At present, most of the actual servo system control still uses PID control, because this controller has the advantages of being intuitive, easy to implement and robust. However, for the actual system, especially for the actual servo system with variable load and strong interference, the control of this control strategy is affected because its mathematical model is difficult to establish, the uncertainty and nonlinearity of the model. In this paper, the neural network is combined with the PID controller to complete the PID adaptive control. The simulation and the 3PID adaptive controller design 3.1 system requirements. In the system, the moment of inertia changes greatly, there is a large imbalance torque and no The balance torque varies greatly. At the same time, the system position accuracy is 0.3mard, the speed error is 2Cmard), and the equivalent sinusoidal error is 4tmard ((a=37*/s2, v=24*/s), so the conventional PID controller is difficult to meet the system requirements. The sum is used instead of the integral, and the differential is replaced by a finite difference, that is, the above formula is: where T is the sampling period. For the above formula, a two-layer linear neural network construction controller is used as shown in Figure 1: The gradient control method can be used to obtain the neural network PID control coefficient correction formula: for the initial value and the learning step size, it is not only related to whether the global minimum point is reached. , but also affect the length of study time Tao Yonghua and so on. The new PID control and its application 丨MBu Machinery Industry Publishing (Continued from page 8) fuzzy controller solves the problem of large static error in fuzzy control, and its dynamic performance is better than classical PID control and pure Volume fuzzy control. 6 Conclusions This paper proposes a filter identification method for online identification of time delays. Based on this, the proposed fuzzy controller overcomes the influence of time delay on the steady-state performance of fuzzy control due to the introduction of prediction. The defect of the fuzzy control itself is better overcome. The fuzzy controller improves the steady state performance of the fuzzy controller comprehensively from both internal and external factors. Li Zhanming, Li Juan. An incremental fuzzy controller that effectively overcomes the static difference. Industrial instrumentation and automation devices. 2000 (4):: 6. Li Zhanming, Li Juan. A variable structure adaptive model that effectively overcomes the static difference, Bai Jianguo, Hu Kejian. A new method to compensate for the pure lag process - intelligent sampling adjustment 丨 J. chemical automation instrumentation. 1988, Wang Dan, Hu Xiaojing. Intelligent resistance furnace temperature control system | J |. Automation Chen Xiaohong. Application of Fuzzy Controller in Resistance Furnace Temperature Control System IJ. Measurement and Control Technology. 1966 Chen Chang sample. Predictive fuzzy controller and its application IJ. Fuzzy system and number The refractory fiber used in temperature resistant fireproof cloth is a kind of temperature resistant inorganic fiber. Temperature resistant fireproof cloth has the characteristics of good applicability, easy processing, wide use, used for temperature resistance, heat insulation, heat preservation material. Fiberglass Cloth Wuxi WenqiIndustry and Trade CO.,LTD. , https://www.wenqiIndustry.com