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Application of Computational Technology and Automation Intelligent Control in AC Motor Speed ​​Regulation Hu Jin Li Zhongfa Liang Xianyu Zhao Huan (Hunan Computer College, Changsha, Hunan, China) This paper is aimed at model uncertainty, system nonlinearity, detection and control in AC motor speed control. The characteristics of transmission and other difficulties are presented. A real-time intelligent control scheme and control structure are proposed, and coordinated control rules and fuzzy control algorithms are given.
Intelligent Control Coordination Control Rule Fuzzy Control Algorithm 1 Introduction AC motors have the advantages of simple structure, low price and few failures compared with DC motors. They are widely used in industry, agriculture, transportation, national defense industry and other industries. The disadvantage is that the speed regulation performance is poor. At present, in the development and application of automatic control systems, high-precision, high-speed movement is a very critical issue. These factors mainly depend on the characteristics of the servo motor drive system. It is hoped that the servo system has the characteristics of fastness, no overshoot, no static difference, and strong interference. If the control object model is determined, more applications in the project are to add feedforward compensation, to form an open and closed loop composite control system, or to use full digital PID control, linear optimal control of state feedback, and so on. However, if the control object is uncertain and the parameter changes greatly, the above control method is difficult to obtain a satisfactory control effect. In this case, adaptive control, variable structure control, and H optimization control can be employed. However, the robustness of adaptive control is limited. When the control object parameters change within a wide range and have nonlinearity, the adaptive control is also incapable of force-variable structural control. So far, there is an unsolved jitter problem. H optimization control is computationally intensive and requires an accurate mathematical model. In view of the shortcomings of the above methods, for the characteristics of model uncertainty, nonlinearity, and detection and transmission in AC motor speed control, we propose a real-time intelligent control scheme and control structure, and give coordinated control rules and fuzzy control. algorithm.
2 System structure design According to the characteristics of AC motor speed control process, we introduce expert intelligent control and Bang-Bang control, Fuzzy control, PI control, designed as the secondary control structure shown in Figure 1.
The first-level intelligent control system includes feature identification information processing, intelligent coordinator, and the second stage of the converter is composed of Bang Bang control, fuzzy controller, and PI controller. The core part is the intelligent control system, which mainly completes the coordinated switching control of the three control schemes, and successfully gives the correct control strategy based on the information provided by the feature identifier.
According to the control theory, Bang-Bang control is when the rate of change of deviation and deviation is large. Increase the control intensity, you can mention Hu Jin, etc.: Intelligent control in the AC motor speed control application of high system speed, but easy to cause excessive overshoot, as shown in Figure 2.
Legend: n - given speed n - actual speed Fuzzy control is not sensitive to parameter changes of the controlled system, does not depend on the object model, and is beneficial to improve the dynamic performance of the system, but because the input and output of the controller are quantified The binning often causes problems such as limit cycle oscillation and excessive dead zone residual error (static difference), as shown in Figure 3.
The form of the control action is where the κ-regulator scale factor κ-regulator integral time constant. The proportional part reflects the adjustment effect, and the integral part eventually eliminates the static difference. The PI controller helps to improve the stability of the system, but the speed is poor, as shown in Figure 4. For systems with time-varying, non-linear factors, and large changes in the parameters of the object, single PI control is difficult to obtain good control effects. Therefore, combining the advantages of the three, according to experience, designing intelligent coordination control rules, it is indeed feasible to achieve correct switching.
3 Coordination control rules According to the above design ideas, and taking into account the sudden changes in control of different controller switching and the coordination problems between the two switching threshold calculation techniques and the automatic oscillation, the following coordination control rules are given: entry control system Maintaining the current control) respectively indicates the maximum error, the minimum error Δe indicates that the deviation change indicates the maximum error change rate, and the minimum error change rate α, β respectively indicates the allowable deviation, and the allowable deviation change rate λ(0 λ 1) indicates that the forgetting factor κ is The proportionality coefficient u represents the maximum control amount and the minimum control amount, respectively. These parameters can be adjusted in the simulation or in the field.
4 Fuzzy control algorithm adopts double input and single output, and the language inference rule is expressed as: the weighted average judgment control quantity is calculated offline by the above formula, and the control table is obtained, and the table can be checked during actual control.
5 Conclusion Before the real-time control of AC motor speed control system, offline simulation is carried out first. Off-line simulation includes dynamic modeling and simulation and speed control system Hu Jin. Intelligent control simulation of intelligent control in AC motor speed regulation. After the digital simulation, the parameters of the real-time control test are initially obtained, and then the real-time control is entered. Figure 5 shows the output response log of the motor speed at a given speed value n = 1000 rpm, when the rated load is started on the motor belt. Curve 1 is the result of Bang Bang control. Curve 2 is the result of Fuzzy control. Curve 3 is the result of PI control. Curve 4 is the result response curve of intelligent coordinated control. The control results show that the intelligent coordinated control is superior to other controllers in several main control indicators.
Reference to the object: (3) DLL object compilation: Create object: to display and access the image, compile it to generate DLL library file.
Registered object: Register the DLL object with REGSV R32 so that the object can be called.
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