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The Study on Hybrid Intelligent Controller and Its Simulation on Industrial Plants

Author: ZhouYingYu
Tutor: GuoChen
School: Dalian Maritime University
Course: Control Theory and Control Engineering
Keywords: Hybrid Intelligent PID control Fuzzy Control Neural Network Adaptive neuro-fuzzy inference system
CLC: TP273.5
Type: Master's thesis
Year: 2001
Downloads: 151
Quote: 4
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Abstract


This article focuses on the introduction of hybrid intelligent control system control algorithm and simulation. Various control strategies have a certain scope of application, using only a single control strategy is difficult to achieve the ideal control effect, but if organic combination of different control strategies, integration, you can achieve better control effect. In this paper, based on the comprehensive study of a large amount of literature several hybrid intelligent controller. Two intelligent PID controller based on the analysis of the basic principles of the PID control: variable parameter PID controller (Vapid) and fast intelligent PI controller (the Ripi). According to the size of the deviation, VAPID online through a non-linear function to adjust the P, I, D three parameters size to obtain satisfactory control performance. RIPI time optimal control of the basic principles and PI combination of a rule-based intelligent controller, the controller deviation within the time optimal control with PI control, a small deviation range, two control The switching of the method is the use of the state point switch. In the analysis of fuzzy control based on the proposed hybrid intelligent controller based on fuzzy control three: fuzzy the parameters adaptive PID controller has the robustness of fuzzy self-tuning controllers and control rules from the modified three-dimensional fuzzy controller. The fuzzy parameter adaptive PID controller is the result of combining fuzzy control and PID control, fuzzy rules and fuzzy reasoning to determine the parameters of the PID controller, in order to adapt to changes in the parameters of the controlled object. Robust self-tuning Fuzzy controller output scale factor Gu by another fuzzy rules library is adjusted according to the current trend of the controlled process online. Fuzzy rules using analytical description of the control rules from the modified three-dimensional fuzzy controller, the preferred method line correction control rules, the fuzzy rules from the optimization. Of the proposed hybrid intelligent controller for a large number of simulation studies, simulation results show that the control effect than the corresponding unmodified controller a greater improvement. In this paper, adaptive network fuzzy inference system-ANFIS be applied to the control system. ANFIS is a combination of fuzzy inference system and neural network with the ability to extract fuzzy rules directly from the sample data, so the lack of expert experience is especially suitable for complex industrial process control. Because it implements the organic combination of fuzzy and digital and adjust the parameters and weights have a clear physical meaning, more conducive to the control Engineering handsome understanding, mastery and application, so it has a wide range of industrial applications prospects. This article discusses the structure of the ANFIS and learning methods and example to the automobile brake the ANFIS engineering application. Comparison can be seen from the ANFIS simple fuzzy control simulation results, ANFIS is an effective hybrid intelligent control method.

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