Research On Combined Design Of Advanced Control System Based On Several Intelligent Methods

Nonlinear characteristics and noise disturbance exist in most of the actual systems. The conventional control methods based on classical control theory and modern control theory are mainly designed for linear systems, which can hardly be applied to strongly nonlinear and complex systems. As the artificial intelligence technologies continue to develop, products of these technologies represented by fuzzy system and artificial neural network show the powerful processing capability to complex nonlinear systems. A series of advanced control systems based on intelligent control theories and methods are continuously put forward and improved, great breakthrough and plentiful fruits are derived for the control problem of complex systems. However, due to the immaturity of the foundation theory of intelligent control,there are still many aspects and key points need to be improved when applying the control methods. In view of the combined design of the advanced intelligent control system in future, the main research work of this dissertation can be described as follows:1,This chapter gives an overview of the background and the development of intelligent control theory, sums up the main research methods and achievements of intelligent control field, illustrates the significance and practical value in the study of the combined design of intelligent control system.2,A fuzzy neural PID controller consists of a fuzzy neural network and a PID neural network is proposed. A partical swarm optimization (PSO) algorithm based on chaos optimization is designed. The control system is designed based on the work above, the system scheme includes: the fuzzy neural PID network is used as the controller; the parameters of the controller are optimized by the mixed learning methods integrating offline particle swarm optimization algorithm combined with chaos strategies of global searching ability and online BP algorithm of local searching ability; a typical nonlinear object and a time delay object are used as controlled plant.3,A self-adaptive intelligent PID control system is presented based on least squared support vector machine (LS_SVM). LS_SVM is used as the identifier for uncertain objects. The proposed control system scheme includes: the controller and the optimization algorithm configuration are the same as the last chapter; by introducing LS_SVM, the control system is extended to an improved scheme which can handle the objects with uncertainty.4,A neural network control system for time-delay objects is presented based on an improved ant colony algorithm (ACO). The proposed control system scheme includes: the controller uses the fuzzy neural PID controller; the parameters of the controller are optimized by the mixed learning methods integrating offline chaotic ant colony optimization (CACO) and online error back propagation algorithm; LS_SVM is used as identifier, and it is trained offine and online to obtain the forecasting value of the next step time of the discrete system. By applying the proposed control system, the simulation is done with a air conditioning room object. Also, the model reference adaptive control system for the whole air conditioning system is designed based on radial basis function (RBF) neural network. The general designing method of the control system using feed-forward neural network is described in details.5,In view of complicated, undetermined and strongly nonlinear aeroengine object, a novel control scheme integrating the merits of fuzzy inference, neural network adaptation and simple proportional-integral-derivative (PID) method is presented. The proposed control system scheme includes: the controller uses the fuzzy neural PID controller; the parameters of the controller are optimized by the mixed learning methods integrating offline chaotic ant colony optimization (CACO) and online error back propagation algorithm; LS_SVM is used as identifier, and it is trained offine and online; the parameters of LS_SVM is optimized by cross-validation method; the simulation is done with a aeroengine object at the designed work condition.6,A novel control scheme consists of several intelligent control strategies for aeroengine acceleration process is proposed. The proposed control system scheme includes: the controller is constructed by a fuzzy neural PID network; the parameters of the controller are optimized by offline quantum-behaved particle swarm optimization (QPSO) with chaos strategy combined by online error back propagation tuning; in process of acceleration, the model with the wide range of uncertainty is partitioned by designed nominal model and several models with the small range of uncertainty are derived; these models are classified and identified by LS_SVM offline. In online situation, the designed model with the small range of uncertainty is selected automatically by LS_SVM based on system data, and the controller is tuned by error back propagation simultaneously; the parameters of the classifier are optimized by cross-validation and the identifier by QPSO. The whole system with the model selected by online system data is designed by nonlinear PID strategy based on pattern recognition and intelligent neural network; the simulation is done with an aeroengine object in acceleration process.7,In order to overcome the conservation of conventional robust control methods, a new classifying and switching strategy based on LS_SVM for the control of uncertain system with the parameters varying in a wide range is proposed. The proposed control strategy includes: the original system model is divided into several models with small range of uncertainty; these models are classified by LS_SVM combined with principal component analysis (PCA) offline; for each model, the sliding-mode controller (SMC) with its gain tuned by RBF neural network is designed to reduce the chattering phenomenon effectively, and the QPSO algorithm with chaos strategy is designed and applied to adjusting the parameters of the controller so as to construct an optimized switching function. In online situation, each model with the designed SMC is selected automatically by LS_SVM based on system data; the parameters of LS_SVM are optimized by QPSO with chaos strategy to improve the performance of classification and generalization. In final, the system scheme is designed by the proposed method.8,Considering the fuzzy neural adaptive control method for undetermined nonlinear system, the control scheme are studied and improved based on the conventional fuzzy neural network control scheme. The controller, identifier and optimization algorithm of the scheme are designed respectively by the improved new methods. The proposed control system scheme includes: the fuzzy neural PID network is used as the controller, and LS_SVM as the identifier; the controller is optimized by offline QPSO with chaos strategy combined by online error back propagation tuning; the parameters of LS_SVM are optimized by PSO with chaos optimization; the stability of the improved scheme is discussed to finish the whole design in conclusion; the simulation is done with a heat exchanger object.In final, the main research work is summarized. As a whole, the conclusions derived from this dissertation is illustrated. The innovation points and the future outlook of the research work is given.