Inverse Dynamics Modeling And Control For Thermal System Based On Support Vector Machines

System inverse dynamics is a class of typical inverse subject, its key problem is that the input of system is determined according to the anticipant output and known current and past information. Inverse dynamics of thermal system has become an important issue in many relative areas such as thermal process control and fault diagnosis. It has very important scientific and practical significance that study on inverse dynamics of thermal system.Support vector machines(SVM) based on the statistical learning theory is a new approach in machine learning. SVM has become an effective tool for complexity system identification because of its advantages such as firm mathematic theory foundation, strict theory analysis, global optimization as well as good adaptability and generalization. Inverse dynamic model identification and control for thermal system based on SVM is studied in this dissertation. The main works include the following five parts:①The basic structure of inverse dynamic model is built for two typical thermal system. A reduction method for input vector of multiple input and multiple output(MIMO) inverse dynamic model is proposed. The largest relation value and leading parameters are obtained through analyzing dynamic characteristics of object, and then the input vector of MIMO system inverse dynamic model is determined. Four basic structures of input vectors are proposed by studying on inverse dynamic model of dual input and dual output system.②Compared with SVM, LSSVM whose learning algorithm is changed to solve linear equations has widely applied in function estimation and approximation. However, LSSVM is difficult to realize on-line identification for complexity system because it need solve inverse matrix when updating model in dynamic modeling process. Recursive least square support vector machines(RLSSVM) is developed by combining recursive least square(RLS) algorithm with LSSVM. The parameters of model are updated by RLS algorithm. To speed up the computational speed, the number of support vectors is reduced by doing pruning. The accuracy of model and real-time of on-line identification is improved in this method. On-line identification for thermal system inverse dynamic model is realized by RLSSVM. The simulation results show the effectiveness of on-line identification method which is based on RLSSVM for inverse dynamic model.③Because selecting kernel function and its parameters is very complex in LSSVM and T-S fuzzy model has weak generalization performance. Fuzzy modeling method based on LSSVM(FLSSVM) is proposed by combing LSSVM with fuzzy model. FLSSVM is proved that is equivalence with LSSVM. The concept of sliding time widow is adopted in on-line algorithm of FLSSVM, the cluster centers are updated by fuzzy competitive learning algorithm. Different weighs are assigned to the samples in sliding time window according to the sampling time when identifying conclusion parameters. The simulation results of identification for thermal inverse dynamic model show that modeling method based on FLSSVM has good precision and tractability. The model based on FLSSVM also has good generalization ability and adaptability to noise.④An adaptive inverse control system based on inversed dynamic model of FLSSVM is constructed by combining the identification method of inverse dynamic model with adaptive inverse control method. The inverse dynamic model of control object is identified on-line by using FLSSVM, and then the controller is updated in the control process. The output disturbance of system is canceling adaptively with inverse dynamic model based on FLSSVM. Adaptive inverse control systems are designed for superheated steam temperature and unit load of power plant. Simulation results show that the adaptive inverse control system has good control performance and adaptability, and has good disturbance canceling effect.⑤The main problem of present two section control method is ensured according to analyze typical dynamic characteristics of steam temperature in once-through boiler. An adaptive inverse control system is built for feed water and superheated steam temperature in once-through boiler based on inverse dynamic model. The control of feed water and superheated steam temperature is realized through on-line identification for inverse dynamic model. In this control system, the demand of feed water and spray water flow to superheated steam temperature and micro-superheated steam temperature is considered. Simulation results show that the control system is designed has good control performance and adaptability and can void the repeated oscillation phenomena of control variables which appear in present two section control system.