Because model switching system is a typical form of Takagi-Sugeno(T-S) model which is an universal approximator of continuous nonlinear systems, we describe the model switching system as mixed logical dynamical (ML...Because model switching system is a typical form of Takagi-Sugeno(T-S) model which is an universal approximator of continuous nonlinear systems, we describe the model switching system as mixed logical dynamical (MLD) system and use it in model predictive control (MPC) in this paper. Considering that each local model is only valid in each local region,we add local constraints to local models. The stability of proposed multi-model predictive control (MMPC) algorithm is analyzed, and the performance of MMPC is also demonstrated on an inulti-multi-output(MIMO) simulated pH neutralization process.展开更多
In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression ...In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control (SVMN-MPC) algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results.展开更多
文摘Because model switching system is a typical form of Takagi-Sugeno(T-S) model which is an universal approximator of continuous nonlinear systems, we describe the model switching system as mixed logical dynamical (MLD) system and use it in model predictive control (MPC) in this paper. Considering that each local model is only valid in each local region,we add local constraints to local models. The stability of proposed multi-model predictive control (MMPC) algorithm is analyzed, and the performance of MMPC is also demonstrated on an inulti-multi-output(MIMO) simulated pH neutralization process.
基金the 973 Program of China (No.2002CB312200)the National Science Foundation of China (No.60574019)
文摘In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control (SVMN-MPC) algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results.