A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machi...A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machine (SVM) dynamic approximation with analytical control law derived. The method does not need on-line parameters estimation because the system’s internal model has been transformed into an off-line global model. Compared with other traditional methods, this control law reduces on-line parameter estimating burden. In addition, its overall linear behavior treating method allows an analytical control law available and avoids on-line nonlinear optimization. Simulation results are presented in the article to illustrate the efficiency of the method.展开更多
Control invariant sets play a key role in model predictive control.Using Lyapunov function,a technique is proposed to design control invariant sets of planar systems in a precise form.First,itis designed for a linear ...Control invariant sets play a key role in model predictive control.Using Lyapunov function,a technique is proposed to design control invariant sets of planar systems in a precise form.First,itis designed for a linear system in Brunovsky canonical form.Then,the result is extended to generallinear systems.Finally,the nonlinear control systems are considered,and some sufficient conditionsand design techniques are also obtained.Numerical examples are presented to illustrate the proposeddesign methods.展开更多
基金Project (No. 60421002) supported by the National Natural ScienceFoundation of China
文摘A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machine (SVM) dynamic approximation with analytical control law derived. The method does not need on-line parameters estimation because the system’s internal model has been transformed into an off-line global model. Compared with other traditional methods, this control law reduces on-line parameter estimating burden. In addition, its overall linear behavior treating method allows an analytical control law available and avoids on-line nonlinear optimization. Simulation results are presented in the article to illustrate the efficiency of the method.
基金supported by the National Natural Science Foundation of China under Grant Nos. 60674022, 60736022 and 60821091
文摘Control invariant sets play a key role in model predictive control.Using Lyapunov function,a technique is proposed to design control invariant sets of planar systems in a precise form.First,itis designed for a linear system in Brunovsky canonical form.Then,the result is extended to generallinear systems.Finally,the nonlinear control systems are considered,and some sufficient conditionsand design techniques are also obtained.Numerical examples are presented to illustrate the proposeddesign methods.
