Intelligent Inverse System And Its Application To Decoupling Control Of Induction Motor

Nonlinear system control plays an important role in the control science. Inverse system control gets flourish development in nonlinear control since 1980s.It has established perfect design theory by introducing some definitions such asα-th order inverse, pseudo linear system and so on. But the exact mathematical model is required in the inverse system control and the model is hardly satisfied in engineering. It is hard to get the analytical inverse, especially to some complicated systems. These are the bottlenecks in the inverse system theory.Combining with intelligent machine learning methods, as neural networks and support vector machines which are independent on mathematical model, intelligent inverse system control method and some significant results are proposed in this thesis.Firstly, the whole control system can exhibit satisfactory dynamic result only by perfect matching the controller and composite pseudo linear system in the generalized inverse control. The traditional generalized inverse must be re-identified with the variation of the expected transfer function. A new type of generalized inverse system is proposed here and its existence is proved. The poles of the pseudo linear system can be assigned via altering the parameters of the feedback part arbitrarily. The dynamic performance of the control system can be improved by regulating the parameters of controller and feedback part. By using this method, induction motor decoupling control can obtain good results.To improve the robustness of inverse, interval self-organizing maps and its learning algorithm is proposed based on interval analysis theory and traditional self-organizing map. The upper and lower boundaries of interval weights are trained and the convergence of the map is proved by combining the capability of VQTAM in storing the information of dynamic system, the nonlinear inverse model is built to deal with uncertainty.The uniqueness of the nicely nonlinear model and its linearizing compensator is proved under some constraints. A new model identification method is proposed by minimizing a novel objective function using neural networks. A nonlinear internal control system is designed on the compensated pseudo-linear system to improve the robustness and reduce the uncertainty including model error and external disturbance. The whole control system can track the reference signal accurately.The key of inverse control is the generalization of the inverse system. An a th-order inverse control method based on least square support vector machines(LS-SVM) on-line algorithm is presented. The inverse execute online learning with incremental and decremental algorithm by introducingεinsensitive function which enhances the accuracy and robustness of the pseudo linear system. The finite gain stability is ensured if the kernel function of LS-SVM is local Lipschitz, and the sufficient condition of local Lipschitz of Gaussian kernel function to any variable is given.At last, the validity of new type of generalized inverse control is proved via simulations of decoupling performance, disturbance-rejection performance and robustness based on vector control. While it is hard to achieve decoupling control on induction motor in a wide range using single neural networks. A multi-model decoupling method is proposed using multi-model theory and the new type of generalized inverse. The rotor flux and rotor speed of induction motor can be decoupled in a wide range by constructing several sub-generalized inverse, designing relevant controller and formulating proper switching rules.