Intelligent Control Research Based On Multiple Models Switching

Due to the parameters or the structure changes of the system to be controlled under the different operating mode, the traditional adaptive control algorithm is helpless in many practical control problems. New theories and new methods are studied for complex systems multiple models switching control in this paper, which aims at the problem of conventional adaptive control and multiple models adaptive control in existence. Here, the dissertation mainly discusses issues on multiple models based modeling and control of complex nonlinear systems. The main contributions of this dissertation are summed as follows:A novel adaptive switching control algorithm based on multiple models is proposed to improve the dynamical response performance of plants with large parameters variations under different operating modes. At the same time dynamic model bank is applied to establish models bank without the prior system information. The closed-loop system stability and track error convergence asymptotically are proved. The simulation results have confirmed the efficacy of the proposed methods.A multiple models adaptive switching control based on neural network is proposed, which aims at the problem of conventional adaptive control and multiple models adaptive control in existence. We adopt the closest cluster algorithm to classify the samples, and then utilize RBF neural network's strong learning and nonlinear approximates abilities to model off-line. Meanwhile dynamic model bank technology is applied to establish multi-model. Once the real time system's states go beyond the space, which is formed of all existing sub-model, then a new state is learnt through online neural network and set up a new model that is added in the dynamic model bank in order to improve the dynamic system's transient response and robustness. Finally, simulations are given to demonstrate the validity of the proposed method.Multiple models adaptive control based on hierarchical structure is presented, which aims at the problem of many sub-models in conventional multiple models adaptive control. The system to be controlled is divided into basic operating-condition level and control model level. Multiple models and corresponding controllers are automatically established on-line by the conventional adaptive model and a reinitialized one. The proposed method can reduce the number of...