A Research On The Construction And Dynamic Optimization Of Models Set For The Multiple Models Adaptive Control

Multiple models adaptive control is a tracking technique which uses the method of multiple models, combined with adaptive controlling. It improves system's ability of controlling when the parameters of the system change. The convergence rate of traditional adaptive control is slow in the beginning and the traditional system cannot deal with the situation in which the system's construction and parameters change in a sudden, resulting in the same deficiency of controlling function. After introducing the fixed parameters models, we can raise the convergence rate of system with the model collection formed with multiple models. At the same time, it can be more close to the complexity and nonlinear character of the system, which has a better controlling effect to this kind of system.Multiple models adaptive control has a key problem which is the construction and the optimization of model sets. Multiple models bring better function of dynamic controlling and control precision but also more operational costs, and also have a bad effect on the dynamic characteristics. As the system becomes more complex, the range of the change of the parameters become larger, and the number of models which are needed to be more close to the real models. When the number of models is the same as before, how the model set can cover the range of the change of the parameters. And in this situation, the control precision must keep the same lever, and the computing consumption must be lower. This is an important direction in this kind of research.In this paper, it reappear the multiple models adaptive control based on corresponding theory of multiple models adaptive control and show the superiority of MMAC compared to traditional adaptive control. At first, different kind of adaptive control model are introduced into the models set. Then the fixed models in the models set are redistributed. At last, a new method of switching is used based on the adaptive control with the passing of parameters. Combined with sub plant location of the whole models set, it can improve the output performance of the object. Through the simulation, the objects have a better performance with the same number of models, which have covered the all range of the change of parameters.