Multiple-model adaptive decoupling controller employing dimension-by-dimension approach
采用逐维定位的多模型自适应解耦控制器

In a multi-variable system, when multiple parameters jump simultaneously, a multiple-model adaptive decoupling controller (MMADC) employing dimension-by-dimension (DBD) approach is presented to solve the problems of many models, long computing time and so on. To find the optimal parameter, it adopts one-dimension optimization methods in series instead of multiple-dimension optimization methods in parallel. At any time only one parameter is focused to find the optimal value and other parameters are kept constant, which can reduce the number of the system fixed models greatly. Based on the switching index, the best model is chosen and the controller is designed accordingly. By choosing of the weighting polynomial matrix, it eliminates the steady output error and places the poles of the closed loop system arbitrarily, but also decouples the system dynamically. The global convergence is eventually obtained. In the simulation example, when compared with the conventional multiple-model adaptive controller, it reduces the number of the models greatly. If the same number of the fixed models is used, system transient response and decoupling result are improved.