【摘要】 针对多变量系统中多个参数同时变化导致模型数目巨大,计算时间长等问题,提出了采用逐维定位的多模型自适应解耦控制器．该方法将多维空间的并行寻优问题转化为多个一维空间的串行寻优问题,每一次固定其他参数、只针对一个参数寻找最优模型,可大大减少系统模型集的数量．该控制器基于性能指标搜索最优模型,通过加权多项式矩阵的选择,不但消除了稳态误差,任意配置闭环系统的极点,而且实现了动态解耦控制．最后给出全局收敛性分析．仿真结果表明当采用相同的固定模型覆盖每个参数的变化区间时,其模型集的数目远远小于常规多模型控制器．而当采用相同数目的模型时,其控制效果明显优于常规多模型控制器．更多还原

【Abstract】 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.更多还原