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为了解决Smith预估控制算法建立模型不精确的问题,并针对一类可重复运行的时滞过程,在模糊相轨迹模型Smith预估控制算法的基础上提出迭代模型Smith预估控制。该算法在不设计自适应的前提下,可以自适应得到精确的预估模型,同时辨识过程的时滞时间。证明指出只要原系统闭环稳定则迭代预估模型Smith预估控制系统稳定,且给出了算法的收敛性判据。仿真表明,所提方法不需要已知过程的模型,通过一定次数的“迭代”即可得到精确的预估模型,从而克服了常规算法控制品质依赖精确数学模型的缺陷。同时该算法具有较强的鲁棒性。
In order to solve the problem of inaccurate modeling of Smith predictive control algorithm, and for a class of repeatable running processes, an iterative model Smith predictive control is proposed based on the Smith predictive control algorithm of fuzzy phase trajectory model. The algorithm can get an accurate prediction model adaptively without any adaptive design and identify the time lag of the process. It is proved that as long as the closed-loop stability of the original system is stable, the predictive model of the iterative estimation model Smith is stable and the convergence criterion of the algorithm is given. The simulation shows that the proposed method does not need the known process model, and the accurate prediction model can be obtained by a certain number of “iterations”, thus overcoming the defect that the control quality of the conventional algorithm depends on the accurate mathematical model. At the same time, the algorithm has strong robustness.