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Cumulative retrospective cost adaptive control with RLS-based optimization

机译:基于RLS优化的累积追溯成本自适应控制

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We present a discrete-time adaptive control algorithm that is effective for multi-input, multi-output systems that are either minimum phase or nonminimum phase. The adaptive control algorithm requires limited model information, specifically, the first nonzero Markov parameter and the nonminimum-phase zeros of the transfer function from the control signal to the performance measurement. Furthermore, the adaptive control algorithm is effective for stabilization as well as command following and disturbance rejection, where the command and disturbance spectrum is unknown. The novel aspect of this adaptive controller is the use of a retrospective performance function which is optimized using a recursive leastsquares algorithm.
机译:我们提出了一种离散时间自适应控制算法,该算法对于最小相位或非最小相位的多输入多输出系统均有效。自适应控制算法需要有限的模型信息,特别是从控制信号到性能测量的传递函数的第一非零马尔可夫参数和非最小相位零。此外,在命令和干扰谱未知的情况下,自适应控制算法对于稳定以及命令跟随和干扰抑制都是有效的。这种自适应控制器的新颖之处在于使用了追溯性能函数,该函数使用递归最小二乘算法进行了优化。

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