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Applying incremental best estimate directed search to optimize fuzzy logic controllers for a ball-and-beam system

机译:应用增量最佳估算定向搜索以优化球束系统的模糊逻辑控制器

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Incremental Best Estimate Directed Search (IBEDS) is a computational controller optimization algorithm developed by the authors in the last few years. It is a very fast and effective off-line controller parameter search method. IBEDS starts with an initial training set that could be obtained through the sampling of the control surface of a primitive controller, or just an empty set. Using the Least Mean Square (LMS) learning algorithm with the training set, another controller with randomly initialized parameters is trained in an iterative procedure. In each iteration, the trained controller is evaluated with cell state space based global and local performance measures. The training set is then updated based on the evaluation with Best Kept Policy. In this way, the training set is optimized incrementally, and the controller trained by the training set is also optimized progressively. IBEDS has been found to have faster convergence speed over other computational method with an inverted pendulum as example. This paper reports the simulation results of applying IBEDS to Fuzzy Logic Controller (FLC) optimization for a ball and beam system. It is shown that it is much easier to control a 4 dimensional ball and beam system than to control a 4 dimensional inverted pendulum system. The results also reveal that all the features of IBEDS that have been found so far on the inverted pendulum example remain the same with the ball and beam system.
机译:增量最佳估算定向搜索(IBEDS)是作者在过去几年中开发的计算控制器优化算法。它是一种非常快速有效的离线控制器参数搜索方法。 IBEDS从一个初始训练集开始,可以通过原始控制器的控制表面的采样来获得,或者只是空集。使用具有训练集的最小均方(LMS)学习算法,在迭代过程中训练具有随机初始化参数的另一个控制器。在每次迭代中,培训的控制器采用基于小区状态空间的全局和本地性能测量评估。然后根据具有最佳保留策略的评估更新训练集。以这种方式,训练集逐步优化,并且由训练集训练的控制器也逐步优化。已发现IBEDS在其他计算方法中具有更快的收敛速度,其中具有倒挂摆在示例中。本文报道了将IBEDS应用于球和光束系统的模糊逻辑控制器(FLC)优化的仿真结果。结果表明,控制4维球和光束系统比控制4维倒立摆系统更容易。结果还揭示了到目前为止在倒立的摆锤示例上发现的所有特征与球和光束系统相同。

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