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Fuzzy Cauchy problem for fuzzy estimation — Application to unicycle-type mobile robots

机译:用于模糊估计的模糊柯西问题—在单轮移动机器人中的应用

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This paper is an attempt to provide a solution to the problem of state estimation of dynamical systems, when no measured output is available, through the example of a unicycle-type mobile robot. The latter is only equipped with encoders on each wheel which provide information only about the system's inputs (see equations (1) and (2)). As is the case in most real situations, in this work, it is also assumed that the initial states are only “approximately” known and we represent them by fuzzy numbers. Fuzzy initial states along with system dynamic equations provide us particular fuzzy differential equations (FDEs), referred as fuzzy Cauchy problem in the literature [7], [5], [4]. The question to which we want to provide an answer is: Knowing the “fuzzy states” of the system at t=0, are we able to estimate them at any time t>0 or, equivalently, starting from the fuzzy initial states, are we able to build the solution of the fuzzy Cauchy problem at any time t>0 ? To solve this problem we consider two approaches: First, solving the robot's crisp (non fuzzy) differential equations with fuzzy initial conditions. In the second, the differential equations themselves are considered as fuzzy and, after discretization, the solution is built step by step using mainly fuzzy arithmetics. Moreover, considering encoders' inherent imprecision, we also assume that the input signals are only approximately known and represent them by fuzzy maps (functions having fuzzy numbers as instantaneous values). We however always make the assumption that the robot wheels do not slip.
机译:本文尝试通过单轮型移动机器人的示例为动态系统状态估计问题提供解决方案,当没有测量输出可用时。后者在每个车轮上仅配备有编码器,这些编码器仅提供有关系统输入的信息(请参见方程式(1)和(2))。与大多数实际情况一样,在此工作中,还假定初始状态仅“近似”已知,我们用模糊数表示它们。模糊初始状态与系统动力学方程一起为我们提供了特定的模糊微分方程(FDE),在文献[7],[5],[4]中称为模糊柯西问题。我们要提供答案的问题是:知道t = 0时系统的“模糊状态”,我们是否能够在t> 0的任何时间估计它们,或者等效地从模糊初始状态开始,我们能够在t> 0的任何时间建立模糊柯西问题的解决方案吗?为了解决这个问题,我们考虑两种方法:首先,用模糊的初始条件求解机器人的清晰(非模糊)微分方程。第二,将微分方程本身视为模糊的,离散化之后,主要使用模糊算法逐步建立解决方案。此外,考虑到编码器的固有不精确性,我们还假设输入信号仅是近似已知的,并由模糊映射表(具有模糊数作为瞬时值的函数)表示。但是,我们始终假设机器人的车轮不会打滑。

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