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Multi-scenario optimization approach for fuzzy control of a robot-car model

机译:机器人模型模糊控制的多场景优化方法

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A simple dynamic model of a robot-car has been built in the Matlab/Simulink environment [1], which expanded with a minimal dynamic part [2]. A fuzzy route controller was developed and its performance compared to the PID control [2]. The multi-scenario simulation with five different spatial target points is using in order to represent the all expected scenarios during the controller optimization. The multi-objective fitness evaluation of the driving has also been developed based on kinematic and dynamic characteristics. The optimization of the Fuzzy route controller is performed on the multi-scenario simulation using previously implemented heuristic optimization methods [3]. The multi-scenario optimum is compared with the single-scenario optimums, and evaluated in that way.
机译:在Matlab / Simulink环境[1]中建立了一个简单的机器人汽车动力学模型,该模型以最小的动态零件[2]进行了扩展。开发了一种模糊路径控制器,并将其性能与PID控制进行比较[2]。为了表示控制器优化过程中所有预期的场景,正在使用具有五个不同空间目标点的多场景仿真。还基于运动学和动态特性开发了驾驶的多目标适应性评估。模糊路由控制器的优化是使用先前实现的启发式优化方法在多场景仿真中执行的[3]。将多方案最优方案与单方案最优方案进行比较,并以此方式进行评估。

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