机器人-汽车模型模糊控制的多场景优化方法

2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics Pub Date : 2015-05-21 DOI:10.1109/SACI.2015.7208167

I. Kecskés, P. Odry

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引用次数: 4

摘要

在Matlab/Simulink环境下建立了机器人汽车的简单动态模型[1]，并将其扩展为最小动态部分[2]。开发了一种模糊路径控制器，并将其性能与PID控制进行了比较[2]。采用5个不同空间目标点的多场景仿真，以表示控制器优化过程中所有预期的场景。基于运动学和动力学特性，提出了多目标适应度评价方法。模糊路径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进行的[3]。将多场景优化方案与单场景优化方案进行了比较，并进行了评价。

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

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.

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2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics

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