基于性能预测的遗传强化学习动态调度方法

针对作业车间动态调度问题,在模式驱动调度的框架下,提出遗传强化学习动态调度方法。首先,采用优先规则编码的染色体表达问题的解,将染色体分割成基因模式作为分阶段调度算法的状态模式;其次,设计性能预测变量,构建启发式立即回报函数,引导和加快遗传强化学习算法的搜索进程;再次,设置遗传算子、强化学习及其相关参数以实现搜索过程＂开采＂与＂探索＂之间的平衡;最后,仿真实验结果验证了遗传强化学习调度方法的有效性。

Abstract：

In the framework of pattern driven scheduling,a genetic reinforcement learning （GRL） approach to schedule the job in the dynamical job-shop was proposed.First,the chromosome was coded by preference rules-based representation for the problem.The chromosome was divided into gene schema as state patterns for the multi-phase scheduling system.Secondly,a performance predictive variable to construct instant reward function was designed which was used to guide the learning system to progress rapidly.Thirdly,genetic operators,RL and controlling parameters carried out the search strategy for the balance of ＂exploration＂ and ＂exploitation＂.Finally,the simulation results verify the efficiency of GRL scheduling approach.     

轮式移动机器人沙地行驶控制建模与仿真研究

结合车轮沙土相互作用的力学分析,研究解决轮式移动机器人沙地行驶车轮过度滑转下陷问题.考虑纵列式重复通过车轮沙土力学参数的修正,建立六轮式沙地移动机器人的动力学模型,以车轮滑转率为状态变量,设计了移动机器人沙地行驶的滑模驱动控制器跟踪车轮期望滑转率.MATLAB/Simulink仿真结果表明,采用该控制器可以较快地跟踪期望滑转率,有效地限制机器人车轮的滑转,避免车轮的过度下陷.