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A Nonlinear Programming Method for Time-Optimal Control of an Omni-Directional Mobile Robot

机译:全向移动机器人时间最优控制的非线性规划方法

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摘要

The time-optimal control problem of a three-wheeled omni-directional mobile robot is studied in this paper. In contrast to traditional methods, in which the Pontryagin's Minimum Principle is usually used, an iterative procedure is proposed to transform the time-optimal problem into a nonlinear programming (NLP) one. In the formulated NLP problem, the number of control steps is fixed initially and the sampling period is treated as a variable to be minimized in the optimization process. An upper limit on the sampling period is set in advance considering the accuracy of discretization. If the value of the sampling period is larger than the upper limit, then an update of the number of control steps is needed. To generate initial feasible solutions of the NLP problem, a genetic algorithm is adopted. Since different initial feasible solutions can be generated, the optimization process can be started from different points to find the optimal solution. In this manner, one can find a time-optimal movement of the omni-directional mobile robot between two configurations. To show the feasibility of the proposed method, simulation results are included for illustration.
机译:研究了三轮全向移动机器人的时间最优控制问题。与通常使用Pontryagin最小原理的传统方法相比,提出了一种将时间最优问题转换为非线性规划(NLP)的迭代过程。在公式化的NLP问题中,控制步骤的数量最初是固定的,并且采样周期被视为要在优化过程中最小化的变量。考虑到离散化的精度,预先设置采样周期的上限。如果采样周期的值大于上限,则需要更新控制步骤的数量。为了产生NLP问题的初始可行解,采用了遗传算法。由于可以生成不同的初始可行解,因此可以从不同点开始优化过程以找到最佳解。以此方式,可以找到全向移动机器人在两种配置之间的最佳时间移动。为了显示所提出方法的可行性,包括仿真结果以进行说明。

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