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A New Solving Method for Non-Linear Optimal Control Problem and Its Application to Automatic Berthing Problem

机译:非线性最优控制问题的一种新求解方法及其在自动泊位问题中的应用

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There are many researches for solving optimal control problems in control engineering, but the obtained input pattern is far differ from that of experienced operator or difficult to implement for real actuator. For example, the minimum-time optimal control solution becomes bang-bang type shape which cannot be implemented by slow actuator. In this paper, we consider a new solving method for optimal control problem with input pattern assignment using genetic algorithm and simulation method. Genetic algorithm is one of the evolutional algorithm to obtain numerical solution for optimization problem. However, the computational time using normal CPU is not sufficiently short for large scale genetic algorithm in which forward simulations of system dynamics are performed during sampling period. For this problem, the high performance GPU in recent years makes it possible to perform parallel genetic algorithm and simulation in very short time. The proposed algorithm is successfully applied to real system.
机译:解决控制工程中最优控制问题的研究很多,但是所获得的输入模式与有经验的操作员相差甚远,或者对于实际的执行器来说很难实现。例如,最短时间的最优控制解决方案变成慢速致动器无法实现的爆炸式形状。在本文中,我们考虑了一种使用遗传算法和模拟方法求解输入模式分配的最优控制问题的新方法。遗传算法是获得优化问题数值解的进化算法之一。但是,对于大规模遗传算法而言,使用常规CPU的计算时间还不够短,在大规模遗传算法中,在采样期间进行系统动力学的正向模拟。对于这个问题,近年来的高性能GPU使得在很短的时间内执行并行遗传算法和仿真成为可能。该算法成功应用于实际系统。

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