基于进化算法的最优控制系统——控制输入范围估计

V. Mînzu, Iulian Arama
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引用次数: 4

摘要

采用后退水平控制(RHC)结构的闭环最优控制系统基于过程模型(PM)进行预测,计算出当前的控制输出。在许多应用中,当前预测范围内的最佳预测是使用元启发式算法计算的,例如进化算法(EA)。与其他基于群体的元启发式方法一样,ea具有很大的计算复杂性。当集成到控制器中时,EA在每个采样时刻执行,并受到时间约束:执行时间应小于采样周期。本文提出了一个集成在控制器中的软件模块,称为每个采样时刻。模块使用PM集成估计未来过程状态,在短时间范围内,对于覆盖给定技术间隔的不同控制输入值。根据所谓的“状态质量标准”,只有一个较窄的间隔被选择为过程的“良好”演变。对于当前采样周期,控制器将只考虑缩小的控制输出范围。EA将在较小的范围内寻找其最佳预测,而不会导致聚合受到影响。仿真结果表明,该控制器的计算复杂度大大降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Control Systems Using Evolutionary Algorithm-Control Input Range Estimation
The closed-loop optimal control systems using the receding horizon control (RHC) structure make predictions based on a process model (PM) to calculate the current control output. In many applications, the optimal prediction over the current prediction horizon is calculated using a metaheuristic algorithm, such as an evolutionary algorithm (EA). The EAs, as other population-based metaheuristics, have large computational complexity. When integrated into the controller, the EA is carried out at each sampling moment and subjected to a time constraint: the execution time should be smaller than the sampling period. This paper proposes a software module integrated into the controller, called at each sampling moment. The module estimates using the PM integration the future process states, over a short time horizon, for different control input values covering the given technological interval. Only a narrower interval is selected for a ‘good’ evolution of the process, based on the so-called ‘state quality criterion’. The controller will consider only a shrunk control output range for the current sampling period. EA will search for its best prediction inside a smaller domain that does not cause the convergence to be affected. Simulations prove that the computational complexity of the controller will decrease.
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