通过学习最优搜索空间大小加速非线性模型预测控制的遗传优化
Accelerating genetic optimization of nonlinear model predictive control by learning optimal search space size
AI总结：
本文提出通过学习最优搜索空间大小来加速非线性模型预测控制的遗传优化，利用多元回归模型自适应预测搜索空间尺寸，减少计算时间17-45%，提高收敛率35-47%，并在三个非线性系统上验证了有效性。
摘要：
遗传算法（GA）通常用于求解非线性模型预测控制的优化问题。然而，遗传算法搜索最优控制输入的搜索空间大小对其在快速响应系统上的适用性至关重要。本文提出通过学习最优搜索空间大小来加速非线性模型预测控制的遗传优化。该方法训练一个多元回归模型，在每个控制周期内自适应地预测搜索空间的最佳最小尺寸。所提出的方法减少了遗传算法的计算时间，提高了收敛到更好控制输入的机会，并提供稳定可行的解决方案。该方法在三个非线性系统上进行了评估，并与以处理器在环方式实现的四种其他进化算法进行了比较。结果表明，所提出的方法将计算时间减少了17-45%，并将收敛率提高了35-47%。源代码可在GitHub上获取。
英文摘要：
Genetic algorithm (GA) is typically used to solve nonlinear model predictive control's optimization problem. However, the size of the search space in which the GA searches for the optimal control inputs is crucial for its applicability to fast-response systems. This paper proposes accelerating the genetic optimization of NMPC by learning optimal search space size. The approach trains a multivariate regression model to adaptively predict the best smallest size of the search space in every control cycle. The proposed approach reduces the GA's computational time, improves the chance of convergence to better control inputs, and provides a stable and feasible solution. The proposed approach was evaluated on three nonlinear systems and compared to four other evolutionary algorithms implemented in a processor-in-the-loop fashion. The results show that the proposed approach provides a 17-45\% reduction in computational time and increases the convergence rate by 35-47\%. The source code is available on GitHub.
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