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.

翻译：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，GA搜索最优控制输入时所处的搜索空间尺寸对其在快速响应系统中的适用性至关重要。本文提出通过学习最优搜索空间尺寸来加速NMPC的遗传优化。该方法训练一个多元回归模型，以自适应地预测每个控制周期中最佳的最小搜索空间尺寸。所提方法减少了GA的计算时间，提高了收敛至更优控制输入的可能性，并能提供稳定可行的解。所提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%。源代码已在GitHub上公开。