Nonlinear model predictive control (NMPC) solves a multivariate optimization problem to estimate the system's optimal control inputs in each control cycle. Such optimization is made more difficult by several factors, such as nonlinearities inherited in the system, highly coupled inputs, and various constraints related to the system's physical limitations. These factors make the optimization to be non-convex and hard to solve traditionally. Genetic algorithm (GA) is typically used extensively to tackle such optimization in several application domains because it does not involve differential calculation or gradient evaluation in its solution estimation. However, the size of the search space in which the GA searches for the optimal control inputs is crucial for the applicability of the GA with systems that require fast response. This paper proposes an approach to accelerate the genetic optimization of NMPC by learning optimal search space size. The proposed approach trains a multivariate regression model to adaptively predict the best smallest search space in every control cycle. The estimated best smallest size of search space is fed to the GA to allow for searching the optimal control inputs within this search space. The proposed approach not only reduces the GA's computational time but also improves the chance of obtaining the optimal control inputs in each cycle. The proposed approach was evaluated on two nonlinear systems and compared with two other genetic-based NMPC approaches implemented on the GPU of a Nvidia Jetson TX2 embedded platform in a processor-in-the-loop (PIL) fashion. The results show that the proposed approach provides a 39-53\% reduction in computational time. Additionally, it increases the convergence percentage to the optimal control inputs within the cycle's time by 48-56\%, resulting in a significant performance enhancement. The source code is available on GitHub.

翻译：非线性模型预测控制（NMPC）通过求解多变量优化问题，在每个控制周期内估算系统的最优控制输入。此类优化因系统固有非线性、强耦合输入以及与物理约束相关的多种限制而更具复杂性，导致优化问题呈非凸特性且难以通过传统方法求解。遗传算法（GA）因其在解估计过程中无需微分计算或梯度评估，常被广泛用于多个应用领域中的此类优化问题。然而，遗传算法搜索最优控制输入时的搜索空间大小，对于需要快速响应的系统，其适用性至关重要。本文提出一种通过学习最优搜索空间大小来加速NMPC遗传优化的方法。该方法训练一个多变量回归模型，以自适应预测每个控制周期中最佳的最小搜索空间。将估计出的最佳最小搜索空间尺寸输入遗传算法，使其在该搜索空间内寻找最优控制输入。该方法不仅降低了GA的计算时间，还提升了每个控制周期中获得最优控制输入的概率。我们在两个非线性系统上对所述方法进行了评估，并与基于NVIDIA Jetson TX2嵌入式平台GPU、以处理器在环（PIL）方式实现的另外两种基于遗传算法的NMPC方法进行对比。结果表明，本方法可将计算时间降低39-53%，并在控制周期内将收敛到最优控制输入的比率提升48-56%，从而显著提升性能。相关源代码已在GitHub上公开。