AI 中文总结
研究基于梯度优化算法在非线性模型预测控制中的应用，提出搜索与加速（SaA）算法，结合多种机制，经600个盒约束优化问题实例测试性能良好，对参数选择鲁棒，还能用于NMPC实施减少控制更新周期。
AI 中文摘要
本文讨论了基于梯度的优化算法的相关方面，特别关注其在非线性模型预测控制实施中的应用要求。通过专门讨论，提出了一种名为搜索与加速（SaA）的新算法，它将新型线搜索、信赖域机制与梯度加速方案的调整相结合。设计了一个包含600个盒约束优化问题实例的专门基准测试来展示该算法性能，其对定义中少量参数选择具有鲁棒性，默认值适用于任何问题。此外，给出了该算法在NMPC实施中的应用示例，表明有可能减少控制更新周期。
英文摘要
This paper discusses some aspects related to gradient-based optimization algorithms with special focus on the requirements associated to their use in the implementation of Nonlinear Model Predictive Control. Based on a dedicated discussion, a new algorithm, termed Search and Accelerate (SaA) is proposed that mixes together a novel line search, a trust region mechanism together with an adaptation of the gradient acceleration scheme. A dedicated benchmark involving a set of 600 instances of box constrained optimization problems is designed and used in order to show the algorithm performances which make it a highly competitive general purpose gradient-based alternative for box-constrained optimization problems. An appealing feature of the algorithm is its robustness to the choice of the few parameters involved in its definition making the default values a valid option for any problem without a priori knowledge of the related Lipchitz constant. Moreover, an example of use of the proposed algorithm in NMPC implementation is proposed showing the possibility to reduce the control updating period which might be mandatory in some circumstances.
Comments15 pages, 14 Figures
