基于约束上下文贝叶斯优化（C-CMES）的车辆座舱气候MPC参数整定
Vehicle Cabin Climate MPC Parameter Tuning Using Constrained Contextual Bayesian Optimization (C-CMES)
AI总结：
针对车辆多区域座舱气候MPC因外部流量变化导致性能下降的整定难题，提出基于约束上下文贝叶斯优化的流量依赖鲁棒参数整定方法，含后处理平滑步骤，仿真验证其性能优于恒定参数化且超调更低、行为更一致。
摘要：
几十年来，具备气候控制功能的座舱一直是车辆的标准配置。模型预测控制器（MPCs）在实现车辆座舱温度追踪方面已展现出可观效果，且有望提升无模型控制的性能。然而在多区域气候控制场景中，控制器的合理整定颇具挑战：外部因素（例如乘客触发的压缩机设置变化及由此导致的质量流量变化）会使控制性能下降。\n本文提出一种整定方法，可根据鼓风机质量流量自动确定鲁棒MPC参数。该方法采用约束上下文贝叶斯优化（BO）推导策略，在设定场景下满足约束条件的同时最小化高层代价函数。所提方法在训练回合中利用随机扰动和模型-对象失配，生成具备鲁棒抗扰能力的控制器参数，还包含一个后处理步骤以生成可用于实际应用的平滑策略。\n仿真结果表明：首先，依赖质量流量的策略在实现预期闭环行为的同时，性能优于恒定参数化方案；其次，该鲁棒整定方法大幅降低了最坏情况下的超调量，在不同运行条件下均可产生一致的闭环行为。
英文摘要：
Climate-controlled cabins have for decades been standard in vehicles. Model Predictive Controllers (MPCs) have shown promising results in achieving temperature tracking in vehicle cabins and may improve upon model-free control performance. However, for the multi-zone climate control case, proper controller tuning is challenging, as externally, e.g., passenger-triggered changes in compressor setting and thus mass flow lead to degraded control performance. This paper presents a tuning method to automatically determine robust MPC parameters, as a function of the blower mass flow. Constrained contextual Bayesian optimization (BO) is used to derive policies minimizing a high-level cost function subject to constraints in a defined scenario. The proposed method leverages random disturbances and model-plant mismatch within the training episodes to generate controller parameters achieving robust disturbance rejection. The method contains a postprocessing step to achieve smooth policies that can be utilized in real-world applications. First, simulation results show that the mass flow-dependent policy outperforms a constant parametrization, while achieving the desired closed-loop behavior. Second, the robust tuning method greatly reduces worst-case overshoot and produces consistent closed-loop behavior under varying operating conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
