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Climate-controlled cabins have for decades been standard in vehicles. ModelPredictive Controllers (MPCs) have shown promising results in achievingtemperature tracking in vehicle cabins and may improve upon model-free controlperformance. However, for the multi-zone climate control case, propercontroller tuning is challenging, as externally, e.g., passenger-triggeredchanges in compressor setting and thus mass flow lead to degraded controlperformance. This paper presents a tuning method to automatically determinerobust MPC parameters, as a function of the blower mass flow. Constrainedcontextual Bayesian optimization (BO) is used to derive policies minimizing ahigh-level cost function subject to constraints in a defined scenario. Theproposed method leverages random disturbances and model-plant mismatch withinthe training episodes to generate controller parameters achieving robustdisturbance rejection. The method contains a postprocessing step to achievesmooth policies that can be utilized in real-world applications. First,simulation results show that the mass flow-dependent policy outperforms aconstant parametrization, while achieving the desired closed-loop behavior.Second, the robust tuning method greatly reduces worst-case overshoot andproduces consistent closed-loop behavior under varying operating conditions.

Vehicle Cabin Climate MPC Parameter Tuning Using Constrained Contextual Bayesian Optimization (C-CMES)