  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.
