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

Abstract

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.