A Design Framework with Embedded Hierarchical Control Architecture Optimization

Abstract

This paper presents a design framework for the optimization of hierarchical model predictive control (MPC) architectures and parameters. Hierarchical MPC is a powerful tool to balance objective trade-offs when controlling large dynamic systems composed of heterogeneous components. However, the multi-layer structure of the controller requires decomposition of the system and tuning of a large number of control parameters. The literature presents methods to identify optimal distributed and hierarchical control architectures, but these studies do not optimize MPC parameters concurrently with the architecture. This paper addresses this gap by embedding hierarchical MPC into a control co-design (CCD) optimization problem. Facilitated by a novel hierarchical control strategy for passing information between layers, the architecture and MPC parameters (e.g., timesteps, horizons) are simultaneously optimized. The design approach is tested on a model of an electric vehicle (EV) powertrain with cooling. The hierarchical control strategy is compared against a baseline control approach, and the behavior of different hierarchical architecture options are compared using performance- and energy-based metrics.