Hierarchical control for multi-domain coordination of vehicle energy systems with switched dynamics

This dissertation presents a hierarchical control framework for vehicle energy management. As a result of increasing electrification, legacy integration and control approaches for vehicle energy systems have become limiting factors of performance and cannot accommodate the requirements of next-generation systems. Addressing this requires control frameworks that coordinate dynamics across multiple physical domains and timescales, enabling transformative improvements in capability, efficiency, and safety. To capture multi-domain storage and exchange of energy, a graph-based dynamic modeling approach is proposed and experimentally validated. This modeling approach is then leveraged for model-based control, in which the complex task of energy management is decomposed into a hierarchical network of model predictive controllers that coordinate decision-making across subsystems, physical domains, and timescales. The controllers govern both continuous and switched dynamic behaviors, addressing the hybrid nature of modern vehicle energy systems. The proposed hierarchical control framework is evaluated in application to a hardware-in-the-loop electro-thermal testbed representative of a scaled aircraft energy system, where it achieves significantly improved capability, efficiency, and safety as compared to legacy control approaches. Next, the structural information embedded in the graph-based modeling approach is shown to facilitate analysis. Closed-loop stability of decentralized MPC frameworks is guaranteed by analyzing the passivity of switched nonlinear graph-based systems and augmenting their controllers with a local passivity-based constraint. Lastly, a hierarchical control formulation guaranteeing satisfaction of state and input constraints for a class of switched graph-based systems is presented. This formulation is demonstrated in application to thermal management using both simulation and experimental implementation

Dynamic modeling, validation, and control for vapor compression systems

This thesis traces the complete process of model-based control design for vapor compression systems (VCSs), from nonlinear model development to linearization and control formulation. Addressing gaps in the previous literature, the equations behind each model and control approach are clearly stated and emphasis is placed on conducting experimental validation at every stage. Both finite volume and switched moving boundary approaches for nonlinear control-oriented heat exchanger modeling are presented, illustrating the key differences in the method of discretization between these approaches. Practical considerations for the numerical implementation of these approaches in simulation are also provided. A detailed linearization of the switched moving boundary approach leads to the creation of a family of four-component linear models for different modes of operation of a VCS. The nonlinear and linear models are then validated with experimental data to reveal the tradeoffs of each. Furthermore, an augmentation to the switched moving boundary method is derived which captures the effects of air humidity. Experimental validation demonstrates that this augmented model more accurately predicts both air-side and refrigerant-side outputs at high humidity in addition to providing accurate predictions of liquid condensate formation and air outlet humidity. Finally, the value of the linear VCS models is demonstrated by their application in model-based control. A switched LQR approach is shown in both simulation and experimental application to be capable of driving the system between operational modes in order to regulate about a desired nominal operating condition. In particular, the experiments demonstrate improved robustness at low evaporator superheat of the switched LQR approach as compared to a decentralized PI approach

A Set-Based Approach for Robust Control Co-Design

Control Co-Design (CCD) considers the coupled effects of both the plant and control parameters to optimize a system's closed-loop transient performance during the design stage. This paper presents a new method for CCD with guarantees on robustness to nondeterministic disturbances for all initial conditions within a specified region of operation. This is accomplished by calculating the reachable sets of a candidate closed-loop system directly within the optimization problem. Using this approach, the plant and control parameters are simultaneously chosen to shape these reachable sets to be robustly positive invariant and thus safe for all time. Compared to conventional approaches that perform the optimization for a single initial condition and an a priori chosen sequence of disturbances, the proposed set-based method avoids sensitivity to variations in the assumed design scenario. As a representative example, the proposed method is applied to an active suspension system.Comment: 8 pages, 4 figure

Set-valued State Estimation for Nonlinear Systems Using Hybrid Zonotopes

This paper proposes a method for set-valued state estimation of nonlinear, discrete-time systems. This is achieved by combining graphs of functions representing system dynamics and measurements with the hybrid zonotope set representation that can efficiently represent nonconvex and disjoint sets. Tight over-approximations of complex nonlinear functions are efficiently produced by leveraging special ordered sets and neural networks, which enable computation of set-valued state estimates that grow linearly in memory complexity with time. A numerical example demonstrates significant reduction of conservatism in the set-valued state estimates using the proposed method as compared to an idealized convex approach