Reliability-based co-design and its applications to wind energy and mobile energy storage systems

Abstract

Autonomous systems, such as autonomous driving vehicles, unmanned aerial vehicles (UAVs), and field robots, received much attentions recently. The performance of autonomous systems relies on both its physical design and the appropriate control strategies, which often takes place at an early stage of design. The plant design and the control design are strongly coupled. Neglecting this coupling effect may cause an imbalance in the feasible design spaces of plant design and control design, such as over-constrained operation conditions, over design, or requirement of skilled operators, which hinders the development of autonomous systems. On the other hand, the products are manufactured goods and usually operate in environments with uncertainty. Reliable operation of such systems ask for balanced physical design and feasible control decisions to address the parametric uncertainty and stochastic environmental disturbances. While integrated physical and control system co-design has been demonstrated successfully on several engineering system design applications, it has been primarily applied in a deterministic manner without considering uncertainties. An opportunity exists to study non-deterministic co-design strategies, taking into account various uncertainties in an integrated co-design framework. While significant advancements have been made in co-design and RBDO separately, little is known about methods where reliability-based dynamic system design and control design optimization are considered jointly. In this research, we investigate optimal design and control of dynamical systems with model parametric uncertainties, which presumably operate in uncertain environments. Techniques in control co-design (CCD) and reliability-based design optimization (RBDO) are adapted and integrated to solve the proposed problem. Since the proposed method adopts the idea of multi-disciplinary design optimization, it can improve the performance of autonomous systems without leveraging the difficulty in design and control for systems with uncertainties. First, the problem formulation and strategies to solve the reliability-based control co-design problem is presented. A comparison of accuracy and efficiency is made using numerical and simple engineering case studies. The method is then applied to a horizontal axis wind turbine. The uncertain wind load and model parameters of a wind turbine are compensated through active control or endured by a reliable design regarding its aerodynamics and structural dynamics. Different strategies of reliability assessment are also compared, which provides insights on their advantages and limits under different cases. In the second application, reliability-based control co-design is applied to Lithium-ion battery. The electrode and charging current are optimized to minimize its charging time while regulating its aging effect for reasonable cycle life. The multi-scale nature of the problem requires first principle model to preserve the coupling effect between electrode design at the micro scale and the charging control at the macro scale. However, it is not feasible to use the first principle model for control optimization. A hybrid physics and machine learning strategy is proposed in this work, which extends the applicability of reliability-based control co-design to multi-scale problems.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste