Control Co-Design With Varying Available Information Applied to Vehicle Suspensions

Abstract Recent optimization strategies for Control Co-Design (CCD) often utilize open-loop optimal control (OLOC) to explore the physical performance limits of actively controlled engineering systems. For most real systems, however, closed-loop control (CLC) is required for implementation. The physical (plant) design generated by an OLOC CCD method will normally not interact optimally with CLC, producing results that are not system optimal. In this article, an intuitive strategy is presented for investigating empirically the impact of information availability on CCD optimization results. Model predictive control (MPC) provides a flexible means to vary what information is used in making real-time control decisions. This is used as a proxy for the vast space of potential controllers, from simple to sophisticated. This method for studying information-based characteristics of CCD problems is demonstrated using a canonical CCD problem based on an active automotive suspension problem. Different plant architectures with various plant design variables are considered. Results show that varying the amount of information in the control design yields different plant designs and different objective values, and has the potential to yield insights into promising CLC architectures (beyond MPC), fruitful directions to head for plant design, and a deeper understanding of the interface between physical and control system design. This article introduces the concept of information-based studies in CCD, but utilizes an applied approach based on MPC to generate insights. A more theoretical approach could be taken in the future that yields a more generalizable understanding of how information limitations influence CCD optimization outcomes.