Integrating Learning-Based and MPC-Based Control for PWA Systems
Challenges and Opportunities
Azita Dabiri (TU Delft - Team Azita Dabiri)
Kanghui He (TU Delft - Team Bart De Schutter)
Shengling Shi (TU Delft - Team Raf Van de Plas)
Dingshan Sun (TU Delft - Traffic Systems Engineering)
Jesus Lago (Amazon.com Inc.)
Bart De Schutter (TU Delft - Delft Center for Systems and Control)
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Abstract
Learning-based control, in particularReinforcement Learning (RL) reinforcementReinforcement learning, and optimization-based control, in particular model predictive control, each have their advantages and disadvantages for online, real-timeOptimal control optimal controlOptimal control of systems with complex dynamicsDynamic. However, both approaches are highly complementary and therefore there is an increased interest in combining their advantages in an integrated approach. In this chapter, we provide an overview of recent results, challenges, and opportunities on an integrated learning-based and optimization-based control approach. We focus in particular on piecewise affine systems as they are an extension of linear systemsLinear systems that can model or approximate hybridHybrid or nonlinearNonlinearbehaviorBehavior and as they still allow for effective numerical solutionSolution approaches.