Abstract
Abstract Control Co-Design (CCD) has been demonstrated to achieve superior solutions for closed-loop systems. However, limited work has addressed CCD problems under probabilistic disturbances. This paper addresses this gap by formulating a finite-horizon optimal control problem with chance constraints and proposing a novel CCD approach. This approach integrates tube-based Stochastic Model Predictive Control with constraint-tightening techniques to optimize performance and robustness while preventing instability and infeasibility. A nested CCD framework is introduced, along with a constrained multi-objective optimization algorithm that enables the performance-robustness trade-off. A method for quantifying the robustness of closed-loop systems under stochastic disturbances is presented. The proposed CCD approach is demonstrated on a numerical example and an engineering case of the satellite attitude control system. Results show that CCD can generate more well-spread Pareto fronts that cannot be reached by other design strategies. This helps designers explore more potential solutions with different dynamic characteristics. Selected non-dominated solution trajectories are visualized for qualitative comparisons. Future work will extend this to nonlinear applications.
Published Version
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