The design of stabilizing controllers for general nonlinear systems remains a challenging task due to their inherent complexities and nonconvexities.In this paper,we consider the problem of designing an asymptotically...The design of stabilizing controllers for general nonlinear systems remains a challenging task due to their inherent complexities and nonconvexities.In this paper,we consider the problem of designing an asymptotically stable controller of a nonlinear dynamic system.We begin by framing the problem as an inverse optimal control problem,aiming to design a pair of cost functions that ensure asymptotic stability for the nonlinear model predictive control closed-loop system.By leveraging the relaxed dynamic programming inequality,a machine learning based algorithm is proposed to learn the cost functions.Finally,we demonstrate the effectiveness of the proposed method through illustrative examples.展开更多
文摘The design of stabilizing controllers for general nonlinear systems remains a challenging task due to their inherent complexities and nonconvexities.In this paper,we consider the problem of designing an asymptotically stable controller of a nonlinear dynamic system.We begin by framing the problem as an inverse optimal control problem,aiming to design a pair of cost functions that ensure asymptotic stability for the nonlinear model predictive control closed-loop system.By leveraging the relaxed dynamic programming inequality,a machine learning based algorithm is proposed to learn the cost functions.Finally,we demonstrate the effectiveness of the proposed method through illustrative examples.
