摘要：AbstractIn nonlinear model predictive control (NMPC), a control task is approached by repeatedly solving an optimal control problem (OCP) over a receding horizon. Popularly, the OCP is approximated with a finite-dimensional nonlinear program (NLP). Since computing the solution of an NLP can be a complex and time-consuming task, tailored optimization algorithms have emerged to (approximately) solve the NLPs. Most methods rely on repeatedly solving a quadratic approximation of the NLP. Since computing this approximation is generally computationally demanding, it can form a bottelenck in obtaining a real-time applicable control law. This paper proposes DOPUS, a novel update scheme for the quadratic approximation of the NLP. DOPUS exploits the structure of the NLP and the repeated nature at which it is solved, to reduce the number of computations at the price of a small reduction of the convergence speed. Foreseen application areas include (economic) NMPC for fast-changing control tasks and fast time-varying systems. The convergence properties of DOPUS are studied and the performance is illustrated in a numerical case study considering a control task for a planar robot arm.
