-Some results concerning the constrained receding horizon formulation with infinite and truncated prediction horizon are presented. In this formulation a distinction is made between prediction and control horizons. The main result is a generalization of a known fact for linear systems, namely that, under certain technical requirements, the asymptotic stability of the above formulation holds for...

In this paper we develop and illustrate methods for estimating the degree of suboptimality of receding horizon schemes with respect to infinite horizon optimal control. The proposed a posteriori and a priori methods yield estimates which are evaluated online along the computed closed–loop trajectories and only use numerical information which is readily available in the scheme.

We propose a new stability constraint for contractive receding horizon control (RHC), which was inspired by a non-monotone line search rule for optimization algorithms. Previously proposed stability constraints for contractive RHC can be seen as special cases of our new constraint. The new stability constraint guarantees asymptotic stability, but it does not ensure the monotone decrease of the ...

To reach a given destination safely and accurately, a micro aerial vehicle needs to be able to avoid obstacles and minimize its state estimation uncertainty at the same time. To achieve this goal, we propose a perception-aware receding horizon approach. In our method, a single forwardlooking camera is used for state estimation and mapping. Using the information from the monocular state estimati...

We present new results on stochastic stability of linear discrete-time systems with additive uncertainty under several receding horizon control policies. We guarantee bounded variance of the closed-loop system under each policy via appending a stability constraint to the existing optimization routine of the corresponding policy.

Abstract This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is approximate optimal strategy cases in which online computation intractable due necessity solving mixed-integer quadratic programs each discrete time instant. proposed approach builds upon an approximated finite-horiz...

A typical bottleneck of model predictive control algorithms is the computational burden in order to compute the receding horizon feedback law which is predominantly determined by the length of the prediction horizon. Based on a relaxed Lyapunov inequality we present techniques which allow us to show stability and suboptimality estimates for a reduced prediction horizon. In particular, the known...

This paper describes a method for constrained state estimation based on receding horizon optimization. The case here studied corresponds to an optimization horizon of size two and a constraint horizon of size one. It is shown that, in this case, a simple closed-form solution can be obtained. The resulting estimator is called a Rolling Horizon Estimator with Constraint Horizon One. It is shown t...