On receding-horizon approximation in time-varying optimal control
The closed-loop stability and infinite-horizon performance of receding-horizon approximations are studied for non-stationary linear-quadratic regulator (LQR) problems. The approach is based on a lifted reformulation of the optimal control problem, under assumed uniform controllability and observability, leading to a strict contraction property of the corresponding Riccati operator. Leveraging this contraction property, a stabilizing linear time-varying state-feedback approximation of the infinite-horizon optimal control policy is constructed to meet a performance-loss specification. Its synthesis involves only finite preview of the time-varying problem data at each time step, over a sufficiently long prediction horizon.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans
We introduce ARES, an efficient approximation algorithm for generating optimal plans (action sequences) that take an initial state of a Markov Decision Process (MDP) to a state whose cost is below a specified (convergenc…
Ensemble Kalman-Bucy filtering for nonlinear model predictive control
We consider the problem of optimal control for partially observed dynamical systems. Despite its prevalence in practical applications, there are still very few algorithms available, which take uncertainties in the curren…
Model Predictive ControlState EstimationOptimal Control for Unmanned Systems with One-way Broadcast Communication
Unmanned systems (USs) including unmanned aerial vehicles, unmanned underwater vehicles, and unmanned ground vehicles have great application prospects in military and civil fields, among which the process of finding feas…
Distributed decentralized receding horizon control for very large-scale networks with application to satellite mega-constellations
The implementation feasibility of control algorithms over very large-scale networks calls for hard constraints regarding communication, computational, and memory requirements. In this paper, the decentralized receding ho…
Suboptimality analysis of receding horizon quadratic control with unknown linear systems and its applications in learning-based control
This work analyzes how the trade-off between the modeling error, the terminal value function error, and the prediction horizon affects the performance of a nominal receding-horizon linear quadratic (LQ) controller. By de…
Prediction