Asymptotic Properties of Receding Horizon Optimal Control Problems
SIAM Journal on Control and Optimization
SIAM Journal on Control and Optimization
A Quasi-Infinite Horizon Nonlinear Model Predictive Control Scheme with Guaranteed Stability
Automatica (Journal of IFAC)
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Matrosov theorem for parameterized families of discrete-time systems
Automatica (Journal of IFAC)
Piecewise constant model predictive control for autonomous helicopters
Robotics and Autonomous Systems
A dual-mode fuzzy model predictive control scheme for unknown continuous nonlinear system
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Hi-index | 22.14 |
Results on stabilizing receding horizon control of sampled-data nonlinear systems via their approximate discrete-time models are presented. The proposed receding horizon control is based on the solution of Bolza-type optimal control problems for the parametrized family of approximate discrete-time models. This paper investigates both situations when the sampling period T is fixed and the integration parameter h used in obtaining approximate model can be chosen arbitrarily small, and when these two parameters coincide but they can be adjusted arbitrary. Sufficient conditions are established which guarantee that the controller that renders the origin to be asymptotically stable for the approximate model also stabilizes the exact discrete-time model for sufficiently small integration and/or sampling parameters.