On constrained infinite-time linear quadratic optimal control
Systems & Control Letters
Paper: Model predictive heuristic control
Automatica (Journal of IFAC)
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
The explicit linear quadratic regulator for constrained systems
Automatica (Journal of IFAC)
Convexity recognition of the union of polyhedra
Computational Geometry: Theory and Applications
Automatica (Journal of IFAC)
Synthesis of explicit model predictive control system with feasible region shrinking
ROCOM'08 Proceedings of the 8th WSEAS International Conference on Robotics, Control and Manufacturing Technology
On the horizons in constrained linear quadratic regulation
Automatica (Journal of IFAC)
MIC '07 Proceedings of the 26th IASTED International Conference on Modelling, Identification, and Control
The study of predictive control system based on coincidence points technology
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
The expression of control law for zone constraints predictive control
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Brief paper: Explicit predictive control with non-convex polyhedral constraints
Automatica (Journal of IFAC)
Using interpolation to improve efficiency of multiparametric predictive control
Automatica (Journal of IFAC)
Hi-index | 22.16 |
This paper presents an efficient algorithm for computing the solution to the constrained infinite-time, linear quadratic regulator (CLQR) problem for discrete time systems. The algorithm combines multi-parametric quadratic programming with reachability analysis to obtain the optimal piecewise affine (PWA) feedback law. The algorithm reduces the time necessary to compute the PWA solution for the CLQR when compared to other approaches. It also determines the minimal finite horizon N@?"S, such that the constrained finite horizon LQR problem equals the CLQR problem for a compact set of states S. The on-line computational effort for the implementation of the CLQR can be significantly reduced as well, either by evaluating the PWA solution or by solving the finite dimensional quadratic program associated with the CLQR for a horizon of N=N@?"S.