Optimal control: linear quadratic methods
Optimal control: linear quadratic methods
A unified framework for the study of anti-windup designs
Automatica (Journal of IFAC)
Robust output-feedback model predictive control for systems with unstructured uncertainty
Automatica (Journal of IFAC)
Constrained Control and Estimation: An Optimisation Approach
Constrained Control and Estimation: An Optimisation Approach
Survey paper: Set invariance in control
Automatica (Journal of IFAC)
Robust output feedback model predictive control of constrained linear systems
Automatica (Journal of IFAC)
Robust model predictive control of constrained linear systems with bounded disturbances
Automatica (Journal of IFAC)
| Hi-index | 22.14 |
In spite of its easy implementation, ability to handle constraints and nonlinearities, etc., model predictive control (MPC) does have drawbacks including tuning difficulties. In this paper, we propose a refinement to the basic MPC strategy by incorporating a tuning parameter such that one can move smoothly from an existing controller to a new MPC strategy. Each change of this tuning parameter leads to a new stabilising control law, therefore, allowing one to gradually move from an existing control law to a new and better one. For the infinite horizon case without constraints and for the general case with state and input constraints, stability results are established. We also examine the practical applicability of the proposed approach by employing it in the nominal prediction model of the tube-based output feedback robust MPC method. The merits of the proposed method are illustrated by examples.