Journal of Optimization Theory and Applications
A universal formula for stabilization with bounded controls
Systems & Control Letters
Global stabilization and restricted tracking for multiple integrators with bounded controls
Systems & Control Letters
Worst-case formulations of model predictive control for systems with bounded parameters
Automatica (Journal of IFAC)
Technical Communique: Superposition in efficient robust constrained predictive control
Automatica (Journal of IFAC)
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Survey Constructive nonlinear control: a historical perspective
Automatica (Journal of IFAC)
Design of robust model-based controllers via parametric programming
Automatica (Journal of IFAC)
Uniting bounded control and MPC for stabilization of constrained linear systems
Automatica (Journal of IFAC)
An efficient model predictive controller with pole placement
Information Sciences: an International Journal
Brief paper: MPC for tracking piecewise constant references for constrained linear systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Generalized receding horizon control of fuzzy systems based on numerical optimization algorithm
IEEE Transactions on Fuzzy Systems
An MPC approach to the design of two-layer hierarchical control systems
Automatica (Journal of IFAC)
Stability analysis for a class of switched nonlinear systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Hi-index | 22.15 |
In this work, we consider nonlinear systems with input constraints and uncertain variables, and develop a robust hybrid predictive control structure that provides a safety net for the implementation of any model predictive control (MPC) formulation, designed with or without taking uncertainty into account. The key idea is to use a Lyapunov-based bounded robust controller, for which an explicit characterization of the region of robust closed-loop stability can be obtained, to provide a stability region within which any available MPC formulation can be implemented. This is achieved by devising a set of switching laws that orchestrate switching between MPC and the bounded robust controller in a way that exploits the performance of MPC whenever possible, while using the bounded controller as a fall-back controller that can be switched in at any time to maintain robust closed-loop stability in the event that the predictive controller fails to yield a control move (due, e.g., to computational difficulties in the optimization or infeasibility) or leads to instability (due, e.g., to inappropriate penalties and/or horizon length in the objective function). The implementation and efficacy of the robust hybrid predictive control structure are demonstrated through simulations using a chemical process example.