Self-scheduled H∞ control of linear parameter-varying systems: a design example
Automatica (Journal of IFAC)
Robust constrained model predictive control using linear matrix inequalities
Automatica (Journal of IFAC)
On constrained infinite-time linear quadratic optimal control
Systems & Control Letters
Adaptive Optimal Control: The Thinking Man's G.P.C.
Adaptive Optimal Control: The Thinking Man's G.P.C.
A Quasi-Infinite Horizon Nonlinear Model Predictive Control Scheme with Guaranteed Stability
Automatica (Journal of IFAC)
Brief paper: Moving horizon H∞ control with performance adaptation for constrained linear systems
Automatica (Journal of IFAC)
Brief paper: Min-max MPC using a tractable QP problem
Automatica (Journal of IFAC)
Brief paper: Control of dynamic keyhole welding process
Automatica (Journal of IFAC)
An efficient model predictive controller with pole placement
Information Sciences: an International Journal
New formulation of robust MPC by incorporating off-line approach with on-line optimization
International Journal of Systems Science
Automatica (Journal of IFAC)
Nonlinear model predictive formation flight
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Dynamic trade-off analysis of QoS and energy saving in admission control for web service systems
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
ACC'09 Proceedings of the 2009 conference on American Control Conference
An output feedback robust model predictive controller design based on quasi-min max algorithm
ACC'09 Proceedings of the 2009 conference on American Control Conference
Improving robust model predictive control via dynamic output feedback
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Brief Robust one-step receding horizon control of discrete-time Markovian jump uncertain systems
Automatica (Journal of IFAC)
Brief Optimizing the end-point state-weighting matrix in model-based predictive control
Automatica (Journal of IFAC)
Constrained RHC for LPV systems with bounded rates of parameter variations
Automatica (Journal of IFAC)
Piecewise affinity of min-max MPC with bounded additive uncertainties and a quadratic criterion
Automatica (Journal of IFAC)
MPC for stable linear systems with model uncertainty
Automatica (Journal of IFAC)
Output feedback model predictive control for LPV systems based on quasi-min-max algorithm
Automatica (Journal of IFAC)
A fast ellipsoidal MPC scheme for discrete-time polytopic linear parameter varying systems
Automatica (Journal of IFAC)
Engineering Applications of Artificial Intelligence
Synthesis of dynamic output feedback RMPC with saturated inputs
Automatica (Journal of IFAC)
Identification and predictive control for a circulation fluidized bed boiler
Knowledge-Based Systems
Stochastic model predictive control of LPV systems via scenario optimization
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Hi-index | 22.18 |
In this paper a new model predictive controller (MPC) is developed for polytopic linear parameter varying (LPV) systems. We adopt the paradigm used in gain scheduling and assume that the time-varying parameters are measured on-line, but their future behavior is uncertain and contained in a given polytope. At each sampling time optimal control action is computed by minimizing the upper bound on the ''quasi-worst-case'' value of an infinite horizon quadratic objective function subject to constraints on inputs and outputs. The MPC algorithm is called ''quasi'' because the first stage cost can be computed without any uncertainty. This allows the inclusion of the first move u(k|k) separately from the rest of the control moves governed by a feedback law and is shown to reduce conservatism and improve feasibility characteristics with respect to input and output constraints. Proposed optimization problems are solved by semi-definite programming involving linear matrix inequalities. It is shown that closed-loop stability is guaranteed by the feasibility of the linear matrix inequalities. A numerical example demonstrates the unique features of the MPC design.