Control of uncertain systems: a linear programming approach
Control of uncertain systems: a linear programming approach
Robust constrained model predictive control using linear matrix inequalities
Automatica (Journal of IFAC)
Stable generalized predictive control with constraints and bounded disturbances
Automatica (Journal of IFAC)
Worst-case formulations of model predictive control for systems with bounded parameters
Automatica (Journal of IFAC)
Feedback Systems: Input-Output Properties
Feedback Systems: Input-Output Properties
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Robust output feedback model predictive control of constrained linear systems
Automatica (Journal of IFAC)
More efficient predictive control
Automatica (Journal of IFAC)
Robust constrained predictive control of uncertain norm-bounded linear systems
Automatica (Journal of IFAC)
Brief paper: Multipliers for model predictive control with structured input constraints
Automatica (Journal of IFAC)
QoS optimization for thermal-aware cyber-physical systems
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Brief paper: Stochastic tube MPC with state estimation
Automatica (Journal of IFAC)
Nonlinear offset-free model predictive control
Automatica (Journal of IFAC)
Predictive metamorphic control
Automatica (Journal of IFAC)
Hi-index | 22.15 |
In this paper, we present novel results that parameterize a broad class of robust output-feedback model predictive control (MPC) policies for discrete-time systems with constraints and unstructured model uncertainty. The MPC policies we consider employ: (i) a linear state estimator, (ii) a pre-determined feedback gain (iii) a set of ''tighter constraints'' and (iv) a quadratic cost function in the degrees of freedom and the estimated state. Contained within the class, we find both well-known control policies and policies with novel features. The unifying aspect is that all MPC policies within the class satisfy a robust stability test. The robust stability test is suited to synthesis and incorporates a novel linear matrix inequality (LMI) condition which involves the parameters of the cost function. The LMI is shown to always be feasible under an appropriate small-gain condition on the pre-determined feedback gain and the state estimator. Moreover, we show, by means of both theoretical and numerical results, that choosing the cost function parameters subject to the proposed condition often leads to good nominal performance whilst at the same time guaranteeing robust stability.