This paper proposes a quadratic programming (QP) approach to robust MPC for constrained linear systems having both model uncertainties and bounded disturbances. To this end, we construct an additional comparison model for worst-case analysis based on a robust control Lyapunov function (RCLF) for the unconstrained system (not necessarily an RCLF in the presence of constraints). By using this com...

NMPC explicitly addresses constraints and nonlinearities during the feedback control of batch processes. This NMPC algorithm also explicitly takes parameter uncertainty into account in the state estimation and state feedback controller designs. An extended Kalman filter estimates the process noise co®ariance matrix from the parameter uncertainty description and employs a sequential integration ...

An accurate definition of a system model significantly affects the performance model-based control strategies, for example, predictive (MPC). In this paper, model-free strategy is presented to mitigate all ramifications model’s uncertainties and parameter mismatch between plant controller power electronic converters in applications such as microgrids. A specific recurrent neural network structu...

Controller design faces a trade-off between robustness and performance, and the reliability of linear controllers has caused many practitioners to focus on the former. However, there is renewed interest in improving system performance to deal with growing energy constraints. This paper describes a learning-based model predictive control (LBMPC) scheme that provides deterministic guarantees on r...

The paper addresses the problem of designing robust output feedback model predictive control to one-step ahead prediction horizon which ensures a parameter dependent quadratic stability and guaranteed cost for the case of linear polytopic systems.

A method of computing a new model predictive control (MPC) law for linear parameter varying systems with input constraints is proposed. The proposed method improves feasibility and system performance by deriving a new sufficient condition for the cost monotonicity. The control problem is formulated as a minimisation of the upper bound of finite horizon cost function satisfying the sufficient co...

Distributed Model Predictive Control (DMPC) has received significant attention in the literature. However, the robustness of DMPC with respect to model errors has not been explicitly addressed. In this paper, an online algorithm that deals explicitly with model errors for DMPC is proposed. The algorithm requires decomposing the entire system into N subsystems and solving N convex optimization p...

Sufficient conditions for robust closed-loop stability of a class of dynamic matrix control (DMC) systems arepresented. The I,-norm is used in the objective function of the on-line optimization, thus resulting in a linear programming problem. The ideas of this work, however, are expandable to other DMC-type controllers. The keys to the stability conditions are: to use an end-condition in the mo...

Abstract: The dual-mode strategy has been adopted in many constrained MPC (Model Predictive Control) methods. The size of stabilizable regions of states of MPC methods depends on the size of underlying feasible and positively invariant sets and the number of control moves. The results, however, may perhaps be conservative because the definition of positive invariance does not allow temporal dep...