This work addresses the problem of robust output feedback model predictive control (MPC) constrained, discrete-time, time-delayed linear systems subject to (bounded) additive disturbances. The proposed controller incorporates an interval observer, that exploits available measurement update set-membership system states, and predictor, used in prediction step MPC. algorithm is similar conventiona...

This paper presents a new analysis tool for predicting the closed-loop performance of a robust constrained Model Predictive Control (MPC) scheme. Currently, performance is typically evaluated by numerical simulation, leading to extensive computation when investigating the effect of controller parameters, such as horizon length, cost weightings, and constraint settings. The method in this paper ...

Control algorithms suitable for online implementation in engineering applications, such as aerospace and mechanical vehicles, often require adherence to physical state and control constraints. Additionally, the chosen algorithms must provide robustness to uncertainty affecting both the system dynamics and the constraints. As further autonomy is built into these systems, the algorithms must be c...

It is common that an efficient constrained plant operation under full range of disturbance inputs requires meeting different sets of control objectives. This calls for application of multiple model predictive controllers each of them being best fit into specific operating conditions. It is inevitable then to switch between the controllers during the plant operation. A simple immediate hard swit...

In this paper, we develop a finite horizon model predictive control algorithm which is robust to model uncertainties. A moving average system matrix is constructed to capture model uncertainties and facilitate future output predictions. The paper is focused on step tracking control. Using linear matrix inequality techniques, the design is converted into a semi-definite optimization problem. Clo...

A number of results are derived for analysing the robust feasibility of a given Model Predictive Control (MPC) scheme which ignores model mismatch and/or disturbances during control input computation. The main contribution of this paper is the development of computationally tractable tests for determining the robust feasibility of an MPC controller for linear or piecewise affine systems, where ...

Model predictive control represents one of the most promising methods, also in fast industrial applications like a Diesel engine. But especially Diesel engine control meets problems of uncertainties and disturbances, thus model-plant mismatch is omnipresent, which obviously decreases the performance of model based control. This paper compares different robust predictive control strategies appli...