Process industry requires now accurate, efficient and flexible operation of the plants. There is always a need for development of innovative technologies and integrated software tools for process modelling, dynamic trajectory optimization and high-performance industrial process control. Process dynamics tend to become too complex to be efficiently controlled by the current generation of control...

The feasibility region of a Model Predictive Control (MPC) algorithm is the subset state space in which constrained optimization problem to be solved feasible. In our recent Intrinsically Stable MPC (IS-MPC) method for humanoid gait generation, means being able satisfy dynamic balance condition, kinematic constraints on footsteps as well an explicit stability condition. Here, we exploit concept...

Set invariance plays a fundamental role in the design of control systems for constrained systems since the constraints can be satisfied for all time if and only if the initial state is contained inside an invariant set. This thesis is concerned with robust set invariance theory and its application to guaranteeing feasibility in model predictive control. In the first part of this thesis, some of...

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 proposes a multirate Model Predictive Control (MPC) algorithm for constrained systems where the control effort is adjusted several times during one sampling interval. Stability criterion for this new proposed algorithm is established. The test of the stability condition, the estimation of stability regions and the choice of the terminal weighting term are discussed.