Sufficient conditions to guarantee stability of MPC strategies require the use of a terminal cost function and a terminal constraint region [9]. This paper gives a procedure for removing the terminal constraint while maintaining asymptotic stability. This is specially interesting when the system is unconstrained on the state. In this case, the computational burden of the optimization problem do...

Inverted pendulum is a non-linear, multivariable and unstable device, a model predictive control (MPC) performance evaluation and tuning method for inverted pendulum device is proposed. MPC was designed to control the inverted pendulum device, and the minimum variance covariance constrained control (MVC) was applied to evaluate the performance of the MPC controller and tune its parameters. The ...

The computational demands of Model predictive control (MPC) are well known, and due to its internal constrained optimiser, historically has been ill-suited for embedded controllers designed to tackle high-speed applications. This paper explores the options of developing a lowcost lightweight MPC controller destined for micro-controller or FPGA architectures for modest applications demanding rea...

Control of drinking water networks is an arduous task given their size and the presence of uncertainty in water demand. It is necessary to impose different constraints for ensuring a reliable water supply in the most economic and safe ways. To cope with uncertainty in system disturbances due to the stochastic water demand/consumption, and optimize operational costs, this paper proposes three st...

In this letter we introduce a novel approach to distributionally robust optimal control that supports online learning of the ambiguity set, while guaranteeing recursive feasibility. We conic representable risk, which is useful derive tractable reformulations optimization problems. Specifically, illustrate techniques introduced, utilize risk measures constructed based on data-driven sets, constr...

In this paper, we present an off-line approach for robust constrained MPC synthesis that gives an explicit control law using Linear Matrix Inequalities (LMIs). This off-line approach can address a broad class of model uncertainty descriptions with guaranteed robust stability of the closed-loop system and substantial reduction of the on-line MPC computation.

Robust explicit/multi-parametric controllers are designed for constrained, linear discrete–time systems with box-constrained states and inputs, involving uncertainty in the left–hand side (LHS) of the Model Predictive Control (MPC) optimization model. Based on previous results, this work presents a new algorithm that features: (i) a dynamic programming reformulation of the MPC optimization, (ii...

This paper deals with the first principle model based predictive control of the primary drying stage of a freeze drying of solutions in vials. In the proposed control approach, any online control problem concerned with a constrained optimization during the primary drying stage may be stated. It is solved using a special model predictive control framework where the model is used in the controlle...

In automotive powertrains, the existence of backlash causes driveability problems, which to some extent are remedied by the engine control system. The control problem has a constrained, minimum-time character, which motivates an investigation of the usability of model predictive control, MPC, in this application. Recent developments in MPC theory make an off-line calculation of the control law ...

Model Predictive Control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. This paper focuses on a stochastic MPC problem with constraints specified in a probabilistic sense. Our aim is to study the incorporation of state estimates into the MPC problem. The original problem can be approximated by a det...