Offset-free control of constrained linear systems using model predictive control

The control of systems in the presence of constraints and disturbances is an important issue in many application fields. In order to guarantee offset-free control when disturbances are asymptotically constant and nonzero, it is standard practice to augment the plant model with a disturbance model and use this combined model to estimate the size of the disturbance. However, this approach is not essentially a straightforward one and on the other hand they are only able to do offset -free tracking of piecewise constant reference inputs. In this paper, a new algorithm is presented for design of offset-free model-predictive control. This algorithm enables the output to do offset free tracking of the reference input while satisfying system constraints in the presence of unmeasured disturbances. This algorithm is split into two parts. The first part includes design of a stabilizing linear time-invariant controller in order to initiate the offset-free tracking feature due to entity of the augmented dynamics. This feature then will be completed by selecting the predictive model and design of a dynamic predictive controller. Design of the model-predictive controller explicitly includes both state and input constraints and thereby guarantees robust constraint satisfaction. In addition, the algorithm to be presented is able to satisfy offset-free tracking of all reference inputs that can be presented in the form of a rational transfer function without the need for estimating the disturbance.