We present a computationally efficient scheduled model predictive control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local predictive controllers with estimates of their regions of stability covering the desired operating region, and implement them as a single scheduled MPC which on-line switches between the set of local controllers and ac...

This paper investigates the robustness of a soft constrained LTI MPC for set-point tracking, using an `1regularised cost. The MPC using this type of cost (informally dubbed `asso-MPC) is suitable, for instance, for redundantlyactuated systems. This is because of its ability to select a set of preferred actuators, leaving the other ones at rest for most of the time. The proposed approach aims to...

The major drawback to the deterministic method for MPC robust design is computational complexity on-line and control conservatism. From the probabilistic point of view, Randomized Algorithm of constrained MPC is developed in this paper, in which the additive disturbances are taken as random variables and then the control algorithm is simplified into on-line QP problem at each sampling time with...

This paper presents an approximate multi-parametric nonlinear programming approach to explicit solution of constrained nonlinear model predictive control (MPC) problems in the presence of model uncertainty. The case of time-invariant parameter uncertainty is considered. The explicit MPC controller is based on an orthogonal search tree structure of the state space partition and is designed by so...

Exact or approximate solutions to constrained linear model predictive control (MPC) problems can be pre-computed off-line in an explicit form as a piecewise linear state feedback defined on a polyhedral partition of the state space. However, the complexity of the polyhedral partition often increases rapidly with the dimension of the state vector, and the number of constraints. This paper presen...

This paper shows how to calculate feasible regions, parameterized in terms for the present state xk, for MPC controllers for constrained linear systems. The dependence of the feasible region on the prediction horizon is also made clear. It is also shown how the procedure may be modified to find guaranteed feasible regions in the presence of unknown, bounded disturbances. These ’robust’ feasible...

Any proper operation could be translated as a constrained optimization problem inside a WWTP, whose nonlinear behavior renders its control problems quite attractive for performance of multivariable optimization–based control technique algorithms, such as NMPC. The main advantage of this control technique lies in its ability to handle model nonlinearity as well as various types of constraints on...

This article proposes a robust self-triggered model predictive control (MPC) algorithm for class of constrained linear systems subject to bounded additive disturbances, in which the intersampling time is determined by fast convergence mechanism. The main idea mechanism select sampling interval so that rapid decrease predicted costs associated with optimal inputs guaranteed. allows reduction req...

In this thesis, practical techniques for analyzing, tuning and implementing an industrial model predictive controller (MPC) for paper machine cross-directional (CD) processes are developed. These techniques include model reduction, performance and robust stability analysis of a linear closed-loop system, and optimal prediction of steady-state performance for constrained MPC. Paper machine cross...

Model predictive control (MPC) has attracted wide attention in process industries with its ability to handle constrained multivariable processes. Computational complexity can become a limiting factor when MPC is applied to large-scale systems with fast sampling times. In this paper, a control scheme known as multi-step robust MPC is presented for polytopic uncertain multi-input systems. Only on...