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In this paper we apply model order reductions techniques to efficiently implement simultaneous model predictive control and moving horizon estimation for high dimensional chemical processes. Two model approximation schemes that both combine order reduction and linearization are employed and compared. The approach is demonstrated on a benchmark distillation column example model.

This paper considers the computational efficiency of using generalised function parameterisations for multi-parametric quadratic programming (mp-QP) solutions to MPC. Earlier work demonstrated the potential of Laguerre parameterisations for improving computational efficiency in that the parametric solutions either required fewer regions and/or gave larger volumes. This paper considers the potential of extending this concept to more general function parameterisations. Specifically the aim is to consider to what extent different function parameterisations affect the parametric solution complexity and feasible volumes. Extensive simulation results which suggest there are indeed benefits from using more general parameterisations than Laguerre.

Recently, a comprehensive dynamic mathematical model named Copernicus has been established to discover the mechanism of the vascular bubble formation and growth during and after decompression from a dive. The model uses Venous Gas Emboli (VGE) as a measurement and connects it to the risk of severe Decompression Sickness (DCS). Being validated by a series of diving tests, Copernicus model is believed to be suitable and efficient to predict DCS hence generate optimal decompression profiles for the divers. This paper is based on the Copernicus model and presents a nonlinear model predictive control approach, where multi-parametric nonlinear programming is used to construct an explicit solution for the ease of implementation on a typical low-cost diving computer.

In this work we present a novel multi-parametric nonlinear programming (mp-NLP) algorithm for explicit multi-parametric nonlinear model predictive control (mp-NMPC). The algorithm is based on (i) local sensitivity analysis of nonlinear programs (NLP), and (ii) an exploration procedure which makes use of successive linearizations of the dynamic system and the nonlinear constraints. The algorithm is illustrated with an example problem drawn from the open literature.

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) a robust reformulation of the constraints that accounts for uncertainty and (iii) a multi–parametric programming solution step where the controls are obtained as an explicit function of the states.

In this work the three–level hierarchical control problem and the decentralised control problem are investigated and a general optimisation strategy is developed for solving these problems based on recent developments on multi–parametric programming. The main idea is to recast each optimisation subproblem in the multilevel hierarchy as a multi–parametric programming problem and then transform the multilevel problem into a single-level optimisation problem. This allows for the control policies (decisions) at each level of the multilevel optimisation problem to be obtained as explicit functions of the state of the dynamic systems involved in each level and the control policies of the higher levels. A three person dynamic optimisation problem is presented to illustrate the mathematical developments.

Multi parameteric quadratic programming gives a full offline solution to a time varying quadratic programming (QP) problem arising during constrained predictive control. However, coding and implementation of this solution may be more burdensome than simply solving the original QP. This paper proposes an algorithm, which by accepting a small amount of suboptimality in the predicted control trajectories, achieves a large decrease in both the online computation and data storage requirements