The models used by model predictive controllers (MPCs) to predict future outcomes are usually unconstrained forms like impulse or step responses and discrete state space models. Certain MPC algorithms allow constraints to be imposed on the inputs or outputs of a system; but they may be infeasible as they are not checked for consistency via the process model. Consistent constraint handling methods - which account for their interdependence and disambiguate the language used to specify constraints – would therefore be an attractive aid when using any MPC package. A rigorous and systematic approach to constraint management has been developed, building on the work of Vinson (2000), Lima (2007) and Georgakis et al. (2003) in interpreting constraint interactions. The method supports linear steady-state system models, and provides routines to obtain the following information: <ul> <li> effects of constraint changes on the corresponding input and output constraints, </li><li> feasibility checks for constraints, </li><li> specification of constraint-set size and</li><li> optimal fitting of constraints within the desirable input and output space.</li></ul> Mathematical rigour and unambiguous language for identifying constraint types were key design criteria. The outputs of the program provide guidance when handling constraints, as opposed to rules of thumb and experience, and promote understanding of the system and its constraints. The metrics presented are not specific to any commercial MPC and can be implemented in the user interfaces of such MPCs. The method was applied to laboratory-scale test rigs to illustrate the information obtained. / Dissertation (MEng)--University of Pretoria, 2011. / Chemical Engineering / unrestricted

<p>This thesis aims to design a controller to apply medication to patients with hypothyroidism, a disease that occurs due to the underacting thyroid gland. The body cannot produce sufficient thyroid hormones, which leads to an increase in the production of hormones in the pituitary gland. The thyroid malfunctioning could lead to other associated conditions like nausea, fatigue, heart conditions, higher cholesterol, and elevated blood pressure. Thus, it is essential to ensure that the levels of thyroid hormones, Triiodothyronine (T3) and Thyroxine (T4), are healthy. The production of these hormones is governed by the hypothalamus-pituitary-thyroid (HPT) axis, a part of the endocrine system. This illness cannot be cured but can be regulated entirely through medication. The standard practice to control hypothyroidism in patients is to prescribe a constant daily dosage of synthetic T4 (i.e., levothyroxine) and, in some cases, an additional dose of synthetic T3 (i.e., Liothyronine). In this thesis, simulation studies are performed where two patients with varying levels of hypothyroidism are prescribed constant doses of synthetic hormones. The medications initially help the patients but are unsuccessful in maintaining healthy ranges. Using model predictive control, an observer-controller-based compensator is proposed to prescribe varying medication doses as inputs based on the patient's requirement. The inputs are quantized to be practically implemented in a real patient scenario. This compensator successfully improves the patient's hormone levels toward healthy values and ensures that the hormone trajectories follow the body's circadian rhythm.  </p>