Traditionally the tuning of DMC-type multivariable controllers is done by trial and error. The APC engineer would choose arbitrary starting values and test the performance on a simulated controller. The engineer would then either increase the values to suppress movement more, or decrease them to have the manipulated variables move faster. When the controller performs acceptably in simulation, then the tuning is improved during the commissioning of the controller on the plant. This is a time consuming and unscientific exercise and therefore often does not get the required attention, leading to unacceptable controller behaviour during commissioning and sub-optimal control once commissioning is completed. This dissertation presents a new method to obtain move suppression factors for DMC type multivariable controllers. The challenge in choosing move suppressions lies in the multivariable nature of the controller. Changing the move suppression on one manipulated variable will not only change the performance of that manipulated variable, it will also change the performance of every other manipulated variable with models to the same controlled variables. In the same way, changing the steady state cost of a manipulated variable or the equal concern error of a controlled variable will also affect the behaviour of every other manipulated variable with shared models. There have been attempts to calculate the required move suppression factors mathematically. Some methods used an approach that is based on the premise that move suppression factors that present a well-conditioned controller matrix will provide a well behaved controller in terms of tuning. Some other methods focussed on providing parameters that will cause desirable controlled variable response, either by determining tuning parameters offline, or by re-tuning the controller in real time. The method described in this paper uses a Nelder Mead (Nelder and Mead, 1965) search algorithm to search for move suppressions that will provide acceptable control behaviour. Acceptable behaviour is defined by characterising the dynamic move plan calculated by the controller for each of the manipulated variables, or by characterising the controlled variable path that will result from the manipulated variable moves. The search algorithm can change the move suppressions, the steady state costs, or the move suppression multipliers as used in DMC type controllers. Copyright / Dissertation (MEng)--University of Pretoria, 2012. / Chemical Engineering / unrestricted

Στην προκειμένη εργασία μελετάται όλο το θεωρητικό υπόβαθρο για τον προβλεπτικό έλεγχο για τις δύο κατηγορίες συστημάτων (Single Input-Single Output SISO, Multiple Input-Multiple Output MIMO). Αρχικά μελετάται η πρώτη μορφή προβλεπτικού ελέγχου που ήταν ο δυναμικός έλεγχος μητρών (DMC). Στην συνέχεια ακολουθεί το πρόβλημα του βέλτιστου προβλεπτικού ελέγχου διακριτού χρόνου όπως αυτό παρουσιάζεται και αναλύεται στην θεωρία του κ. Μaciejowski. Αμέσως μετά μελετάται πάλι το πρόβλημα εύρεσης βέλτιστου προβλεπτικού ελέγχου διακριτού χρόνου αλλά με την χρησιμοποίηση των διακριτών ορθοκανονικών συναρτήσεων βάσης Laguerre όπως αναλύεται από τον κ. Wang στο βιβλίο του. Στις δύο επόμενες ενότητες παρουσιάζονται οι ορθοκανονικές συναρτήσεις βάσης Laguerre συνεχούς χρόνους καθώς και μια άλλη κατηγορία, οι συναρτήσεις Κautz και αναλύεται ο τρόπος που υπολογίζεται ο προβλεπτικός έλεγχος συνεχούς χρόνου με τη χρήση αυτών. Αφού ο αναγνώστης αποκτήσει τις γνώσεις που χρειάζονται πάνω στον προβλεπτικό έλεγχο, ακολουθεί μια πρακτική εφαρμογή πάνω σε ένα ελικόπτερο 2 βαθμών ελευθερίας της Quanser. Εκεί αρχικά αφού περιγραφεί πλήρως η διάταξη μελετάμε τα προβλήματα ελέγχου πρώτα με Pole Placement στην συνέχεια με LQR καθώς και με την χρησιμοποίηση εκτιμητών κατάστασης όπως κάποιο παρατηρητή (observer) ή φίλτρο Kalman πάντα με τη βοήθεια του Μatlab και του Simulink. Επίσης όλη η θεωρία του ΜPC που μελετήσαμε έχει εφαρμοσθεί επιτυχώς σε προσομοίωση στο Μatlab και Simulink. Παρουσιάζονται ο κώδικας που χρειάζεται κάθε φορά καθώς και ενδιαφέροντα αποτέλεσματα για την απόκριση της διεργασίας. Επιπλέον μελετήθηκε το toolbox του Matlab για τον προβλεπτικό έλεγχο (MPC Toolbox). Τέλος οι παραπάνω έλεγχοι εφαρμόσθηκαν κατευθείαν στην πραγματική διεργασία (μη γραμμική) και τα αποτελέσματα ήταν ικανοποιητικά. / This work presents all the necessary theory for the Model Predictive Control for both system categories (Single Input-Single Output SISO, Multiple Input-Multiple Output MIMO). To start, the earliest form of MPC called dynamic matrix control (DMC) is studied. Then the optimal Model Predictive Control for discrete time is analyzed based on the theory that Maciejowski presented. Afterwards the same problem is studied using the discrete time Laguerre orthonormal base functions and the optimal Model Predictive Control is computed as presented in Wang’s theory. In the next two chapters the reader will be guided through the continuous time Laguerre and Kautz orthonormal base functions and how they are used in computing the optimal continuous time Model Predictive Control. Since the reader has acquired all the necessary knowledge about MPC, a practical approach on the Quanser’s two degrees of freedom helicopter follows. Initially, after we have fully described the plant and all its components, we study the control problems using the pole placement and LQR techniques along with state estimators such as observers and Kalman filter, always in the Matlab and Simulink enviroment. Next, the MPC approaches we’ve studied are applied successfully, again using Matlab and Simulink. In every case, all the necessary programs and results are presented in detail. Addionally, the Matlab MPC Toolbox is studied along with its results for the problem. Finally all those controls are applied directly to the real nonlinear plant successfully and the results are discussed.

Diabetes is a leading cause of death and disability in the United States. Diabetes requires a lifetime medical treatment. Some diabetes drugs could be taken orally, while others require daily injection or inhalation to maximize bioavailability and minimize toxicity. Parenteral delivery is a group of delivery routes which bypass human gastrointestinal track. Among all the parenteral methods, we chose subcutaneous implant based on its fast act and high patient compliance. When using subcutaneous implant, drug release needs to be strictly controlled. There are three major groups of controlled release methods. Solvent controlled system is already used as osmotic implant. Matrix controlled system is used in Zoladex® implant to treat cancer. Membrane controlled systems is widely used in coating tablets, but not that popular as an implant. Based on the research reported by previous scientists, we decided to build a hybrid system using both matrix and membrane control to delivery human insulin and other small molecule drugs. Subcutaneous environment is different from human GI track. It has less tolerance for external materials so many polymers cannot be used. From the FDA safe excipient database, we selected albumin as our primary polymer and gelatin as secondary choice. In our preliminary insulin diffusion study, we successfully found that insulin mixed with albumin provided a slower diffusion rate compared with control. In addition, we added zinc chloride, a metal salt that can precipitate albumin. The insulin diffusion rate is further reduced. The preliminary study proved that matrix control using albumin is definitely feasible and we might add zinc chloride as another factor. In order to fabricate an implant with appropriate size, we use lyophilisation technology to produce uniformly mixed matrix. Apart from albumin and human insulin, we added sucrose as protectant and plasticizer. The fine powder after freeze-dry was pressed as a form of tablet. The tablets were sealed in Falcon® cell culture insert. Cell culture insert provide a cylinder shape and 0.3 cm2 surface area for drug release. Insulin release study provided a zero order kinetics from prototypes with zinc chloride or 0.4 micron pore size membrane. Caffeine was used as a model drug to investigate the releasing mechanism. Three pore size membranes (0.4, 3 and 8 micron) were tested with same formulation. While 0.4 micron prototypes provided the slowest release, 3 micron ones surprisingly released caffeine faster than 8 micron implants. We calculated the porosity with pore size and concluded that the percentage of open area on a membrane is the key point to control caffeine release. 0.4 micron membranes were used for future research. We increased the percentage of albumin in our excipient, and achieved a slower caffeine release. However, the zero order release could only last for 3 days. After we replaced sucrose with gelatin, a 5 day zero order release of caffeine was achieved. With all the results, we proposed our “Three Phase” drug release mechanism controlled by both membrane and matrix. Seven other small molecule drugs were tested using our prototype. Cloudy suspension was observed with slightly soluble drugs. We updated our “Three Phase” drug release mechanism with the influence of drug solubility. Data shows that releasing rate with same formulation and membrane follows the solubility in pH 7.4. This result proves that our prototype might be used for different drugs based on their solubility. Finally, with all the information of our prototype, we decided to build a “smart insulin implant” with dose adjustment. We proposed an electrical controlled implant with different porosity membranes. Solenoid was used as the mechanical arm to control membrane porosity. 3-D printing technology was used to produce the first real prototype of our implant. Finally, insulin implant with clinically effective insulin release rate was achieved. / Pharmaceutical Sciences