From Continuous Modelling To Discrete Constrained Optimal Control Of Distributed Parameter Systems

From Continuous Modelling to Discrete Constrained Optimal Control of Distributed Parameter Systems

Distributed parameter systems (DPS) are systems that have their evolution through time and in space. These systems are present in every type of industrial process, from chemical to electrical applications. Thus, proper modeling and control of DPS are indispensable for the optimization and control of such processes. Due to their spatiotemporal dynamics, these systems are generally represented by partial integro-differential equations (PIDEs), which brings issues with the control and monitoring of such applications. This thesis studies the modeling and control of such systems, specifically the ones modelled by first and second-order hyperbolic PDEs, not relying on the spatial approximation generally applied to deal with the PIDEs. First, an alternative model for transport-reaction processes is analyzed, taking into account the possible inertia present in the transport. Second, the regulator design of a heat exchanger system in the continuous-time setting is developed, ensuring disturbance rejection and proper output tracking. Then, the leap from the continuous to the discrete-time is taken by studying the regulator design for the sediment-filled water canal dynamics. Lastly, the optimal constrained controller is developed in the last chapters, to take into account constraints applied to the system. First, an autothermal reactor operating in an unstable condition is considered. The simulations show the controller performance and proper convergence to the desired steady-state. In the subsequent chapter, the constrained control problem is solved for the alternative model of transport-reaction processes. The difference in the system response of the commonly used model and the proposed model is noticeable. The discrete representation used for the systems in the discrete-time setting does not consider the early spatial approximation generally used when dealing with DPS.

Technical Abstract Bulletin

Modelling Optimization and Control of Biomedical Systems

Shows the newest developments in the field of multi-parametric model predictive control and optimization and their application for drug delivery systems This book is based on the Modelling, Control and Optimization of Biomedical Systems (MOBILE) project, which was created to derive intelligent computer model-based systems for optimization of biomedical drug delivery systems in the cases of diabetes, anaesthesia, and blood cancer. These systems can ensure reliable and fast calculation of the optimal drug dosage without the need for an online computer—while taking into account the specifics and constraints of the patient model, flexibility to adapt to changing patient characteristics and incorporation of the physician’s performance criteria, and maintaining the safety of the patients. Modelling Optimization and Control of Biomedical Systems covers: mathematical modelling of drug delivery systems; model analysis, parameter estimation, and approximation; optimization and control; sensitivity analysis & model reduction; multi-parametric programming and model predictive control; estimation techniques; physiologically-based patient model; control design for volatile anaesthesia; multiparametric model based approach to intravenous anaesthesia; hybrid model predictive control strategies; Type I Diabetes Mellitus; in vitro and in silico block of the integrated platform for the study of leukaemia; chemotherapy treatment as a process systems application; and more. Introduces readers to the Modelling, Control and Optimization of Biomedical Systems (MOBILE) project Presents in detail the theoretical background, computational tools, and methods that are used in all the different biomedical systems Teaches the theory for multi-parametric mixed-integer programming and explicit optimal control of volatile anaesthesia Provides an overview of the framework for modelling, optimization, and control of biomedical systems This book will appeal to students, researchers, and scientists working on the modelling, control, and optimization of biomedical systems and to those involved in cancer treatment, anaesthsia, and drug delivery systems.