Vapor compression cooling systems are the primary method used for refrigeration and air conditioning, and as such are a major component of household and commercial building energy consumption. Application of advanced control techniques to these systems is still a relatively unexplored area, and has the potential to significantly improve the energy efficiency of these systems, thereby decreasing their operating costs. This thesis explores a new method of decentralizing the capacity control of a multiple evaporator system in order to meet the separate temperature requirements of two cooling zones. The experimental system used for controller evaluation is a custom built small-scale water chiller with two evaporators; each evaporator services a separate body of water, referred to as a cooling zone. The two evaporators are connected to a single condenser and variable speed compressor, and feature variable water flow and electronic expansion valves. The control problem lies in development of a control architecture that will chill the water in the two tanks (referred to as cooling zones) to a desired temperature setpoint while minimizing the energy consumption of the system. A novel control architecture is developed that relies upon time scale separation of the various dynamics of the system; each evaporator is controlled independently with a model predictive control (MPC) based controller package, while the compressor reacts to system conditions to supply the total cooling required by the system as a whole. MPC’s inherent constraint-handling capability allows the local controllers to directly track an evaporator cooling setpoint while keeping superheat within a tight band, rather than the industrially standard approach of regulating superheat directly. The compressor responds to system conditions to track a pressure setpoint; in this configuration, pressure serves as the signal that informs the compressor of cooling demand changes. Finally, a global controller is developed that has knowledge of the energy consumption characteristics of the system. This global controller calculates the setpoints for the local controllers in pursuit of a global objective; namely, regulating the temperature of a cooling zone to a desired setpoint while minimizing energy usage.

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An adaptive model predictive control (MPC) method using models derived from orthonormal basis functions is presented. The defining predictor dynamics are obtained from state-space realizations of finite truncations of generalized orthonormal basis functions (GOBF). A structured approach to define multivariable system models with customizable, open-loop stable linear dynamics is presented in Chapter 2. Properties of these model objects that are relevant to the adaptation component of the overall scheme, are also discussed. In Chapter 3, non-adaptive model predictive control policies are presented with the definition of extended state representations through filter operations that enable output feedback. An infinite horizon set-point tracking policy which always exists under the adopted modeling framework is presented. This policy and its associated cost are included as the terminal stage elements for a more general constrained case. The analysis of robust stability guarantees for the non-adaptive constrained formulation is presented, under the assumption of small prediction errors. In Chapter 4, adaptation is introduced and the certainty equivalence constrained MPC policy is formulated under the same framework of its non-adaptive counterpart. Information constraints that induce the excitation of the signals relevant to the adaptation process are formulated in Chapter 5. The constraint generation leverages the GOBF model structure by enforcing a sufficient richness condition directly on the state elements relevant to the control task. This is accomplished by the definition of a selection procedure that takes into account the characteristics of the most current parameter estimate distribution. Throughout the manuscript, illustrative simulation examples are provided with respect to minimal plant models. Concluding remarks and general descriptions for potential future work are outlined in Chapter 6.

This dissertation focuses on implementing and analyzing advanced methods of model predictive control to control helicopters into stable flight near a ship and perform a soft touchdown from that state. A shrinking horizon model predictive control method is presented which can target specific states at specific times and take into account several important factors during landing. This controller is then used in simulation to perform a touchdown maneuver on a ship for a helicopter by targeting a landed state at a specific time. Increasing levels of fidelity are considered in the simulations. Computational power required reduces the closer the helicopter starts to the landing pad. An infinite horizon model predictive controller which allows simultaneous cost on state tracking, control energy, and control rates and allows tracking of an arbitrary equilibrium to infinity is then presented. It is applied in simulation to control a helicopter initially in a random flight condition far from a ship to slowly transition to stable flight near the ship, holding an arbitrary rough position relative to the ship indefinitely at the end. Three different target positions are simulated. This infinite horizon control method can be used to prepare for landing procedures that desire starting with the helicopter in some specific position in close proximity to the landing pad, such as the finite horizon method of landing control described previously which should start with the helicopter close to the ship to reduce computation power required. A method of constructing a landing envelope is then presented and used to construct a landing envelope for the finite horizon landing controller. A pre-existing method of combining linear controllers to account for nonlinearity is then slightly modified and used on implementations of the finite horizon landing controller to make a control that takes into account some of the nonlinearity of the problem. This control is tested in simulation. / Doctor of Philosophy / This dissertation proposes and, using simulation, analyzes control algorithms and their use on helicopter shipboard operations. Various benefits and advances for controls in this area are suggested, tested, and discussed. The control methods presented and implemented, while not limited to these use cases, are particularly well suited for them. One control algorithm is used for controlling flight near the landing point on a ship and performing a soft touchdown on the ship. The algorithm is tested in simulation. Another algorithm is used to control a helicopter initially in flight far away from the ship to slowly transition to stable flight near the ship, holding a rough position relative to the ship indefinitely at the end. This control could be used to set up the helicopter for later use of the touchdown control. This control is also tested in simulation. A method of quantifying what conditions the touchdown controller has a relatively good chance of successfully landing in is then suggested. The range of conditions for which successful touchdown has a relatively high chance of being achieved along with an analysis of that likelihood is called the landing envelope. Using the landing envelope construction method with numerous simulations, a landing envelope for the touchdown controller is obtained. The touchdown controller assumes that the helicopter’s dynamics are linear. Helicopter dynamics (like most dynamics of real systems) are nonlinear. However, under conditions near the point that dynamics are linearized about, a linear approximation is sufficiently accurate. To improve on the above landing algorithm, a method of combining multiple specific implementations of the touchdown controller to help account for nonlinearity to improve the approximation of the dynamics that the controller assumes is then suggested and performed in simulation.

The work in this thesis focuses on the development of a Stochastic Model Predictive Control (SMPC) algorithm for linear systems with additive and multiplicative stochastic uncertainty subjected to linear input/state constraints. Constraints can be in the form of hard constraints, which must be satisfied at all times, or soft constraints, which can be violated up to a pre-defined limit on the frequency of violation or the expected number of violations in a given period. When constraints are included in the SMPC algorithm, the difficulty arising from stochastic model parameters manifests itself in the online optimization in two ways. Namely, the difficulty lies in predicting the probability distribution of future states and imposing constraints on closed loop responses through constraints on predictions. This problem is overcome through the introduction of layered tubes around a centre trajectory. These tubes are optimized online in order to produce a systematic and less conservative approach of handling constraints. The layered tubes centered around a nominal trajectory achieve soft constraint satisfaction through the imposition of constraints on the probabilities of one-step-ahead transition of the predicted state between the layered tubes and constraints on the probability of one-step-ahead constraint violations. An application in the field of Sustainable Development policy is used as an example. With some adaptation, the algorithm is extended the case where the uncertainty is not identically and independently distributed. Also, by including linearization errors, it is extended to non-linear systems with additive uncertainty.

In this master thesis, a real-time control system that stabilizes the rotational rates of a tri-copter, has been studied. The tricopter is a rotorcraft with three rotors. The tricopter has been modelled and identified, using system identification algorithms. The model has been used in a Kalman filter to estimate the state of the system and for design ofa model based controller. The control approach used in this thesis is a model predictive controller, which is a multi-variable controller that uses a quadratic optimization problem to compute the optimal con-trol signal. The problem is solved subject to a linear model of the system and the physicallimitations of the system. Two different types of algorithms that solves the MPC problem have been studied. These are explicit MPC and the fast gradient method. Explicit MPC is a pre-computed solution to the problem, while the fast gradient method is an online solution. The algorithms have been simulated with the Kalman filter and were implemented on themicrocontroller of the tricopter.

Ce manuscrit de thèse s'intéresse à la modélisation et au contrôle des réfrigérateurs cryogéniques. Le cas particulier des réfrigérateurs soumis à de fortes variations de charges thermiques est étudié. Un modèle de chaque objet pouvant se trouver dans un réfrigérateur est proposé. La méthodologie d'assemblage pour obtenir le modèle des sous-systèmes qui composent le réfrigérateur est présenté, accompagnée de la méthode permettant d'obtenir une approximation linéaire des modèles des sous-systèmes. Grâce aux modèles développées, des lois de commande avancées sont synthétisées. Un contrôleur linéaire quadratique pour les stations de compression à deux ou trois niveaux de pression est proposé, ainsi qu'un contrôleur prédictif sous contrainte pour la boite froide. La particularité de ces stratégies de contrôle est qu'elles sont compatibles avec un automate programmable industriel (API) , doté d'une capacité de calcul et de stockage de donnée réduite. La capacité de prédiction en boucle ouverte du modèle développé est validé au regard de données expérimentales et les stratégies de contrôle sont validés en simulation et expérimentalement sur la station d'essais 400W@1.8K du SBT et sur la station de compression du LHC, au CERN. / This manuscript is concern with both the modeling and the derivation of control schemes for large cryogenic refrigerators. The particular case of those which are submitted to highly variable pulsed heat load is studied. A model of each objet that normally compose a large cryorefrigerator is proposed. The methodology to gather objects model into the model of a subsystem is presented. The manuscript also shows how to obtain a linear equivalent model of the subsystem. Based on the derived models, advances control scheme are proposed. Precisely, a linear quadratic controller for warm compression station working with both two and three pressures state is derived, and a predictive constrained one for the cold-box is obtained. The particularity of those control schemes is that they fit the computing and data storage capabilities of Programmable Logic Controllers (PLC) with are well used in industry. The open loop model prediction capability is assessed using experimental data. Developed control schemes are validated in simulation and experimentally on the 400W@1.8K SBT's cryogenic test facility and on the CERN's LHC warm compression station.

In a near future, it is imminent that passenger vehicles will soon be introduced with a new revolutionary brake by wire system which replaces all the mechanical linkages and the conventional hydraulic brake systems with complete 'dry' electrical components. One of the many potential benefits of a brake by wire system is the increased brake dynamic performances due to a more accurate and continuous operation of the EMB actuators which leads to the increased amount of possibilities for control in antilock brake system. The main focus of this thesis is on the application of a model predictive control (MPC) method to devise an antilock brake control system for a brake by wire vehicle. Unlike the traditional ABS control algorithms which are based on a trial and error method, the MPC based ABS algorithm aims to utilizes the behaviour of the model to optimize the wheel slip dynamics subject to system constraints. The final implementation of the wheel slip controller emb races decentralized control architecture to independently control the brake torque at each four wheel. Performance of the wheel slip controller is validated through Software-in-the-Loop and Hardware-in-the-Loop simulation. In order to support the high demands of the computational power and the real time constraints of the Hardware-in-the-Loop simulation, a novel multi processor real-time simulation system is developed using the reflective memory network and the off-the-shelf hardware components.

abstract: With the growing penetration of plug-in electric vehicles (PEVs), the impact of the PEV charging brought to the utility grid draws more and more attention. This thesis focused on the optimization of a home energy management system (HEMS) with the presence of PEVs. For a household microgrid with photovoltaic (PV) panels and PEVs, a HEMS using model predictive control (MPC) is designed to achieve the optimal PEV charging. Soft electric loads and an energy storage system (ESS) are also considered in the optimization of PEV charging in the MPC framework. The MPC is solved through mixed-integer linear programming (MILP) by considering the relationship of energy flows in the optimization problem. Through the simulation results, the performance of optimization results under various electricity price plans is evaluated. The influences of PV capacities on the optimization results of electricity cost are also discussed. Furthermore, the hardware development of a microgrid prototype is also described in this thesis. / Dissertation/Thesis / Masters Thesis Engineering 2018

The main topic of this thesis is model predictive control (MPC) of an unstable fighter aircraft. When flying it is important to be able to reach, but not exceed the aircraft limitations and to consider the physical boundaries on the control signals. MPC is a method for controlling a system while considering constraints on states and control signals by formulating it as an optimization problem. The drawback with MPC is the computational time needed and because of that, it is primarily developed for systems with a slowly varying dynamics. Two different methods are chosen to speed up the process by making simplifications, approximations and exploiting the structure of the problem. The first method is an explicit method, performing most of the calculations offline. By solving the optimization problem for a number of data sets and thereafter training a neural network, it can be treated as a simpler function solved online. The second method is called fast MPC, in this case the entire optimization is done online. It uses Cholesky decomposition, backward-forward substitution and warm start to decrease the complexity and calculation time of the program. Both methods perform reference tracking by solving an underdetermined system by minimizing the weighted norm of the control signals. Integral control is also implemented by using a Kalman filter to observe constant disturbances. An implementation was made in MATLAB for a discrete time linear model and in ARES, a simulation tool used at Saab Aeronautics, with a more accurate nonlinear model. The result is a neural network function computed in tenth of a millisecond, a time independent of the size of the prediction horizon. The size of the fast MPC problem is however directly affected by the horizon and the computational time will never be as small, but it can be reduced to a couple of milliseconds at the cost of optimality.