The increasing penetration of renewable energy resources within the Great Britain (GB) transmission system has created much greater variability of power flows within the transmission network. Consequently, modern transmission networks are presented with an ever increasing range of operating conditions. As a result, decision making in the Electricity National Control Centre (ENCC) of the GB electrical power transmission system is becoming more complex and control room actions are required for reducing timescales in the future so as to enable optimum operation of the system. To maximise utilisation of the electricity transmission system there is a requirement for fast transient and dynamic stability control. In this regard, GB electrical power transmissions system reinforcement using new technology, such as High Voltage Direct Current (HVDC) links and Thyristor-Controlled Series Compensation (TCSC), is planned to come into operation. The research aim of this PhD thesis is to fully investigate the effects of HVDC lines on power system small-disturbance stability in the presence of operational uncertainties. The main research outcome is the comprehensive probabilistic assessment of the stability improvements that can be achieved through the use of supplementary damping control when applied to HVDC systems. In this thesis, two control schemes for small-signal dynamic stability enhancement of an embedded HVDC link are proposed: Modal Linear Quadratic Gaussian (MLQG) controller and Model Predictive Controller (MPC). Following these studies, probabilistic methodologies are developed in order to test of the robustness of HVDC based damping controllers, which involves using classification techniques to identify possible mitigation options for power system operators. The Monte Carlo (MC) and Point Estimated Method (PEM) are developed in order to identify the statistical distributions of critical modes of a power system in the presence of uncertainties. In addition, eigenvalue sensitivity analysis is devised and demonstrated to ensure accurate results when the PEM is used with test systems. Finally, the concepts and techniques introduced in the thesis are combined to investigate robustness for the widely adopted MLQG controller and the recently introduced MPC, which are designed as the supplementary controls of an embedded HVDC link for damping inter-area oscillations. Power system controllers are designed using a linearised model of the system and tuned for a nominal operating point. The assumption is made that the system will be operating within an acceptable proximity range of its nominal operating condition and that the uncertainty created by changes within each operating point can possibly have an adverse effect on the controller’s performance.

<p> This thesis studies critical zones of automated vehicles. The goal is for the automated vehicle to complete a car-following or lane change maneuver without collision. For instance, the automated vehicle should be able to indicate its interest in changing lanes and plan how the maneuver will occur by using model predictive control theory, in addition to the autonomous vehicle toolbox in Matlab. A test bench (that includes a scenario creator, motion logic and planner, sensors, and radars) is created and used to calculate the parameters of a critical zone. After a trajectory has been planned, the automated vehicle then attempts the car following or lane change while constantly ensuring its safety to continue on this path. If at any point, the lead vehicle brakes or a trailing vehicle accelerates, the automated vehicle makes the decision to either brake, accelerate, or abandon the lane change. </p>

Die modellprädiktive Regelung (MPC) hat sich in vielen industriellen Anwendungen bewährt. Ein Nachteil ist jedoch der hohe Rechenaufwand, bedingt durch die erforderliche Lösung eines Optimierungsproblems in jedem Abtastschritt. In diesem Beitrag wird die entworfene MPC daher durch ein neuronales Netz approximiert. Als neuronales Netz wird ein Multi-Layer-Perceptron (MLP) verwendet. Mit dem trainierten MLP sind nur noch Matrix-Vektor-Multiplikationen erforderlich, die effizient berechnet werden können. Das trainierte MLP wird in C++ Code übersetzt und durch einen Industrie- PC ausgeführt. Am akademischen Beispiel einer Wippenwinkel-Regelung wird die Funktionsfähigkeit und der geringere Rechenbedarf demonstriert.

A dynamic real-time optimization (DRTO) formulation with closed-loop prediction is used to coordinate distributed model predictive controllers (MPCs) by rigorously predicting the interaction between the distributed MPCs and full plant response in the DRTO formulation. This results a multi-level optimization problem and that is solved by replacing the MPC quadratic programming subproblems by their equivalent Karush-Kuhn-Tucker (KKT) first-order optimality conditions to yield a single-level mathematical program with complementarity constraints (MPCC). The proposed formulation is able to perform both target tracking and economic optimization with significant performance improvement over decentralized control, and similar performance to centralized MPC. A linear dynamic case study illustrates the performance of the proposed strategy for coordination of distributed MPCs for different levels of plant interaction,. The method is thereafter applied to a nonlinear integrated plant with recycle, where its performance in both set-point target tracking and economic optimization is demonstrated. Subsequently, this study presents two techniques for approximation of the closed-loop prediction within the DRTO formulation - a hybrid closed-loop formulation and an input clipping formulation. The hybrid formulation generates closed-loop predictions for a limited number of time intervals along the DRTO prediction horizon, followed by an open-loop optimal control formulation extended to rest of the horizon. The input clipping formulation utilizes an unconstrained MPC optimization formulation for each distributed MPC, coupled with the application of an input saturation mechanism. The performance of the approximation techniques is evaluated through application to case studies based on linear and nonlinear dynamic plant models respectively. The approximation techniques are demonstrated to be more computationally efficient than than the rigorous counterpart without significant loss in performance. The performance of the proposed DRTO formulation can be further improved by the introduction of nonlinearity. The nonlinear dynamic plant model is firstly introduced in the DRTO formulation while maintaining the linear formulation for the distributed MPCs. The performance of resulting formulation is demonstrated and compared against the linear counterpart. The nonlinear MPC formulation is then included in both lower-level control implementation and DRTO formulation. By reformulating the Lagrangian of the nonlinear MPC optimization subproblems, the nonlinear MPC formulation is successfully implemented in the DRTO formulation. The performance of such DRTO formulation is further improved and shown using a nonlinear case study. The conclusion of this study is summarized and the potential directions of this research such as large-scale applications, variation of MPC implementations, and robust model-based control are outlined and explained in the end. / Thesis / Master of Applied Science (MASc)

Model Predictive Control (MPC) algorithms are widely applied in the chemical process industry. The main advantage of these controllers over others is their ability to provide multivariable control of the process subject to specified constraints. The presence of degrees of freedom in the plant provide an opportunity for the introduction of an optimization level (Real-Time Optimization (RTO) level), to determine optimal set points and target values for controlled variables and manipulated variables respectively, and the constraints the plant should follow to provide maximum profit. Industrial MPC controllers typically include an upper level steady-state optimizer, which usually comprises a linear programming (LP) or quadratic programming (QP) problem. This local optimizer may serve either as an integrating level between the low frequency nonlinear steady-state RTO and regulatory level, or as an independent optimizer with an economic objective function. Many researchers have reported success of LP-MPC cascade control system implementations (Sorensen and Cutler, 1998; Verne et al., 1999). However, despite its apparent success, poor LP-MPC cascade system performance and possible instability have also been reported. In particular, Shah et al. (2002) show that in the presence of a steady-state LP optimizer, the set-points could have a large variation relative to the controlled variable variation; thus the LP could degrade the MPC performance by sending highly variable set-points to the controller. Kozub (2002) indicates that in a control system with an LP steady-state optimizer, an LP instability problem may arise under certain conditions. These observations motivated research which aims to investigate the effect of the various factors on the stability and performance of the two-level LP-MPC cascade control system. Such factors include plant/model mismatch, the frequency of LP implementation, the LP objective function, constraints and type of disturbances. Since the optimization can be executed at different frequencies, two most common scenarios are considered: (i) when the LP is implemented at steady-state only and (ii) when the LP is implemented at every MPC iteration. Initially, steady-state LP optimization only is considered and it is shown that the set-points may fail to converge to constant values in the absence of external disturbances under certain conditions. Then, the effects of optimization frequency and control structure on the closed-loop properties of the LP-MPC control system are investigated. Results of a number of case studies are shown, and root causes for observed behavior discussed. As a part of the regulatory level analysis, the calculation of the closed-loop equilibrium of a process controlled by constrained MPC is studied. This problem arises in process design and operations, and is often applied within an optimization framework. It is shown that the effect of the control system on the resulting steady-state must be explicitly accounted for, and that in the general case, the use of a steady-state process model only is not sufficient for this calculation to be correctly executed. Two solution strategies, sequential and simultaneous, are presented and evaluated. The effect of high frequency noise-like disturbances on the two-level control system behavior is analyzed. The analysis which verified by case studies, showed that the LP may have an effect of amplifying the system noise through the bias term which is used for the model update. Such amplification may result in high variation of the LP set points provided to the MPC, thereby degrading the overall performance of the two-level system. / Thesis / Master of Applied Science (MASc)

Using robots to navigate through un-mapped environments, specially man-made infrastructures, for the purpose of exploration or inspection is a topic that has gathered a lot of interest in the last years. Micro Aerial Vehicles (MAV's) have the mobility and agility to move quickly and access hard-to-reach areas where ground robots would fail, but using MAV's for that purpose comes with its own set of problems since any collision with the environment results in a crash. The control architecture used in a MAV for such a task needs to perform obstacle avoidance and on-line path-planning in an unknown environment with low computation times as to not lose stability. In this thesis a Non-linear Model Predictive Controller (NMPC) for obstacle avoidance and path-planning on an aerial platform will be established. Included are methods for constraining the available state-space, simulations of various obstacle avoidance scenarios for single and multiple MAVs and experimental validation of the proposed control architecture. The validity of the proposed approach is demonstrated through multiple experimental and simulation results. In these approaches, the positioning information of the obstacles and the MAV are provided by a motion-capture system. The thesis will conclude with the demonstration of an experimental validation of a centralized NMPC for collision avoidance of two MAV's.

<p>Arbetet har utförts på Saab Bofors Dynamics AB i Linköping och dess syfte är att undersöka möjligheten att applicera teorin för prediktionsreglering, Model Predictive Control (MPC), på guidance systemet i en missil av typen Medium Range Air-to-Air Missiles (MRAAM). Även implementering via Explicit MPC har undersökts. </p><p>I tidigare studier har det visat sig att den moderna slutfasstyrningsalgoritmen Linear Quadratic Augmented Proportional Navigation (LQAPN), som återkopplar missilens acceleration och rotation, uppvisar en bättre prestanda än de mer klassiska styrlagarna. Det främsta intresset med denna studie är därför att undersöka hur tillvida en styrlag baserad på MPC kan mäata sig med dessa resultat. Fördelen med att använda MPC är framförallt att man kan ta hänsyn till styrsignalbegränsningar på ett direkt och intuitivt sätt. </p><p>En nackdel med MPC är beräkningstiden. På senare år har dock forskning bedrivits för att ta fram en variant av MPC som beräknar styrsignalen explicit som en affin funktion av det aktuella tillståndet. Denna metod kallas Explicit MPC och har betraktats som en separat metod i detta arbete. </p><p>Styrlagen baserad på MPC kallas i detta arbete för Model Predictive Control Augmented Proportional Navigation (MPCAPN) och utmärker sig framförallt i två fall. Dels då så kallade händelsestyrda simuleringar studeras, då den uppvisar ett klart bättre resultat än vad som erhålls med en styrlag baserad på Linear Quadratic Augmented Proportional Navigation (LQAPN). Även vid beräkningar av skjutzoner blir resultaten ibland bättre. Framförallt förbättras den inre skjutgränsen för flygscenariet då målet utför en så kallad ”tunnelroll”.</p>

The thesis has been conducted at Bosch Thermoteknik AB and its aim is to develop a Model Predictive Control (MPC) controller for a ground source heat pump which minimizes the power consumption while being able to keep the inside air temperature and Domestic Hot Water (DHW) temperature within certain comfortintervals. First a model of the system is derived, since the system consists of both continuous and binary states a hybrid model is used. The MPC controller utilizes the model to predict the future states of the system, and by formulating an optimizationproblem an optimal control is achieved. The MPC controller is evaluated and compared to a conventional controller using simulations. After some tuning the MPC controller is capable of maintaining the inside air and DHW temperature at their reference levels without oscillating too much. The MPC controller’s general performance is quite similar to the conventional controller, but with a power consumption which is 1-3 % lower. A simulation using an inside air temperature reference which is lowered during the night is also conducted, it achieved a power consumption which was 7.5 % lower compared to a conventional controller.

Arbetet har utförts på Saab Bofors Dynamics AB i Linköping och dess syfte är att undersöka möjligheten att applicera teorin för prediktionsreglering, Model Predictive Control (MPC), på guidance systemet i en missil av typen Medium Range Air-to-Air Missiles (MRAAM). Även implementering via Explicit MPC har undersökts. I tidigare studier har det visat sig att den moderna slutfasstyrningsalgoritmen Linear Quadratic Augmented Proportional Navigation (LQAPN), som återkopplar missilens acceleration och rotation, uppvisar en bättre prestanda än de mer klassiska styrlagarna. Det främsta intresset med denna studie är därför att undersöka hur tillvida en styrlag baserad på MPC kan mäata sig med dessa resultat. Fördelen med att använda MPC är framförallt att man kan ta hänsyn till styrsignalbegränsningar på ett direkt och intuitivt sätt. En nackdel med MPC är beräkningstiden. På senare år har dock forskning bedrivits för att ta fram en variant av MPC som beräknar styrsignalen explicit som en affin funktion av det aktuella tillståndet. Denna metod kallas Explicit MPC och har betraktats som en separat metod i detta arbete. Styrlagen baserad på MPC kallas i detta arbete för Model Predictive Control Augmented Proportional Navigation (MPCAPN) och utmärker sig framförallt i två fall. Dels då så kallade händelsestyrda simuleringar studeras, då den uppvisar ett klart bättre resultat än vad som erhålls med en styrlag baserad på Linear Quadratic Augmented Proportional Navigation (LQAPN). Även vid beräkningar av skjutzoner blir resultaten ibland bättre. Framförallt förbättras den inre skjutgränsen för flygscenariet då målet utför en så kallad ”tunnelroll”.

This work deals with the predictive control algorithms of the AC drives. The introductory section contains summary of current state of theory and further description and classification of most significant predictive algorithms. A separate chapter is dedicated to linear model predictive control (linear MPC). The main contribution of this work is the introduction of two new predictive control algorithm for PMSM motor, both of which are based on linear MPC. The first of these algorithms has been created with the aim of minimizing its computational demands, while the second algorithm introduces the ability of field weakening. Both new algorithms and linear MPC were simulated in MATLAB-Simulink.