Combined heat and power (CHP) is a technology that decreases total fuel consumption and related greenhouse gas emissions by producing both electricity and useful thermal energy from a single energy source. In the industrial and commercial sectors, a typical CHP site relies upon the electricity distribution network for significant periods, i.e., for purchasing power from the grid during periods of high demand or when off-peak electricity tariffs are available. On the other hand, in some cases, a CHP plant is allowed to sell surplus power to the grid during on-peak hours when electricity prices are highest while all operating constraints and local demands are satisfied. Therefore, if the plant is connected with the external grid and allowed to participate in open energy markets in the future, it could yield significant economic benefits by selling/buying power depending on market conditions. This is achieved by solving the power system generation scheduling problem using mathematical programming. In this work, we present the application of mixed-integer nonlinear programming (MINLP) approach for scheduling of a CHP plant in the day-ahead wholesale energy markets. This work employs first principles models to describe the nonlinear dynamics of a CHP plant and its individual components (gas and steam turbines, heat recovery steam generators, and auxiliary boilers). The MINLP framework includes practical constraints such as minimum/maximum power output and steam flow restrictions, minimum up/down times, start-up and shut-down procedures, and fuel limits. We provide case studies involving the Hal C. Weaver power plant complex at the University of Texas at Austin to demonstrate this methodology. The results show that the optimized operating strategies can yield substantial net incomes from electricity sales and purchases. This work also highlights the application of a nonlinear model predictive control scheme to a heavy-duty gas turbine power plant for frequency and temperature control. This scheme is compared to a classical PID/logic based control scheme and is found to provide superior output responses with smaller settling times and less oscillatory behavior in response to disturbances in electric loads. / text

We consider the solution of nonlinear optimal control problems subject to stochastic perturbations with incomplete observations. In particular, we generalize results obtained by Ito and Kunisch in [8] where they consider a receding horizon control (RHC) technique based on linearizing the problem on small intervals. The linear-quadratic optimal control problem for the resulting time-invariant (LTI) problem is then solved using the linear quadratic Gaussian (LQG) design. Here, we allow linearization about an instationary reference trajectory and thus obtain a linear time-varying (LTV) problem on each time horizon. Additionally, we apply a model predictive control (MPC) scheme which can be seen as a generalization of RHC and we allow covariance matrices of the noise processes not equal to the identity. We illustrate the MPC/LQG approach for a three dimensional reaction-diffusion system. In particular, we discuss the benefits of time-varying linearizations over time-invariant ones.

The goal of this dissertation is to evaluate the effectiveness of model predictive control (MPC) for a magnetically suspended flywheel energy storage uninterruptible power supply (FlyUPS). The reason this research topic was selected was to determine if an advanced control technique such as MPC could perform better than a classical control approach such as decentralised Proportional-plus-Differential (PD) control. Based on a literature study of the FlyUPS system and the MPC strategies available, two MPC strategies were used to design two possible MPC controllers were designed for the FlyUPS, namely a classical MPC algorithm that incorporates optimisation techniques and the MPC algorithm used in the MATLAB® MPC toolbox™. In order to take the restrictions of the system into consideration, the model used to derive the controllers was reduced to an order of ten according to the Hankel singular value decomposition of the model. Simulation results indicated that the first controller based on a classical MPC algorithm and optimisation techniques was not verified as a viable control strategy to be implemented on the physical FlyUPS system due to difficulties obtaining the desired response. The second controller derived using the MATLAB® MPC toolbox™ was verified to be a viable control strategy for the FlyUPS by delivering good performance in simulation. The verified MPC controller was then implemented on the FlyUPS. This implementation was then analysed in order to validate that the controller operates as expected through a comparison of the simulation and implementation results. Further analysis was then done by comparing the performance of MPC with decentralised PD control in order to determine the advantages and limitations of using MPC on the FlyUPS. The advantages indicated by the evaluation include the simplicity of the design of the controller that follows directly from the specifications of the system and the dynamics of the system, and the good performance of the controller within the parameters of the controller design. The limitations identified during this evaluation include the high computational load that requires a relatively long execution time, and the inability of the MPC controller to adapt to unmodelled system dynamics. Based on this evaluation MPC can be seen as a viable control strategy for the FlyUPS, however more research is needed to optimise the MPC approach to yield significant advantages over other control techniques such as decentralised PD control. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013

A cyber-security problem is a conflict-resolution scenario that typically consists of a security system and at least two decision makers (e.g. attacker and defender) that can each have competing objectives. In this thesis, we are interested in cyber-security problems where one decision maker has superior or better information. Game theory is a well-established mathematical tool that can be used to analyze such problems and will be our tool of choice. In particular, we will formulate cyber-security problems as stochastic games with asymmetric information, where game-theoretic methods can then be applied to the problems to derive optimal policies for each decision maker. A severe limitation of considering optimal policies is that these policies are computationally prohibitive. We address the complexity issues by introducing methods, based on the ideas of model predictive control, to compute suboptimal polices. Specifically, we first prove that the methods generate suboptimal policies that have tight performance bounds. We then show that the suboptimal polices can be computed by solving a linear program online, and the complexity of the linear program remains constant with respect to the game length. Finally, we demonstrate how the suboptimal policy methods can be applied to cyber-security problems to reduce the computational complexity of forecasting cyber-attacks.

This thesis consists of four independent papers concerningthe control of mobile robots in the context of obstacleavoidance and formation keeping. The first paper describes a new theoreticallyv erifiableapproach to obstacle avoidance. It merges the ideas of twoprevious methods, with complementaryprop erties, byusing acombined control Lyapunov function (CLF) and model predictivecontrol (MPC) framework. The second paper investigates the problem of moving a fixedformation of vehicles through a partiallykno wn environmentwith obstacles. Using an input to state (ISS) formulation theconcept of configuration space obstacles is generalized toleader follower formations. This generalization then makes itpossible to convert the problem into a standard single vehicleobstacle avoidance problem, such as the one considered in thefirst paper. The properties of goal convergence and safetyth uscarries over to the formation obstacle avoidance case. In the third paper, coordination along trajectories of anonhomogenuos set of vehicles is considered. Byusing a controlLyapunov function approach, properties such as boundedformation error and finite completion time is shown. Finally, the fourth paper applies a generalized version ofthe control in the third paper to translate,rotate and expanda formation. It is furthermore shown how a partial decouplingof formation keeping and formation mission can be achieved. Theapproach is then applied to a scenario of underwater vehiclesclimbing gradients in search for specific thermal/biologicalregions of interest. The sensor data fusion problem fordifferent formation configurations is investigated and anoptimal formation geometryis proposed. Keywords:Mobile Robots, Robot Control, ObstacleAvoidance, Multirobot System, Formation Control, NavigationFunction, Lyapunov Function, Model Predictive Control, RecedingHorizon Control, Gradient Climbing, Gradient Estimation. / QC 20111121

This research work provides the development of an Adaptive Flight Control System (AFCS) for autonomous hover of a Rotary-wing Unmanned Aerial Vehicle (RUAV). Due to the complex, nonlinear and time-varying dynamics of the RUAV, indirect adaptive control using the Model Predictive Control (MPC) is utilised. The performance of the MPC mainly depends on the model of the RUAV used for predicting the future behaviour. Due to the complexities associated with the RUAV dynamics, a neural network based black box identification technique is used for modelling the behaviour of the RUAV. Auto-regressive neural network architecture is developed for offline and online modelling purposes. A hybrid modelling technique that exploits the advantages of both the offline and the online models is proposed. In the hybrid modelling technique, the predictions from the offline trained model are corrected by using the error predictions from the online model at every sample time. To reduce the computational time for training the neural networks, a principal component analysis based algorithm that reduces the dimension of the input training data is also proposed. This approach is shown to reduce the computational time significantly. These identification techniques are validated in numerical simulations before flight testing in the Eagle and RMAX helicopter platforms. Using the successfully validated models of the RUAVs, Neural Network based Model Predictive Controller (NN-MPC) is developed taking into account the non-linearity of the RUAVs and constraints into consideration. The parameters of the MPC are chosen to satisfy the performance requirements imposed on the flight controller. The optimisation problem is solved numerically using nonlinear optimisation techniques. The performance of the controller is extensively validated using numerical simulation models before flight testing. The effects of actuator and sensor delays and noises along with the wind gusts are taken into account during these numerical simulations. In addition, the robustness of the controller is validated numerically for possible parameter variations. The numerical simulation results are compared with a base-line PID controller. Finally, the NN-MPCs are flight tested for height control and autonomous hover. For these, SISO as well as multiple SISO controllers are used. The flight tests are conducted in varying weather conditions to validate the utility of the control technique. The NN-MPC in conjunction with the proposed hybrid modelling technique is shown to handle additional disturbances successfully. Extensive flight test results provide justification for the use of the NN-MPC technique as a reliable technique for control of non-linear complex dynamic systems such as RUAVs.

This thesis addresses the problems of design and control of small cooperative unmanned autonomous quadrotor aerial vehicles. A new approach is proposed, for the modeling of the system’s dynamics using linearized Piecewise AffineModels. The Piecewise Affine dynamic–models cover a large part of the quadrotor’s flight envelope while also taking into account the additive effects of environmental disturbances. The effects of aerodynamic forces and moments were also examined. A small quadrotor is designed and developed that emphasizes in the areas of increased on–board computational capabilities, state estimation and modular connectivity. Based on the translational and rotational system’s dynamics: a) a switching model predictive controller, b) an explicitly solved constrained finite time optimal control strategy, and c) a cascade control scheme comprised of classical Proportional Integral Derivative control scheme augmented with angular acceleration feedback, were designed and experimentally tested in order to achieve trajectory tracking under the presence of wind–gusts. The efficiency of the proposed control methods was verified through extended experimental studies. The final quadrotor design utilizes a powerful control unit, a sensor system that provides state estimation based on inertial sensors, ultrasound sonars, GPS and vision chips, and an efficient actuating system. The research effort extended in the field of unmanned aerial vehicles cooperation. Cooperation strategies were proposed in order to address the problems of: a) Forest Fire Monitoring and b) Unknown Area Exploration and Target Acquisition. The Forest FireMonitoring algorithm is formulated based on consensus systems theory formulated as a spatiotemporal rendezvous problem in between the quadrotors. The Area Exploration and Target Acquisition algorithm is formulated based on market–based approaches. / Η συγκεκριμένη διατριβή καταπιάνεται με τα προβλήματα της σχεδίασης και ελέγχου μικρού μεγέθους συνεργαζόμενων μη επανδρωμένων αεροσκαφών με έμφαση στα συστήματα Κάθετης Απογείωσης και Προσγείωσης και ιδιαίτερα στη συστήματα τύπου Quadrotor. Μια νέα προσέγγιση για την μοντελοποίηση της δυναμικής του συστήματος η οποία βασίζεται στη θεωρία των Piecewise Affine συστημάτων προτείνεται. Η μοντελοποίηση με βάση τη θεωρία των Piecewise Affine συστημάτων καλύπτει ένα μεγάλο μέρος του φακέλου πτήσης του αεροσκάφους καθότι συνυπολογίζει μέρος της μη-γραμμικότητας του συστήματος ενώ παράλληλα δίνει τη δυνατότητα να χρησιμοποιηθούν τα ιδιαίτερα ανεπτυγμένα εργαλεία του γραμμικού ελέγχου. Αναπτύσσεται νέα πειραματική πλατφόρμα αεροσκάφους τύπου quadrotor η οποία χαρακτηρίζεται από ιδιαίτερες ικανότητες υπολογιστικής ισχύος, αυτόνομη εκτίμηση κατάστασης, πολλαπλή συνδεσιμότητα και αποδοτικό σύστημα πρόωσης. Η τελική πλατφόρμα quadrotor ελικοπτέρου UPATcopter ενσωματώνει μικρουπολογιστικό σύστημα υψηλών δυνατοτήτων, ειδικά συστήματα εκτίμησης κατάστασης τόσο σε εσωτερικούς όσο και σε εξωτερικούς χώρους μέρος των οποίων αναπτύχθηκε στα πλαίσια της διατριβής και αποδοτικό υποσύστημα πρόωσης. Τρεις διαφορετικοί νόμοι ελέγχου αναπτύχθηκαν και δοκιμάστηκαν πειραματικά. Αρχικά δοκιμάσθηκε ένας Constrained Finite Time Optimal Controller, ο οποίος υπολογίζεται πολύ-παραμετρικά και συνυπολογίζει την επίδραση των περιορισμών εισόδου και κατάστασης. Ο συγκεκριμένος ελεγκτής υπολογίσθηκε με βάση μια οικογένεια Piecewise Affine αναπαραστάσεων του υποσυστήματος προσανατολισμού και δοκιμάσθηκε επιτυγχάνοντας αποδοτικό έλεγχο του προσανατολισμού του σκάφους. Ακολούθως δοκιμάσθηκε ένας Switching Model Predictive Control βασισμένος στην Piecewise Affine μοντελοποίηση του συστήματος ο οποίος επίσης συνυπολογίζει την επίδραση των περιορισμών του συστήματος και του ρόλου των διαταραχών. Με τη χρήση αυτού του ελεγκτή επιτεύχθηκε έλεγχος προσανατολισμού και θέσης του αεροσκάφους τόσο σε άπνοια όσο και υπό την επίδραση ισχυρών διαταραχών ανέμου. Επιπρόσθετα, δοκιμάσθηκε ελεγκτής βασισμένος στη θεωρία PID ελέγχου επαυξημένος με ανάδραση γωνιακής επιτάχυνσης του συστήματος. Τέλος, η έρευνα επεκτάθηκε και στις στρατηγικές συνεργασίας μη επανδρωμένων αεροσκαφών προτείνοντας δύο αλγόριθμους. Συγκεκριμένα προτάθηκε αλγόριθμος για την αντιμετώπιση των προβλημάτων επιθεώρησης δασικής πυρκαγιάς και αλγόριθμος εξερεύνησης μιας άγνωστης περιοχής από ομάδα ετερογενών αεροσκαφών.

Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: The development of fast and efficient processors, programmable devices and high power semiconductors has led to the increased use of semiconductors directly in the power supply path in order to achieve strict power quality standards. New and advanced algorithms are used in the process and calculated on-line to bring about the required fast response to voltage variations. Losses in high voltage semiconductors increase with increased operating frequencies. A balance between semiconductor power losses and power quality is achieved through control of power semiconductor switching frequencies. A predictive control algorithm to achieve high power quality and limit the power losses in the high power semiconductor switches through switching frequency control is discussed for a tap switched voltage regulator. The quality of power, voltage regulator topology and the control algorithm are discussed. Simulation results of output voltage and current are shown when the control algorithm is used to control the regulator. These results are verified by practical measurements on a synchronous buck converter. / AFRIKAANSE OPSOMMING: Die ontwikkeling van vinnige en doeltreffende verwerkers, programmeerbare toestelle en hoëdrywings halfgeleiers het gelei tot 'n groter gebruik van halfgeleiers direk in die kragtoevoer pad om streng elektriese toevoer kwaliteit standaarde te bereik. Nuwe en gevorderde algoritmes word gebruik in die proses en word aan-lyn bereken om die nodige vinnige reaksie tot spanningswisselinge te gee. Verliese in hoë-spannings halfgeleiers verhoog met hoër skakel frekwensies. 'n Balans tussen die halfgeleier drywingsverliese en spanningskwalteit is behaal deur die skakel frekwensie in ag te neem in die beheer. 'n Voorspellinde-beheer algoritme om ‘n hoë toevoerkwaliteit te bereik en die drywingsverliese in die hoëdrywingshalfgeleier te beperk, deur skakel frekwensie te beheer, is bespreek vir 'n tap-geskakelde spanning reguleerder. Die toevoerkwaliteit, spanningsreguleerder topologie en die beheer algoritme word bespreek. Simulasie resultate van die uittree-spanning en stroom word getoon wanneer die beheer algoritme gebruik word om die omsetter te beheer. Hierdie resultate is deur praktiese metings op 'n sinkrone afkapper.

Nous observons ces dernières années un besoin grandissant dans l’industrie pour des robots capables d’interagir et de coopérer dans des environnements confinés. Cependant, aujourd’hui encore, la définition de trajectoires sûres pour les robots industriels doit être faite manuellement par l’utilisateur et le logiciel ne dispose que de peu d’autonomie pour réagir aux modifications de l’environnement. Cette thèse vise à produire une structure logicielle innovante pour gérer l’évitement d’obstacles en temps réel pour des robots manipulateurs évoluant dans des environnements dynamiques. Nous avons développé pour cela un algorithme temps réel de génération de trajectoires qui supprime de façon automatique l’étape fastidieuse de définition d’une trajectoire sûre pour le robot.La valeur ajoutée de cette thèse réside dans le fait que nous intégrons le problème de contrôle optimal dans le concept de hiérarchie de tâches pour résoudre un problème d’optimisation non-linéaire efficacement et en temps réel sur un système embarqué aux ressources limitées. Notre approche utilise une commande prédictive (MPC) qui non seulement améliore la réactivité de notre système mais présente aussi l’avantage de pouvoir produire une bonne approximation linéaire des contraintes d’évitement de collision. La stratégie de contrôle présentée dans cette thèse a été validée à l’aide de plusieurs expérimentations en simulations et sur systèmes réels. Les résultats démontrent l’efficacité, la réactivité et la robustesse de cette nouvelle structure de contrôle lorsqu’elle est utilisée dans des environnements dynamiques. / In the field of industrial robots, there is a growing need for having cooperative robots that interact with each other and share work spaces. Currently, industrial robotic systems still rely on hard coded motions with limited ability to react autonomously to dynamic changes in the environment. This thesis focuses on providing a novel framework to deal with real-time collision avoidance for robots performing tasks in a dynamic environment. We develop a reactive trajectory generation algorithm that reacts in real time, removes the fastidious optimization process which is traditionally executed by hand by handling it automatically, and provides a practical way of generating locally time optimal solutions.The novelty in this thesis is in the way we integrate the proposed time optimality problem in a task priority framework to solve a nonlinear optimization problem efficiently in real time using an embedded system with limited resources. Our approach is applied in a Model Predictive Control (MPC) setting, which not only improves reactivity of the system but presents a possibility to obtain accurate local linear approximations of the collision avoidance constraint. The control strategies presented in this thesis have been validated through various simulations and real-world robot experiments. The results demonstrate the effectiveness of the new control structure and its reactivity and robustness when working in dynamic environments.

L'énergie est une ressource clé dans les réseaux de capteurs sans fil (WSNs), en particulier lorsque les nœuds capteurs sont alimentés par des batteries. Cette thèse s'inscrit dans le contexte de la réduction de la consommation de l'énergie d'un réseau de capteurs au niveau application construite au-dessus de ce réseau, grâce à des stratégies de contrôle, en temps réel et de façon dynamique. La première stratégie de gestion de l'énergie considérée s'appuie sur le contrôle prédictif (MPC). Le choix de MPC est motivé par les objectifs globaux qui sont de réduire la consommation d'énergie de l'ensemble des nœuds capteurs tout en assurant un service donné, nommé mission, pour le réseau de capteurs. En outre, un ensemble de contraintes sur les variables de contrôle binaires et sur les nœuds capteur doit être rempli. La deuxième stratégie de gestion de l'énergie au niveau de l'application utilise une approche de contrôle hybride (HDS). Ce choix est motivé par la nature inhérente du système WSN qui est par essence hybride, en particulier lorsque l'on s'intéresse à la gestion de l'énergie. La nature hybride vient essentiellement de la combinaison de processus physiques continus tels la charge et décharge des batteries des nœuds; tandis que la partie discrète est liée à la modification des modes de fonctionnement et l'état Inaccessible des nœuds. Les stratégies proposées sont évaluées et comparées en simulation sur des différents scenarios réalistes. Elles ont aussi \'et\'e mises en œuvre sur un banc d'essai réel et les résultats obtenus ont été discutés. / Energy is a key resource in Wireless Sensor Networks (WSNs), especially when sensor nodes are powered by batteries. This thesis is investigates how to save energy of the whole WSN, at the application level, thanks to control strategies, in real time and in a dynamic way. The first energy management strategy investigated is based on Model Predictive Control (MPC). The choice of MPC is motivated by the global objectives that are to reduce the energy consumption of the set of sensor nodes while ensuring a given service, named mission, for the sensor network. Moreover, a set of constraints on the binary control variables and on the sensor modes must be fulfilled. The second energy management strategy at the application level is based on a Hybrid Dynamical System (HDS) approach. This choice is motivated by the hybrid inherent nature of the WSN system when energy management is considered. The hybrid nature basically comes from the combination of continuous physical processes, namely, the charge / discharge of the node batteries; while the discrete part is related to the change in the functioning modes and the Unreachable condition of the nodes. The proposed strategies are evaluated and compared in simulation on a realistic test-case. Lastly, they have been implemented on a real test-bench and the results obtained have been discussed.