A two-level optimal coordination control approach for energy storage and conventional generation consisting of advanced frequency control and stochastic optimal dispatch is proposed to deal with the real power balancing control problem introduced by variable renewable energy sources (RESs) in power systems. In the proposed approach, the power and energy constraints on energy storage are taken into account in addition to the traditional power system operational constraints such as generator output limits and power network constraints. The advanced frequency control level which is based on the robust control theory and the decentralized static output feedback design is responsibl e for the system frequency stabilization and restoration, whereas the stochastic optimal dispatch level which is based on the concept of stochastic model predictive control (SMPC) determines the optimal dispatch of generation resources and energy storage under uncertainties introduced by RESs as well as demand. In the advanced frequency control level, low-order decentralized robust frequency controllers for energy storage and conventional generation are simultaneously designed based on a state-space structure-preserving model of the power system and the optimal controller gains are solved via an improved linear matrix inequality algorithm. In the stochastic optimal dispatch level, various optimization decomposition techniques including both primal and dual decompositions together with two different decomposition schemes (i.e. scenario-based decomposition and temporal-based decomposition) are extensively investigated in terms of convergence speed due to the resulting large-scale and computationally demanding SMPC optimization problem. A two-stage mixed decomposition method is conceived to achieve the maximum speedup of the SMPC optimization solution process. The underlying control design philosophy across the entire work is the so-called time-scale matching principle, i.e. the conventional generators are mainly responsible to balance the low frequency components of the power variations whereas the energy storage devices because of their fast response capability are employed to alleviate the relatively high frequency components. The performance of the proposed approach is tested and evaluated by numerical simulations on both the WECC 9-bus system and the IEEE New England 39-bus system.

Current robot manipulation and control paradigms have largely been developed for static or highly structured environments such as those common in factories. For most techniques in robot trajectory generation, such as heuristic-based geometric planning, this has led to putting a high cost on contact with the world. This approach and methodology can be prohibitive to robots operating in many unmodeled and dynamic environments. This dissertation presents work on using haptic based feedback (torque and tactile sensing) to formulate a controller for robot manipulation in clutter. We define “clutter” as any environment in which we expect the robot to make both incidental and purposeful contact while maneuvering and manipulating. The controllers developed in this dissertation take the form of single or multi-time step Model Predictive Control (a form of optimal control which incorporates feedback) which attempts to regulate contact forces at multiple locations on a robot arm while reaching to a goal. The results and conclusions in this dissertation are based on extensive testing in simulation (tens of thousands of trials) and testing in realistic scenarios with real robots incorporating tactile sensing. The approach is novel in the sense that it allows contact and explicitly incorporate the contact and predictive model of the robot arm in calculating control effort at every time step. The expected broader impact of this research is progress towards a new foundation of reactive feedback controllers that will include a higher likelihood of success in many constrained and dynamic scenarios such as reaching into containers without line of sight, maneuvering in cluttered search and rescue situations or working with unpredictable human co-workers.

An increased penetration of renewable energy into the electric power grid is desirable from an environmental standpoint as well as an economical one. However, renewable sources such as wind and solar energy are often variable and intermittent, and additionally, are non-dispatchable. Also, the locations with the highest amount of available wind or solar may be located in areas that are far from areas with high levels of demand, and these areas may be under the control of separate, individual entities. In this dissertation, a method that coordinates these areas, accounts for the variability and intermittency, reduces the impact of renewable energy forecast errors, and increases the overall social benefit in the system is developed. The approach for the purpose of integrating intermittent energy sources into the electric power grid is considered from both the planning and operations stages. In the planning stage, two-stage stochastic optimization is employed to find the optimal size and location for a storage device in a transmission system with the goal of reducing generation costs, increasing the penetration of wind energy, alleviating line congestions, and decreasing the impact of errors in wind forecasts. The size of this problem grows dramatically with respect to the number of variables and constraints considered. Thus, a scenario reduction approach is developed which makes this stochastic problem computationally feasible. This scenario reduction technique is derived from observations about the relationship between the variance of locational marginal prices corresponding to the power balance equations and the optimal storage size. Additionally, a probabilistic, or chance, constrained model predictive control (MPC) problem is formulated to take into account wind forecast errors in the optimal storage sizing problem. A probability distribution of wind forecast errors is formed and incorporated into the original storage sizing problem. An analytical form of this constraint is derived to directly solve the optimization problem without having to use Monte-Carlo simulations or other techniques that sample the probability distribution of forecast errors. In the operations stage, a MPC AC Optimal Power Flow problem is decomposed with respect to physical control areas. Each area performs an independent optimization and variable values on the border buses between areas are exchanged at each Newton-Raphson iteration. Two modifications to the Approximate Newton Directions (AND) method are presented and used to solve the distributed MPC optimization problem, both with the intention of improving the original AND method by improving upon the convergence rate. Methods are developed to account for numerical difficulties encountered by these formula- tions, specifically with regards to Jacobian singularities introduced due to the intertemporal constraints. Simulation results show convergence of the decomposed optimization problem to the centralized result, demonstrating the benefits of coordinating control areas in the IEEE 57- bus test system. The benefit of energy storage in MPC formulations is also demonstrated in the simulations, reducing the impact of the fluctuations in the power supply introduced by intermittent sources by coordinating resources across control areas. An overall reduction of generation costs and increase in renewable penetration in the system is observed, with promising results to effectively and efficiently integrate renewable resources into the electric power grid on a large scale.

Wireless networks in industrial process control enable new system architectures and designs. However, wireless control systems can be severely affected by the imperfections of the communication links. This thesis proposes new methods to handle such imperfections by adding additional components in the control loop, or by adapting sampling intervals and control actions. First, the predictive outage compensator is proposed. It is a filter which is implemented at the receiver side of networked control systems. There it generates predicted samples when data are lost, based on past data. The implementation complexity of the predictive outage compensator is analyzed. Simulation and experimental results show that it can considerably improve the closed-loop control performance under communication losses. The thesis continues with presenting an algorithm for controlling multiple processes on a shared communication network, using adaptive sampling intervals. The methodology is based on model predictive control, where the controller jointly decides the optimal control signal to be applied as well as the optimal time to wait before taking the next sample. The approach guarantees conflict-free network transmissions for all controlled processes. Simulation results show that the presented control law reduces the required amount of communication, while maintaining control performance. The third contribution of the thesis is an event-triggered model predictive controller for use over a wireless link. The controller uses open-loop optimal control, re-computed and communicated only when the system behavior deviates enough from a prediction. Simulations underline the methods ability to significantly reduce computation and communication effort, while guaranteeing a desired level of system performance. The thesis is concluded by an experimental validation of wireless control for a physical lab process. A hybrid model predictive controller is used, connected to the physical system through a wireless medium. The results reflect the advantages and challenges in wireless control. / <p>QC 20140217</p>

In this study, the performance of the Model Predictive Controller (MPC) algorithm is investigated in two different reaction systems. The first case is a saponification reaction system where ethyl acetate reacts with sodium hydroxide to produce sodium acetate and ethanol in a CSTR. In the reactor, temperature and sodium acetate concentration are controlled by manipulating the flow rates of ethyl acetate and cooling water. The model of the reactor is developed considering first principal models. The experiments are done to obtain steady state data from the reaction system and these are compared with the model outputs to find the unknown parameters of the model. Then, the developed model is used for designing SISO and MIMO-MPC considering Singular Value Decomposition (SVD) technique for coupling. The second case is the reaction system used for the production of boric acid by the reaction of colemanite and sulfuric acid in four CSTR&rsquo / s connected in series. In the reactor, the boric acid concentration in the fourth reactor is controlled by manipulating the sulfuric acid flow rate fed to the reactor. The transfer functions of the process and disturbance (colemanite flow rate) are obtained experimentally by giving step changes to the manipulated variable and to the disturbance. A model-based and constrained SISO-MPC is designed utilizing linear step response coefficients. The designed controllers are tested for performance in set point tracking, disturbance rejection and robustness issues for the two case studies. It is found that, they are satisfactory except in robustness issues for disturbance rejection in boric acid system.

Research Doctorate - Doctor of Philosophy (PhD) / This dissertation presents some new approaches to addressing the main issues encountered by practitioners in the implementation of linear model predictive control(MPC), namely, stability, feasibility, complexity and the size of the region of attraction. When stability guaranteeing techniques are applied nominal feasibility is also guaranteed. The most common technique for guaranteeing stability is to apply a special weighting to the terminal state of the MPC formulation and to constrain the state to a terminal region where certain properties hold. However, the combination of terminal state constraints and the complexity of the MPC algorithm result in regions of attraction that are relatively small. Small regions of attraction are a major problem for practitioners. The main approaches used to address this issue are either via the reduction of complexity or the enlargement of the terminal region. Although the ultimate goal is to enlarge the region of attraction, none of these techniques explicitly consider the upper bound of this region. Ideally the goal is to achieve the largest possible region of attraction which for constrained systems is the null controllable set. For the case of systems with a single unstable pole or a single non-minimum phase zero their null controllable sets are defined by simple bounds which can be thought of as implicit constraints. We show in this thesis that adding implicit constraints to MPC can produce maximally controllable systems, that is, systems whose region of attraction is the null controllable set. For higher dimensional open-loop unstable systems with more than one real unstable mode, the null controllable sets belong to a class of polytopes called zonotopes. In this thesis, the properties of these highly structured polytopes are used to implement a new variant of MPC, which we term reduced parameterisation MPC (RP MPC). The proposed new strategy dynamically determines a set of contractive positively invariant sets that require only a small number of parameters for the optimisation problem posed by MPC. The worst case complexity of the RP MPC strategy is polylogarithmic with respect to the prediction horizon. This outperforms the most efficient on-line implementations of MPC which have a worst case complexity that is linear in the horizon. Hence, the reduced complexity allows the resulting closed-loop system to have a region of attraction approaching the null controllable set and thus the goal of maximal controllability.

Agile autonomous systems are emerging, such as unmanned aerial vehicles (UAVs), that must robustly perform tightly coordinated time-critical missions; for example, military surveillance or search-and-rescue scenarios. In the space domain, execution of temporally flexible plans has provided an enabler for achieving the desired coordination and robustness, in the context of space probes and planetary rovers, modeled as discrete systems. We address the challenge of extending plan execution to systems with continuous dynamics, such as air vehicles and robot manipulators, and that are controlled indirectly through the setting of continuous state variables.Systems with continuous dynamics are more challenging than discrete systems, because they require continuous, low-level control, and cannot be controlled by issuing simple sequences of discrete commands. Hence, manually controlling these systems (or plants) at a low level can become very costly, in terms of the number of human operators necessary to operate the plant. For example, in the case of a fleet of UAVs performing a search-and-rescue scenario, the traditional approach to controlling the UAVs involves providing series of close waypoints for each aircraft, which incurs a high workload for the human operators, when the fleet consists of a large number of vehicles.Our solution is a novel, model-based executive, called Sulu, that takes as input a qualitative state plan, specifying the desired evolution of the state of the system. This approach elevates the interaction between the human operator and the plant, to a more abstract level where the operator is able to “coach” the plant by qualitatively specifying the tasks, or activities, the plant must perform. These activities are described in a qualitative manner, because they specify regions in the plant’s state space in which the plant must be at a certain point in time. Time constraints are also described qualitatively, in the form of flexible temporal constraints between activities in the state plan. The design of low-level control inputs in order to meet this abstract goal specification is then delegated to the autonomous controller, hence decreasing the workload per human operator. This approach also provides robustness to the executive, by giving it room to adapt to disturbances and unforeseen events, while satisfying the qualitative constraints on the plant state, specified in the qualitative state plan.Sulu reasons on a model of the plant in order to dynamically generate near-optimal control sequences to fulfill the qualitative state plan. To achieve optimality and safety, Sulu plans into the future, framing the problem as a disjunctive linear programming problem. To achieve robustness to disturbances and maintain tractability, planning is folded within a receding horizon, continuous planning and execution framework. The key to performance is a problem reduction method based on constraint pruning. We benchmark performance using multi-UAV firefighting scenarios on a real-time, hardware-in-the-loop testbed. / SM thesis

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.

This thesis presents the work conducted during a three year research project in the field of Control Systems and Biomaterials Engineering. The findings are presented over seven chapters, starting with a thorough literature review of the existing methods and key technologies, and following through by highlighting the existing problems with the current methods and how they have been overcome. The data is presented in tables, figures and photographs to enhance understanding and clarification. The research focuses on two relatively new manufacturing methods in the field of Tissue Engineering. Both of the methods are used for creating materials for regeneration of human and animal tissue, with the aim of replacing the current surgical methods. The methods are viewed from a control systems perspective and improvements have been made with the implementation of new technologies and methods. Additionally, further advancements are presented on the theoretical modelling field of control systems, where the shortfalls of existent modelling methods are highlighted and solutions proposed.

Un des plus grands défis dans la commande des robots est de combler l'écart entre la capacité de mouvement de l'humain et des robots humanoïdes. La difficulté réside dans la complexité des systèmes dynamiques représentant les robots humanoïdes: la non linéarité, le sous-actionnement, le comportement non-lisse en raison de collisions et de frottement, le nombre élevé de degrés de liberté. De plus, les robots humanoïdes sont censés opérer dans des environnements non-déterministes, qui exigent une commande temps réel avancée.L'approche qui prévaut actuellement pour faire face à ces difficultés est d'imposer diverses restrictions sur les mouvements et d'employer des modèles approximatifs des robots. Dans cette thèse, nous suivons la même ligne de recherche et proposons une nouvelle approche pour la conception de contrôleurs corps entier qui préservent l'équilibre. L'idée principale est de tirer parti des avantages des modèles approximatifs et de corps entier en les mélangeant dans un seul problème de contrôle prédictif avec des objectifs strictement hiérarchisés.La préservation de l'équilibre est l'une des principales préoccupations dans la commande des robots humanoïdes. Des recherches antérieures ont déjà établi que l'anticipation des mouvements est essentiel à cet effet. Nous préconisons que l'anticipation est utile dans ce sens comme un moyen de maintenir la capturabilité du mouvement, i.e., la capacité de s'arrêter. Nous soulignons que capturabilité des mouvements prévus peut être imposée avec des contraintes appropriées. Dans la pratique, il est fréquent d'anticiper les mouvements du robot à l'aide de modèles approximatifs afin de réduire l'effort de calcul, par conséquent, un contrôleur séparé de mouvement du corps entier est nécessaire pour le suivi. Au lieu de cela, nous proposons d'introduire l'anticipation avec un modèle approximatif directement dans le contrôleur corps entier. En conséquence, les mouvements du corps entier générés respectent les contraintes de capturabilité et les mouvements anticipes du modèle approximatif prennent en compte les contraintes et les tâches désirées pour le corps entier. Nous posons nos contrôleurs du mouvement du corps entier comme des problèmes d'optimisation avec des objectifs strictement hiérarchisés. Bien que cet ordre de priorité soit commun dans la littérature, nous croyons qu'il est souvent mal exploité.Par conséquent, nous proposons plusieurs exemples de contrôleurs, où la hiérarchisation est utile et nécessaire pour atteindre les comportements souhaités. Nous évaluons nos contrôleurs dans deux scénarios simulés, où la tâche du corps entier du robot influence la marche et le robot exploite éventuellement un contact avec la main pour maintenir son équilibre en étant debout. / One of the greatest challenges in robot control is closing the gap between themotion capabilities of humans and humanoid robots. The difficulty lies in thecomplexity of the dynamical systems representing the said robots: theirnonlinearity, underactuation, discrete behavior due to collisions and friction,high number of degrees of freedom. Moreover, humanoid robots are supposed tooperate in non-deterministic environments, which require advanced real timecontrol. The currently prevailing approach to coping with these difficulties isto impose various limitations on the motions and employ approximate models ofthe robots. In this thesis, we follow the same line of research and propose anew approach to the design of balance preserving whole body motion controllers.The key idea is to leverage the advantages of whole body and approximate modelsby mixing them within a single predictive control problem with strictlyprioritized objectives.Balance preservation is one of the primary concerns in the control of humanoidrobots. Previous research has already established that anticipation of motionsis crucial for this purpose. We advocate that anticipation is helpful in thissense as a way to maintain capturability of the motion, i.e., the ability tostop. We stress that capturability of anticipated motions can be enforced withappropriate constraints. In practice, it is common to anticipate motions usingapproximate models in order to reduce computational effort, hence, a separatewhole body motion controller is needed for tracking. Instead, we propose tointroduce anticipation with an approximate model into the whole body motioncontroller. As a result, the generated whole body motions respect thecapturability constraints and the anticipated motions of an approximate modeltake into account whole body constraints and tasks. We pose our whole bodymotion controllers as optimization problems with strictly prioritizedobjectives. Though such prioritization is common in the literature, we believethat it is often not properly exploited. We, therefore, propose severalexamples of controllers, where prioritization is useful and necessary toachieve desired behaviors. We evaluate our controllers in two simulatedscenarios, where a whole body task influences walking motions of the robot andthe robot optionally exploits a hand contact to maintain balance whilestanding.