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.

By September 2009 the International Atomic Energy Agency reported that the number of commercially operated nuclear reactors in 30 countries across the world is 436, around 50 reactors are currently being constructed, 137 reactors have been ordered or is already planned, and there are around 295 proposed reactors. Pressurised water reactors (PWRs) make up the majority of these numbers. The growing number of carbon emissions and the ongoing fight against fossil fuel power stations might see the number of planned nuclear reactors increase even more to be able to satisfy the world’s need for cleaner energy. To ensure that technology keeps pace with this growing demand, ongoing research is essential. Not only is the research of new reactor technologies (i.e. High Temperature Reactors) important, but improving the current technologies (i.e. PWRs) is critical. With the increased contribution of nuclear generated electricity to our grids, it is becoming more common for nuclear reactors to be operated as load following units, and not base load units as they are more commonly being operated. Therefore a need exists to study and develop new strategies and technologies to improve the automatic load following capabilities of reactors. PWR power plants are multivariable systems. In this study a multivariable, more specifically, a model predictive controller (MPC) is developed for controlling the load following of a nuclear power plant, more specifically a PWR plant. In developing this controller system identification is employed to develop a model of the PWR plant. For the identification of the model, measured data from a computer based PWR simulator is used as the input. The identified plant model is used to develop the MPC controller. The controller is developed and tested on the plant model. The MPC controller is also evaluated against another set of measured data from the simulator. To compare the performance of the MPC controller to that of the conventional controller the ITAE performance index is employed. During the process Matlab ® , the System Identification Toolbox™, the MPC Toolbox™ and Simulink ® are used. The results reveal that MPC is practicable to be used in the control of non-linear systems such as PWR plants. The MPC controller showed good results for controlling the system and also outperformed the conventional controllers. A further result from the dissertation is that system identification can successfully be used to develop models for use in model based controllers like MPC controllers. The results of the research show that a need exists for future research to improve the methods to eventually have a controller that can be applied on a commercial plant. / Thesis (M.Ing. (Nuclear Engineering))--North-West University, Potchefstroom Campus, 2010.

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.

By September 2009 the International Atomic Energy Agency reported that the number of commercially operated nuclear reactors in 30 countries across the world is 436, around 50 reactors are currently being constructed, 137 reactors have been ordered or is already planned, and there are around 295 proposed reactors. Pressurised water reactors (PWRs) make up the majority of these numbers. The growing number of carbon emissions and the ongoing fight against fossil fuel power stations might see the number of planned nuclear reactors increase even more to be able to satisfy the world’s need for cleaner energy. To ensure that technology keeps pace with this growing demand, ongoing research is essential. Not only is the research of new reactor technologies (i.e. High Temperature Reactors) important, but improving the current technologies (i.e. PWRs) is critical. With the increased contribution of nuclear generated electricity to our grids, it is becoming more common for nuclear reactors to be operated as load following units, and not base load units as they are more commonly being operated. Therefore a need exists to study and develop new strategies and technologies to improve the automatic load following capabilities of reactors. PWR power plants are multivariable systems. In this study a multivariable, more specifically, a model predictive controller (MPC) is developed for controlling the load following of a nuclear power plant, more specifically a PWR plant. In developing this controller system identification is employed to develop a model of the PWR plant. For the identification of the model, measured data from a computer based PWR simulator is used as the input. The identified plant model is used to develop the MPC controller. The controller is developed and tested on the plant model. The MPC controller is also evaluated against another set of measured data from the simulator. To compare the performance of the MPC controller to that of the conventional controller the ITAE performance index is employed. During the process Matlab ® , the System Identification Toolbox™, the MPC Toolbox™ and Simulink ® are used. The results reveal that MPC is practicable to be used in the control of non-linear systems such as PWR plants. The MPC controller showed good results for controlling the system and also outperformed the conventional controllers. A further result from the dissertation is that system identification can successfully be used to develop models for use in model based controllers like MPC controllers. The results of the research show that a need exists for future research to improve the methods to eventually have a controller that can be applied on a commercial plant. / Thesis (M.Ing. (Nuclear Engineering))--North-West University, Potchefstroom Campus, 2010.

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.