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The deployment of Renewable Energy (RE)-based generation has experienced a sustained global growth in the recent decades, driven by many countries' interest in reducing greenhouse gas emissions and dependence on fossil fuel for electricity generation. This trend is also observed in remote off-grid systems (isolated microgrids), where local communities, in an attempt to reduce fossil fuel dependency and associated economic and environmental costs, and to increase availability of electricity, are favouring the installation of RE-based generation. This practice has posed several challenges to the operation of such systems, due to the intermittent and hard-to-predict nature of RE sources. In particular, this thesis addresses the problem of reliable and economic dispatch of isolated microgrids, also known as the energy management problem, considering the uncertain nature of those RE sources, as well as loads. Isolated microgrids feature characteristics similar to those of distribution systems, in terms of unbalanced power flows, significant voltage drops and high power losses. For this reason, detailed three-phase mathematical models of the microgrid system and components are presented here, in order to account for the impact of unbalanced system conditions on the optimal operation of the microgrid. Also, simplified three-phase models of Distributed Energy Resources (DERs) are developed to reduce the level of complexity in small units that have limited impact on the optimal operation of the system, thus reducing the number of equations and variables of the problem. The proposed mathematical models are then used to formulate a novel energy management problem for isolated microgrids, as a deterministic, multi-period, Mixed-Integer Nonlinear Programming (MINLP) problem. The multi-period formulation allows for a proper management of energy storage resources and multi-period constraints associated with the commitment decisions of DERs. In order to obtain solutions of the energy management problem in reasonable computational times for real-time, realistic applications, and to address the uncertainty issues, the proposed MINLP formulation is decomposed into a Mixed-Integer Linear Programming (MILP) problem, and a Nonlinear programming (NLP) problem, in the context of a Model Predictive Control (MPC) approach. The MILP formulation determines the unit commitment decisions of DERs using a simplified model of the network, whereas the NLP formulation calculates the detailed three-phase dispatch of the units, knowing the commitment status. A feedback signal is generated by the NLP if additional units are required to correct reactive power problems in the microgrid, triggering a new calculation MINLP problem. The proposed decomposition and calculation routines are used to design a new deterministic Energy Management System (EMS) based on the MPC approach to handle uncertainties; hence, the proposed deterministic EMS is able to handle multi-period constraints, and account for the impact of future system conditions in the current operation of the microgrid. In the proposed methodology, uncertainty associated with the load and RE-based generation is indirectly considered in the EMS by continuously updating the optimal dispatch solution (with a given time-step), based on the most updated information available from suitable forecasting systems. For a more direct modelling of uncertainty in the problem formulation, the MILP part of the energy management problem is re-formulated as a two-stage Stochastic Programming (SP) problem. The proposed novel SP formulation considers that uncertainty can be properly modelled using a finite set of scenarios, which are generated using both a statistical ensembles scenario generation technique and historical data. Using the proposed SP formulation of the MILP problem, the deterministic EMS design is adjusted to produce a novel stochastic EMS. The proposed EMS design is tested in a large, realistic, medium-voltage isolated microgrid test system. For the deterministic case, the results demonstrate the important connection between the microgrid's imbalance, reactive power requirements and optimal dispatch, justifying the need for detailed three-phase models for EMS applications in isolated microgrids. For the stochastic studies, the results show the advantages of using a stochastic MILP formulation to account for uncertainties associated with RE sources, and optimally accommodate system reserves. The computational times in all simulated cases show the feasibility of applying the proposed techniques to real-time, autonomous dispatch of isolated microgrids with variable RE sources.

Advances in hardware design have made wheeled mobile robots (WMRs) exceptionally mobile. To fully exploit this mobility, WMR planning, control, and estimation systems require motion models that are fast and accurate. Much of the published theory on WMR modeling is limited to 2D or kinematics, but 3D dynamic (or force-driven) models are required when traversing challenging terrain, executing aggressive maneuvers, and manipulating heavy payloads. This thesis advances the state of the art in both the formulation and calibration of WMR models We present novel WMR model formulations that are high-fidelity, general, modular, and fast. We provide a general method to derive 3D velocity kinematics for any WMR joint configuration. Using this method, we obtain constraints on wheel ground contact point velocities for our differential algebraic equation (DAE)-based models. Our “stabilized DAE” kinematics formulation enables constrained, drift free motion prediction on rough terrain. We also enhance the kinematics to predict nonzero wheel slip in a principled way based on gravitational, inertial, and dissipative forces. Unlike ordinary differential equation (ODE)-based dynamic models which can be very stiff, our constrained dynamics formulation permits large integration steps without compromising stability. Some alternatives like Open Dynamics Engine also use constraints, but can only approximate Coulomb friction at contacts. In contrast, we can enforce realistic, nonlinear models of wheel-terrain interaction (e.g. empirical models for pneumatic tires, terramechanics-based models) using a novel force-balance optimization technique. Simulation tests show our kinematic and dynamic models to be more functional, stable, and efficient than common alternatives. Simulations run 1K-10K faster than real time on an ordinary PC, even while predicting articulated motion on rough terrain and enforcing realistic wheel-terrain interaction models. In addition, we present a novel Integrated Prediction Error Minimization (IPEM) method to calibrate model parameters that is general, convenient, online, and evaluative. Ordinarily system dynamics are calibrated by minimizing the error of instantaneous output predictions. IPEM instead forms predictions by integrating the system dynamics over an interval; benefits include reduced sensing requirements, better observability, and accuracy over a longer horizon. In addition to calibrating out systematic errors, we simultaneously calibrate a model of stochastic error propagation to quantify the uncertainty of motion predictions. Experimental results on multiple platforms and terrain types show that parameter estimates converge quickly during online calibration, and uncertainty is well characterized. Under normal conditions, our enhanced kinematic model can predict nonzero wheel slip as accurately as a full dynamic model for a fraction of the computation cost. Finally, odometry is greatly improved when using IPEM vs. manual calibration, and when using 3D vs. 2D kinematics. To facilitate their use, we have released open source MATLAB and C++ libraries implementing the model formulation and calibration methods in this thesis.

A typical wastewater effluent of a chemical process can contain several strong acids/bases, weak acids/bases as well as their salts. They must be neutralized before being discharged to the environment in order to protect aquatic life and human welfare. However, neutralization process is highly non&ndash / linear and has time&ndash / varying characteristics. Therefore, the control of pH is a challenging problem where advanced control strategies are often considered. In this study, the aim is to design a pH control system that will be capable of controlling the pH-value of a plant waste-water effluent stream having unknown acids with unknown concentrations using an on&ndash / line identification procedure. A Model Predictive Controller, MPC, and a Fuzzy Logic Controller, FLC, are designed and used in a laboratory scale pH neutralization system. The characteristic of the upstream flow is obtained by a small identification reactor which has ten times faster dynamics and which is working parallel to actual neutralization tank. In the control strategy, steady&ndash / state titration curve of the process stream is obtained using the data collected in terms of pH value from the response of the identification reactor to a pulse input in base flow rate and using the simulated response of the identification reactor for the same input. After obtaining the steady&ndash / state titration curve, it is used in the design of a Proportional&ndash / Integral, PI, and of an Adaptive Model Predictive Controller, AMPC. On the other hand, identification reactor is not used in the FLC scheme. The performances of the designed controllers are tested mainly for disturbance rejection, set&ndash / point tracking and robustness issues theoretically and experimentally. The superiority of the FLC is verified.

This dissertation investigates a new design for a magnetostrictive actuator that employs individually controlled coils distributed axially along the magnetostrictive rod. As a quantitative goal, the objective is to show that the multi-coil actuator can operate effectively at frequencies as high as 10,000 Hz with 900 N force and 50 microns of displacement. Conventional, single coil actuators with the same parameters for force and displacement develop significant attenuation in their response at frequencies above the first longitudinal vibration resonance at about 2750 Hz. The goal of the research is to investigate whether multiple coils can effectively increase the frequency range a least four times the range of conventional magnetostrictive actuators. This document derives a new mathematical model of the actuator that represents the spatial distributions of magnetic field and vibration, devises a control design that takes advantage of the multiple inputs to control the displacement of the actuator while consuming minimum electrical power, and describes a prototype multi-coil actuator and experimental system developed to test the idea. The simulations of the multi-coil actuator and control design demonstrate successful transient operation of the actuator over the targeted frequency range with feasible levels of input power and current. Experimental tests of the design, although limited by a computer sampling rate less than 10,000 Hz, are able to validate the predictions of the developed model of the actuator and reproduce the simulated control performance within the constraints of the experimental system.

This thesis investigates the questions of where to grasp and how to grasp a given object with an articulated robotic grasping device. To this end, aspects of grasp synthesis and hand motion planning and control are investigated. Grasp synthesis is the process of determining a palm pose with respect to the target object, as well as a hand joint configuration and/or grasp contact points such that a successful grasp execution is allowed. Existing methods tackling the grasp synthesis problem can be categorized in analytical and empirical approaches. Analytical approaches are based on geometric, kinematic and/or dynamic formulations, whereas empirical methods aim at mimicking human strategies.An overarching idea throughout this thesis is to circumvent the curse of dimensionality, which is inherent in high-dimensional planning problems, by incorporating empirical data in analytical approaches. To this end, tools from the field of constrained optimization are used (i) to synthesize grasp families based on available prototype grasps, (ii) to incorporate heuristics capturing human grasp strategies in the grasp synthesis process and (iii) to encode demonstrated grasp motions in primitive motion controllers.The first contribution is related to the computation and analysis of grasp families which are represented as Independent Contact Regions (ICR) on a target object’s surface. To this end, the well-known concept of the Grasp Wrench Space for a single grasp is extended to be applicable for a set of grasps. Applications of ICR include grasp qualification by capturing the robustness of a grasp to position inaccuracies and the visual guidance of a demonstrator in a teleoperating scenario. In the second main contribution of this thesis, it is shown how to reduce the grasp solution space during the synthesis process by accounting for human approach strategies. This is achieved by imposing appropriate constraints to a corresponding optimization problem. A third contribution in this dissertation is made to reactive motion planning. Here, primitive controllers are synthesized by estimating the free parameters of corresponding dynamical systems from multiple demonstrated trajectories. The approach is evaluated on an anthropomorphic robot hand/arm platform. Also, an extension to a Model Predictive Control (MPC) scheme is presented which allows to incorporate state constraints for auxiliary tasks such as obstacle avoidance.

In order to meet tight product quality specifications for chemical batch processes, it is vital to monitor and control product quality throughout the batch duration. However, the frequent lack of in situ sensors for continuous monitoring of batch product quality complicates the control problem and calls for novel control approaches. This thesis focuses on the study and application of multivariate statistical methods to control product quality in chemical batch processes. These multivariate statistical methods can be used to identify data-driven prediction models that can be integrated within a model predictive control (MPC) framework. The ideal MPC control strategy achieves end-product quality specifications by performing trajectory tracking during the batch operating time. However, due to the lack of in-situ sensors, measurements of product quality are usually obtained by laboratory assays and are, therefore, inherently intermittent. This thesis proposes a new approach to realise trajectory tracking control of batch product quality in those situations where only intermittent measurements are available. The scope of this methodology consists of: 1) the identification of a partial least squares (PLS) model that works as an estimator of product quality, 2) the transformation of the PLS model into a recursive formulation utilising a moving window technique, and 3) the incorporation of the recursive PLS model as a predictor into a standard MPC framework for tracking the desired trajectory of batch product quality. The structure of the recursive PLS model allows a straightforward incorporation of process constraints in the optimisation process. Additionally, a method to incorporate a nonlinear inner relation within the proposed PLS recursive model is introduced. This nonlinear inner relation is a combination of feedforward artificial neural networks (ANNs) and linear regression. Nonlinear models based on this method can predict product quality of highly nonlinear batch processes and can, therefore, be used within an MPC framework to control such processes. The use of linear regression in addition to ANNs within the PLS model reduces the risk of overfitting and also reduces the computational e↵ort of the optimisation carried out by the controller. The benefits of the proposed modelling and control methods are demonstrated using a number of simulated batch processes.

abstract: There is increasing interest in the medical and behavioral health communities towards developing effective strategies for the treatment of chronic diseases. Among these lie adaptive interventions, which consider adjusting treatment dosages over time based on participant response. Control engineering offers a broad-based solution framework for optimizing the effectiveness of such interventions. In this thesis, an approach is proposed to develop dynamical models and subsequently, hybrid model predictive control schemes for assigning optimal dosages of naltrexone, an opioid antagonist, as treatment for a chronic pain condition known as fibromyalgia. System identification techniques are employed to model the dynamics from the daily diary reports completed by participants of a blind naltrexone intervention trial. These self-reports include assessments of outcomes of interest (e.g., general pain symptoms, sleep quality) and additional external variables (disturbances) that affect these outcomes (e.g., stress, anxiety, and mood). Using prediction-error methods, a multi-input model describing the effect of drug, placebo and other disturbances on outcomes of interest is developed. This discrete time model is approximated by a continuous second order model with zero, which was found to be adequate to capture the dynamics of this intervention. Data from 40 participants in two clinical trials were analyzed and participants were classified as responders and non-responders based on the models obtained from system identification. The dynamical models can be used by a model predictive controller for automated dosage selection of naltrexone using feedback/feedforward control actions in the presence of external disturbances. The clinical requirement for categorical (i.e., discrete-valued) drug dosage levels creates a need for hybrid model predictive control (HMPC). The controller features a multiple degree-of-freedom formulation that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed loop system. The nominal and robust performance of the proposed control scheme is examined via simulation using system identification models from a representative participant in the naltrexone intervention trial. The controller evaluation described in this thesis gives credibility to the promise and applicability of control engineering principles for optimizing adaptive interventions. / Dissertation/Thesis / M.S. Electrical Engineering 2011

Cette thèse aborde les problèmes de la modélisation et de la commande d'un micro-réseau courant continu (CC) en vue de la gestion énergétique optimale, sous contraintes et incertitudes. Le micro-réseau étudie contient des dispositifs de stockage électrique (batteries ou super-capacités), des sources renouvelables (panneaux photovoltaïques) et des charges (un système d'ascenseur motorise par une machine synchrone a aimant permanent réversible). Ces composants, ainsi que le réseau triphasé, sont relies a un bus commun en courant continu, par des convertisseurs dédies. Le problème de gestion énergétique est formule comme un problème de commande optimale qui prend en compte la dynamique du système, des contraintes sur les variables, des prédictions sur les prix, la consommation ou la production et des profils de référence.Le micro-réseau considère est un système complexe, de par l'hétérogénéité de ses composants, sa nature distribuée, la non-linéarité de certaines dynamiques, son caractère multi-physiques (électromécanique, électrochimique, électromagnétique), ainsi que la présence de contraintes et d'incertitudes. La représentation consistante des puissances échangées et des énergies stockées, dissipées ou fournies au sein de ce système est nécessaire pour assurer son opération optimale et fiable.Le problème pose est abordé via l'usage combine de la formulation hamiltonienne a port, de la platitude et de la commande prédictive économique base sur le modelé. Le formalisme hamiltonien a port permet de décrire les conservations de la puissance et de l'énergie au sein du micro-réseau explicitement et de relier les composants hétérogènes dans un même cadre théorique. Les non linéarités sont gérées par l'introduction de la notion de platitude démentielle et la sélection de sorties plates associées au modèle hamiltonien a ports. Les profils de référence sont génères a l'aide d'une para métrisation des sorties plates de telle sorte que l'énergie dissipée soit minimisée et les contraintes physiques satisfaites. Les systèmes hamiltoniens sur graphes sont ensuite introduits pour permettre la formulation et la résolution du problème de commande prédictive _économique a l'échelle de l'ensemble du micro-réseau CC. Les stratégies de commande proposées sont validées par des résultats de simulation pour un système d'ascenseur multi-sources utilisant des données réelles, identifiées sur base de mesures effectuées sur une machine synchrone. / The goals of this thesis is to propose modelling and control solutions for the optimal energy management of a DC microgrid under constraints. The studied microgrid system includes electrical storage units (e.g., batteries, supercapacitors), renewable sources (e.g., solar panels) and loads (e.g., an electro-mechanical elevator system). These interconnected components are linked to a three phase electrical grid through a DC bus and associated DC/AC converters. The optimal energy management is usually formulated as an optimal control problem which takes into account the system dynamics, cost, constraints and reference profiles.An optimal energy management for the microgrid is challenging with respect to classical control theories. Needless to say, a DC microgrid is a complex system due to its heterogeneity, distributed nature (both spatial and in sampling time), nonlinearity of dynamics, multi-physic characteristics, the presence of constraints and uncertainties. Moreover, the power-preserving structure and the energy conservation of a microgrid are essential for ensuring a reliable operation.This challenges are tackled through the combined use of port-Hamiltonian formulations, differential flatness, and economic Model Predictive Control.The Port-Hamiltonian formalism allows to explicitly describe the power-preserving structure and the energy conservation of the microgrid and to connect different components of different physical natures through the same formalism. The strongly non-linear system is then translated into a flat representation. Taking into account differential flatness properties, reference profiles are generated such that the dissipated energy and various physical constraints are taken into account. Lastly, we minimize the purchasing/selling electricity cost within the microgrid using the economic Model Predictive Control with the Port-Hamiltonian formalism on graphs.The proposed control designs are validated through simulation results.

Lors du contrôle de robots, les variations fortes et soudaines dans les couples de commande doivent impérativement être évitées. En effet ces discontinuités peuvent entraîner, en plus des comportements imprévisibles du système, des dommages physiques, notamment au niveau des actionneurs. Pour la réalisation de tâches complexes, un robot à plusieurs degrés de liberté utilise généralement un système de commande multi-objectif avec lequel plusieurs tâches doivent être réalisées et plusieurs contraintes respectées. Le basculement entre ces différentes tâches ainsi que les contraintes causées par un environnemt dynamique et imprévisible sont les causes directes des variations fortes dans les couples de commande. Dans ce travail, les problèmes de transitions de priorités entre les différentes tâches ainsi que la variation des contraintes sont considérées avec pour objectif la des variations fortes dans les couples de commande. Deux contributions principales ont été réalisées.Premièrement, un nouveau contrôleur appelé "contrôle hiérarchique généralisé (GHC)" est implémenté sous forme d’optimisation quadratique pour gérer la priorité des transitions entre les tâches de poids différents. Le projecteur utilisé assure en plus de la continuité des transitions, la gestion de l’ajout et/ou de la suppression de tâches. Les couples de commande sont alors calculés en résolvant un problème d’optimisation prenant en compte en même temps la hiérarchie des tâches et les contraintes égalitaire et inégalitaires.Deuxièmement, nous avons développé une primitive de contrôle à base de Contrôle par Modèle Prédictif (CMP) afin de gérer l’existence des discontinuités des contraintes que doit respecter le robot, tel que le changement d’état des contacts ou l’évitement d'obstacles. Le contrôleur profite ainsi de la formulation prédictive en anticipant l'évolution des contraintes vis-à-vis des scénarios de commande et/ou de l'information des capteurs. Il permet de générer des nouvelles contraintes continues qui remplacent les anciennes contraintes discrètes dans le contrôleur réactif QP. Par conséquent, le taux de changement des couples articulaires est minimisé, comparé aux anciennes contraintes discrètes. Cette primitive de contrôle prédictive ne modifie pas directement les objectifs désirés des tâches mais les contraintes, ce qui permet de s’assurer que les changements de couple sont bien gérés dans les pires scénarios.L'efficacité de la stratégie de contrôle proposée est validée via des expériences en simulation avec le robot Kuka LWR 4+ et le robot humanoïde iCub. Les résultats montrent que l'approche développée peut réduire de manière significative la variation des couples articulaires pendant les changements de priorité des tâches ou sous contraintes discrètes. / Large and sudden changes in the torques of the actuators of a robot are highly undesirable and should be avoided during robot control as they may result in unpredictable behaviours. Multi-objective control system for complex robots usually have to handle multiple prioritized tasks while satisfying constraints. Changes in tasks and/or constraints are inevitable for robots when adapting to the unstructured and dynamic environment, and they may lead to large sudden changes in torques. Within this work, the problem of task priority transitions and changing constraints is primarily considered to reduce large sudden changes in torques. This is achieved through two main contributions as follows. Firstly, based on quadratic programming (QP), a new controller called Generalized Hierarchical Control (GHC) is developed to deal with task priority transitions among arbitrary prioritized task. This projector can be used to achieve continuous task priority transitions, as well as insert or remove tasks among a set of tasks to be performed in an elegant way. The control input (e.g. joint torques) is computed by solving one quadratic programming problem, where generalized projectors are adopted to maintain a task hierarchy while satisfying equality and inequality constraints. Secondly, a predictive control primitive based on Model Predictive Control (MPC) is developed to handle presence of discontinuities in the constraints that the robot must satisfy, such as the breaking of contacts with the environment or the avoidance of an obstacle. The controller takes the advantages of predictive formulations to anticipate the evolutions of the constraints by means of control scenarios and/or sensor information, and thus generate new continuous constraints to replace the original discontinuous constraints in the QP reactive controller. As a result, the rate of change in joint torques is minimized compared with the original discontinuous constraints. This predictive control primitive does not directly modify the desired task objectives, but the constraints to ensure that the worst case of changes of torques is well-managed. The effectiveness of the proposed control framework is validated by a set of experiments in simulation on the Kuka LWR robot and the iCub humanoid robot. The results show that the proposed approach significantly decrease the rate of change in joint torques when task priorities switch or discontinuous constraints occur.