Adaptive control of a nonlinear time varying (NTV) plant, such as a robotic manipulator, is intended to tolerate the unmodeled disturbances and the uncertain parameters of the dynamic model. Most of the previous research has been focused on NTV plants with bounded and "slowly-varying" plant terms. Almost all adaptive controllers require persistent excitations to guarantee stable tracking in the presence of unmodeled disturbances. The new adaptive controllers developed in this work provide stable and robust performance without persistent excitations and the "slowly-varying" assumption. Moreover, the uncertainties of a NTV plant model are not required to be bounded. This allows one to treat some potentially unbounded dynamics as disturbances. Stability and robustness analysis of adaptive controllers under the relaxed conditions is an essential part of this study. A major problem arising in robotic control is parameter uncertainty. The linear parameterization approach is also implemented in this work to deal with the parameter uncertainty. An innovative algorithm for determining the manipulator "regressor" (a coefficient matrix in parameter-linearized form of robot dynamics) is developed. Based on this algorithm a robust self-tuning controller is designed. The control law is proved to be robust with respect to parameter errors and disturbances. The robustness of the controller relaxes the requirement for the parameter estimator, and leads to a stable system without persistent excitations. / Graduate

Thesis submitted in fulfillment of the requirements for the degree Master of Technology: Electrical Engineering in the Faculty of Engineering at the Cape Peninsula University of Technology 2014 / A new control strategy for control of the nonlinear process of Dissolved Oxygen (DO) concentration in the aerobic tank of wastewater treatment process is proposed. It provides means to improve the performance of the Linear Proportional Integration and Derivative (LPID) controller by extending it to a Nonlinear Proportional Integration and Derivative (NLPID) controller. The aim of the thesis is to develop methods, algorithms and software for design, simulation, and programmable logic controller (PLC) implementation of NLPID controllers in order to control the nonlinear process of dissolved oxygen. The thesis investigates the possibilities the widely used in theory and industry methods for the design of the LPID controllers for linear processes as Ziegler- Nichols and Pole Placement, to be applied to the design of NLPID controllers for the nonlinear process of DO concentration. Three cases are considered: Case 1: Application of the values of the parameters the linear PID controller designed by the Ziegler-Niched method for the linearized DO process model to be used as parameters of the nonlinear PID controllers to control the DO nonlinear process. Case 2: Application of the values of the parameters of the linear PID controller designed by the Pole placement method for the linearized DO process model, to be used as parameters of the nonlinear PID controller to control the nonlinear DO process. Case 3: Novel, proposed in the thesis, method based on the Pole placement method for direct design of the parameters of the linear and nonlinear PID controllers to control the nonlinear DO process. Software is developed to simulate in MATLAB environment the behavior of the closed loop DO process for the considered cases of controller designs. The results of the simulations show that in the Case1 and the Case 2 it is not possible to use the values of the LPID controller parameters designed for the linearized DO process, directly to control the nonlinear process by the NPID controllers. Additional tuning for some of the parameters is needed. The simulation in the Case 3 shows the excellent behaviors of the closed loop system for all linear and nonlinear PID controllers which prove that the new method is effective and applicable. Real-time simulations of the closed loop system are done in a TwinCAT 3 simulation environment of the Bechkoff EX5020 PLC. The deliverables of the thesis are applicable to many type nonlinear processes in chemical, manufacturing, and other industries.

This thesis explores the possibility of controller restructuring for improved closed-loop performance of nonlinear plants using a gradient based method of symbolic adaptation- Model Structure Adaptation Method (MSAM). The adaptation method starts with a controller which is a linear controller designed according to the linearized model of the nonlinear plant. This controller is then restructured into a series of nonlinear candidate controllers and adapted iteratively toward a desired closed-loop response. The noted feature of the adaptation method is its ability to quantify structural perturbations to the controllers. This quantification is important in scaling the structural Jacobian that is used in gradient-based adaptation of the candidate controllers. To investigate this, two nonlinear plants with unknown nonlinearities viz., nonlinear valve and nonlinear inverted pendulum are chosen. Furthermore, the properties of restructured controllers obtained for two systems, stability, effect of measurement noise, reachability, scalability and algorithmic issues of MSAM are studied and compared with the starting controller.

archives@tulane.edu / This dissertation is a detailed scientific study concerning a proton exchange membrane fuel cell, which is coupled to a DC-to-DC converter as the power processor, serving as a power source. The novel aspect of the dissertation is the use of a new controller or nonlinear observer to predict parameter estimation of the fuel cell and the DC-to-DC converter as the load potential changes for the automated control system. Nonlinear control algorithms, which include nonlinear observers, were developed for such systems. / 1 / Sean

Abstrakt: This report examines different control design methods, linear as well as nonlinear, for a suborbital reusable launch vehicle. An investigation of the natural vehicle behavior is made, after which a baseline linear controller is constructed to fulfill certain handling quality criteria. Thereafter the nonlinear cascade control methods block backstepping and nonlinear dynamic inversion via time scale separation are examined and used to construct two nonlinear controllers for the vehicle. Optimal controllers, in terms of three different criteria, are found using the genetic optimization algorithm differential evolution. The optimal controllers are compared, and it is found that nonlinear dynamic inversion via time scale separation performs better than block backstepping with respect to the cases investigated. The results suggest control design by global optimization is a viable and promising complement to classical methods.

The development of high-energy-density batteries, advanced sensor technologies, and advanced control algorithms for multirotor electric vertical takeoff and landing (eVTOL) unmanned aerial vehicles (UAVs) has led to interest in using these vehicles for a variety of applications including surveillance, package delivery, and even human transportation. In each of these cases, the ideal vehicle is one that can maneuver in congested spaces, but is efficient for traveling long distances. The combination of wings and vectored thrust make winged eVTOLs the obvious choice. However, these aircraft experience a much wider range of flight conditions that makes them challenging to model and control. This thesis contributes an aerodynamic model and a planning and control method for small, 1-2 m wingspan, winged eVTOLs. We develop the aerodynamic model based on first-principles, lumped-element aerodynamics, extending the lift and drag models to consider high-angle-of-attack flight conditions using models proposed in the literature. We present two methods for generating spline trajectories, one that uses the singular value decomposition to find a minimum-derivative polynomial spline, and one that uses B-splines to produce trajectories in the convex hull of a set of waypoints. We compare the quality of trajectories produced by both methods. Current control methods for winged eVTOL UAVs consider the vehicle primarily as a fixed-wing aircraft with the addition of vertical thrust used only during takeoff and landing. These methods provide good long-range flight handling but fail to consider the full dynamics of the vehicle for tracking complex trajectories. We present a trajectory tracking controller for the full dynamics of a winged eVTOL UAV in hover, fixed-wing, and partially transitioned flight scenarios. We show that in low- to moderate-speed flight, trajectory tracking can be achieved using a variety of pitch angles. In these conditions, the pitch of the vehicle is a free variable that we use to minimize the necessary thrust, and therefore energy consumption, of the vehicle. We use a geometric attitude controller and an airspeed-dependent control allocation scheme to operate the vehicle at a wide range of airspeeds, flight path angles, and angles of attack. We provide theoretical guarantees for the stability of the proposed control scheme assuming a standard aerodynamic model, and we present simulation results showing an average tracking error of 20 cm, an average computation rate of 800 Hz, and an 85% reduction in tracking error versus using a multirotor controller for low-speed flight.

The study of fixed wing aeronautical engineering has matured to the point where years of research result in small performance improvements.  In the past decade, micro air vehicles, or MAVs, have gained attention of the aerospace and robotics communities.  Many researchers have begun investigating aircraft schemes such as ones which use rotary or flapping wings for propulsion.  While the engineering of rotary wing aircraft has seen significant advancement, the complex physics behind flapping wing aircraft remains to be fully understood.  Some studies suggest flapping wing aircraft can be more efficient when the aircraft operates in low Reynolds regimes or requires hovering.  Because of this inherent complexity, the derivation of flapping wing control methodologies remains an area with many open research problems.  This thesis investigates flapping wing vehicles whose design is inspired by avian flight.  The flapping wing system is examined in the cases where the core body is fixed or free in the ground frame.  When the core body is fixed, the Denavit Hartenberg representation is used for the kinematic variables.  An alternative approach is introduced for a free base body case.  The equations of motion are developed using Lagranges equations and a process is developed to derive the aerodynamic contributions using a virtual work principle.  The aerodynamics are modeled using a quasi-steady state formulation where the lift and drag coefficients are treated as unknowns.  A collection of nonlinear controllers are studied, specifically an ideal dynamic inversion controller and two switching dynamic inversion controllers.  A dynamic inversion controller is modified with an adaptive term that learns the aerodynamic effects on the equation of motion.  The dissipative controller with adaptation is developed to improve performance.  A Lyapunov analysis of the two adaptive controllers guarantees boundedness for all error terms.  Asymptotic stability is guaranteed for the derivative error in the dynamic inversion controller and for both the position and derivative error in the dissipative controller.  The controllers are simulated using two dynamic models based on flapping wing prototypes designed at Virginia Tech.  The numerical experiments validate the Lyapunov analysis and illustrate that unknown parameters can be learned if persistently excited. / Master of Science

La forte proportion des sources d'énergie intermittentes présente de nouveaux défis pour la stabilité et la fiabilité des réseaux électriques. Dans ce travail nous considérons la connexion de ces sources avec et un système de stockage hybride via un MicroGrid à courant continu (DC) afin de satisfaire les contraintes de connexion au réseau (les Grid-Codes). L'objectif principal ici est de concevoir un système pouvant répondre à ces exigences et nous permettant d'obtenir un comportement Plug and Play; cette approche est basée sur la "philosophie System of Systems ". utilisant des méthodologies de contrôle distribué.Cette thèse constitue une contribution au contrôle DC MicroGrid et introduit une analyse rigoureuse de la dynamique du system. La stabilisation du système repose sur des dispositifs de stockage: les batteries pour l'équilibre énergétique et la réponse à long terme des variations des flux d'énergie tandis que les supercondensateurs traitent l'équilibre des puissances et des variations rapide du system.Nous présenterons d’abord l’analyse du MicroGrid DC dont le contrôle est conçu à partir des modèles détaillés des sources d’énergie et des systèmes de stockage. Ce réseau peut présenter un comportement instable créé par intermittence de la source, les commutation des convertisseurs et leurs électroniques puissance et les courants oscillatoires produits par certains types de charges. Par conséquent, le système est sujet à des variations rapides et lentes. . La stabilisation de tels systèmes reposera sur le fonctionnement de différentes technologies de stockage, telles que la batterie et les supercondensateurs, qui opèrent dans différentes échelles de temps.Nous proposons un schéma de contrôle hiérarchique, basé sur la théorie du contrôle non linéaire, en particulier de Lyapunov, le backstraping et d’entrée / sortie de feedback linéarisation. Le DC MicroGrid proposé et son contrôle sont vérifiés à la fois par simulations et par expérimentation Les résultats montrent la bonne performance du système sous des variations de production et de consommation. / The large penetration intermittent energy sources, presents a new challenges to power systems' stability and reliability; we consider in this work their connection through a Direct Current (DC) MicroGrid and a hybrid storage system, in order to satisfy constraints of connection to the network (the so-called Grid-Codes). The main objective here is to design a system that can fulfil these requirements and allow us to attain a Plug and Play behaviour; this approach is based on the “System of Systems philosophy'' using distributed control methodologies.This thesis constitutes a contribution for DC MicroGrid control and introduces a rigorous dynamics' analysis.. The stabilization of the system is based on storage devices: batteries for energy balance and long term response of power flow, while supercapacitors deal with power balance and fast response.First it will be presented the analysis of the DC MicroGrid which control is designed based on detailed models of energy sources and storage systems. This grid may present an unstable behaviour created by the source’s intermittent output power, switching ripples from the power converters and their power electronic and oscillatory currents produced by some types of loads. Therefore the system is subject to both fast and slow variations. The stabilization of such systems will be based on the operation of different technologies of storage, such as battery and supercapacitor, in different time scales.We propose a hierarchical control scheme, based on nonlinear control theory, in particular Lyapunov, backstepping and input/output feedback linearization. The proposed DC MicroGrid and its control are then verified both by computer simulations and by experiments. The results show the good performance of the system under variations on production and on consumption

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23158号 / 工博第4802号 / 新制||工||1751(附属図書館) / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 引原 隆士, 教授 大村 善治, 特定講師 木村 真之 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

The rising levels of autonomy exhibited by complex cyber-physical systems have brought questions related to safety and adaptation to the forefront of the minds of controls and robotics engineers. Often, such autonomous systems are deemed to be safety-critical in the sense that failures during operation could significantly harm the system itself, other autonomous systems, or, in the worst-case, humans interacting with such a system. Complicating the design of control and decision-making algorithms for safety-critical systems is that they must cope with various degrees of uncertainty as they are deployed autonomously in increasingly real-world environments. These challenges motivate the use of learning-based techniques that can adapt to such uncertainties while adhering to safety-critical constraints. The main objective of this dissertation is to present a unified framework for the design of controllers that learn from data online with formal guarantees of safety. Rather than using a controller trained on an a priori dataset collected offline that is then statically deployed on a system, we are interested in using real-time data to continuously update the control policy online and cope with uncertainties that are challenging to characterize until deployment. We approach the problem of designing such learning-based control algorithms for safety-critical systems through the use of certificate functions, such as Control Lyapunov Functions (CLFs) and Control Barrier Functions (CBFs), from nonlinear control theory. To this end, we first discuss how modern data-driven techniques can be integrated into traditional adaptive control frameworks to develop classes of CLFs and CBFs that facilitate the design of both controllers and learning algorithms that guarantee, respectively, stability and safety by construction. Next, we shift from the problem of safe adaptive control to safe reinforcement learning where we demonstrate how similar ideas from adaptive control can be extended to safely learn the value functions of optimal control problems online using data from a single trajectory. Finally, we discuss an extension of the aforementioned approaches to richer control specifications given in the form of temporal logic formulas, which provide a formal way to express complex control objectives beyond that of stability and safety. / 2025-05-30T00:00:00Z