<p><strong>Abstract</strong></p> <p><strong> </strong>Lithium-ion cells have been the workhorse for customer products including laptop computers, cell phones and battery energy storage systems that can store energy from renewable energy sources. The internal resistance of them is very low, resulting in a low amount of wasted energy. Performance of Li-Ion cells is wonderful if they treated well. For this reason, an efficient battery management system (BMS) is essential in order to maximize the battery's capacity, battery means a collection of cells wired in series providing a higher voltage. So by using a BMS, SOC (State Of Charge) levels of cells get closer to each other resulting increased life and capacity of a battery. There are active and passive battery balancers. The energy which is extracted from those cells with higher SOC using passive balancer is wasted in heat, so it is not so efficient in terms of wasted energy in comparison with active balancing algorithms. There are different types of active balancing techniques. Cell to cell technique, cell to battery and battery to cell. Among the above mentioned, cell to battery performs well. However, when push-pull converters (which have high performance) are used, flux imbalance phenomenon ( which is resulted in a asymmetric hysteresis loop of a magnetic core) is unavoidable. In order to prevent this phenomenon, current mode topology is used. Hence, the transistors won't burn out due to this effect. In this thesis I simulated current mode algorithms with push-pull converters with MATLAB SIMULINK. I also physically designed push-pull converters and built it with help of chips ( current mode controllers, optocouplers, transistors, fast recovery diodes, linear and shunt regulators). I did simulate two active balancing methods ( Cell to cell and cell to battery) and compared the results. The results that came from cell to battery indicated a better performance in terms of balancing speed. For three cells balancing time reduced from 3570 seconds in cell to cell method to 518 seconds in cell to battery method.</p> / Master of Science in Electrical and Computer Engineering (MSECE)

Combining the advantages from electric motor and the pneumatic actuator, the hybrid pneumatic-electric actuator is expected to be safe, low-cost, clean, high power to weight ratio, and to provide precise position control. In this thesis, the modeling and control of an improved hybrid pneumatic-electric actuator prototype is presented. The actuator’s main components consist of a low-friction pneumatic cylinder, two on/off solenoid valves, and a small DC motor. The cylinder and motor are connected to a common output shaft using gears. The shaft rotates a single-link robot arm. Its position is measured by an incremental encoder. The prototype was improved by incorporating faster switching valves, flow controls, a faster valve drive circuit, a high resolution encoder rather than the existing linear potentiometer, more accurate pressure sensors and stronger gears. A system dynamic model without the valve dynamic was developed identified and validated using open-loop experiments. The valve models for a discrete input and PWM input were then developed and validated separately. The use of bipolynomial function and artificial neural network fitting methods for modeling the valve mass flow rates were compared. The combined system model with valve dynamics was validated experimentally. Two model-based nonlinear position controllers, using the backstepping and discrete-valued model predictive control (DVMPC) methods, were designed, simulated and extensively tested. Testing was done with the actuator operating using the cylinder alone, the motor alone and in hybrid mode using the cylinder and motor together. Operating in the hybrid mode reduced the root-mean-square error (RMSE) up to 80%. A stability analysis for the backstepping control including the valve modeling error, friction model error, and electric motor torque modeling error was performed. Compensation terms were designed to improve the performance for the two controllers. Additional stability analyses were performed for backstepping controller with a feedback term and the DVMPC with motor control. A payload estimation algorithm was proposed and shown to enhance the robustness of the DVMPC operating in vertical configuration. Simulations and experiments demonstrated that the model-based controllers performed well for both vertical and horizontal configurations. Regarding robustness to payload mismatch, if the payload was within the load capacity of the hybrid actuator, the model-based controllers were both insensitive to the payload variations in horizontal configuration. The backstepping controller was also robust to the payload variations in the vertical configuration. In experiments, the backstepping control in hybrid actuation mode produced a RMSE of 0.0066 radian for a 2 Hz sine wave desired position trajectory with a 0.3 radian amplitude. With DVMPC, this value decreased to 0.0045 radian. These tracking errors were shown to be 30 to 50% less than those produced by a modified linear position plus velocity plus acceleration controller. / Master of Applied Science (MASc)

<p>A decentralized discrete-event system (DES) consists of supervisors that are physically distributed. Co-observability is one of the necessary and sufficient conditions for the existence of a decentralized supervisors that correctly solve the control problem. In this thesis we present a state-based definition of co-observability and introduce algorithms for its verification. Existing algorithms for the verification of co-observability do not scale well, especially when the system is composed of many components. We show that the implementation of our state-based definition leads to more efficient algorithms.</p> <p>We present a set of algorithms that use an existing structure for the verification of state-based co-observability (SB Co-observability). A computational complexity analysis of the algorithms show that the state-based implementation of algorithms result in quadratic complexity. Further improvements come from using a more compact way of representing finite-state machines namely Binary Decision Diagrams (BDD).</p> / Master of Science (MSc)

Electromechanical systems dynamics analysis is approached through nonlinear differential equations and further creating a state space model for the system. There are three modules analyzed and validated, first module consists two magnet coupled with a mass spring damper system as a band-pass system, Low-pass equivalent system and Low-pass equivalent system through perturbation analysis. Initially Band Pass frameworks for the systems are formulated considering the relation between the mechanical forcing and current. Using Mathematical tools such as Hilbert transforms, Low-Pass equivalent of the systems are derived. The state equations of the systems are then used to design a working model in MATLAB and simulations investigated completely. The scope of the modules discussed for further development of tools various applications.

This thesis discusses the use of a cooperative multiple robot system as applied to distributed tracking and sampling of a boundary edge. Within this system the boundary edge is partitioned into subsegments, each allocated to a particular robot such that workload is balanced across the robots. Also, to minimize the time between sampling local areas of the boundary edge, it is desirable to minimize the difference between each robot’s progression (i.e. phase) along its allocated sub segment of the edge. The paper introduces a new distributed controller that handles both workload and phase balancing. Simulation results are used to illustrate the effectiveness of the controller in an Autonomous Underwater Vehicle (AUV) under ice edge sampling application. Successful results from experimentation with three iRobot(R) Creates are also presented.

We present new adaptive control algorithms that address the problem of rejecting sinusoids with known frequencies that act on an unknown asymptotically stable linear time-invariant system. To achieve asymptotic disturbance rejection, adaptive control algorithms of this dissertation rely on limited or no system model information. These algorithms are developed in discrete time, meaning that the control computations use sampled-data measurements. We demonstrate the effectiveness of algorithms via analysis, numerical simulations, and experimental testings. We also present extensions to these algorithms that address systems with decentralized control architecture and systems subject to disturbances with unknown frequencies.

Renewable energy is one of the most reliable resource that can be used to generate the electricity. It is expected to be the most highly used resource for electricity generation in many countries in the world in the next few decades. Renewable energy resources can be used in several purposes. It can be used for electricity generation, water desalination and mining. Using renewable resources to desalinate the water has several advantages such as reduce the emission, save money and improve the public health. The research described in the thesis focuses on the analysis of using the renewable resources such as solar and wind turbines for desalination plant. The output power from wind turbine is connected through converter and the excess power will be transfer back to the main grid. The photo-voltaic system (PV) is divided into several sections, each section has its own DC-DC converter for maximum power point tracking and a two-level grid connected inverter with different control strategies. The functions of the battery are explored by connecting it to the system in order to prevent possible voltage fluctuations and as a bu er storage in order to eliminate the power mismatch between PV array generation and load demand. Computer models of the system are developed and implemented using the PSCADTM / EMTDCTM software.

The work presented in this thesis is a frequency adaptive tracking filter that can be used in exact tracking of power frequencies and rejection of unwanted harmonics introduced during power disturbances. The power synchronization process includes power converters and other equipment that have many non-linear components that introduce unwanted harmonics. This new design is motivated by the requirement of a filter that can filter all the harmonics and exactly track a rapidly varying fundamental frequency with little time delay and phase error. This thesis analyzes the proposed filter mathematically based on Lyapunov theory and simulations are presented to show the performance of the design in rapid frequency variations.

Abstract POLAR FIELD-ORIENTED CONTROL with 3RD HARMONIC INJECTION Martin Todd Hess Field Oriented Control (FOC), also known as vector control, is a widely used and well documented method for controlling Permanent-Magnet Synchronous Motors (PMSM) and induction motors. Almost invariably the orientation of the stator and rotor (field) fluxes are described in rectangular coordinates. In this thesis we explore the practicality of using polar coordinates. Third harmonic injection is also a well-known technique that allows full utilization of the bus (DC-link), thus allowing the motor to run to full base speed without the use of field weakening. This technique potentially allows a 15.4% improvement in the available bus. It has fallen out of use since it requires direct knowledge of the terminal voltage vector angle. The use of polar FOC permits the use of third-harmonic injection. We believe the combination of FOC and third-harmonic injection to be unique, and we present this paper as a novel contribution to the literature on the subject of motor control.

Faults and failures in system components are the two main reasons for the instability and the degradation in control performance. In recent decades, fault-tolerant control (FTC) approaches were introduced to improve the resiliency of the control system against faults and failures. In general, FTC techniques are classified into two major groups: passive and active. Passive FTC systems do not rely on the fault information to control the system and are closely related to the robust control techniques while an active FTC system performs based on the information received from the fault detection and isolation (FDI) system, and the fault problem will be tackled more intelligently without affecting other parts of the system. This dissertation technically reviews fault and failure causes in control systems and finds solutions to compensate for their effects. Recent achievements in FDI approaches, and active and passive FTC designs are investigated. Thorough comparisons of several different aspects are conducted to understand the advantages and disadvantages of different FTC techniques to motivate researchers to further developing FTC, and FDI approaches. Then, a novel active FTC system framework based on online FDI is presented which has significant advantages in comparison with other state of the art FTC strategies. To design the proposed active FTC, a new FDI approach is introduced which uses the artificial neural network (ANN) and a model based observer to detect and isolate faults and failures in sensors and actuators. In addition, the extended Kalman filter (EKF) is introduced to tune ANN weights and improve the ANN performance. Then, the FDI signal combined with a nonlinear dynamic inversion (NDI) technique is used to compensate for the faults in the actuators and sensors of a nonlinear system. The proposed scheme detects and accommodates faults in the actuators and sensors of the system in real-time without the need of controller reconfiguration. The proposed active FTC approach is used to design a control system for three different applications: Unmanned aerial vehicle (UAV), load frequency control system, and proton exchange membrane fuel cell (PEMFC) system. The performance of the designed controllers are investigated through numerical simulations by comparison with conventional control approaches, and their advantages are demonstrated.