Instrumented umanned aerial vehicles represent a new way of measuring turbulence in the atmospheric boundary layer. However, autonomous measurements require control methods with disturbance-rejection and altitude command-following capabilities. Filtered dynamic inversion is a control method with desirable disturbance-rejection and command-following properties, and this controller requires limited model information. We implement filtered dynamic inversion as the pitch controller in an altitude-hold autopilot. We design and numerically simulate the continuous-time and discrete-time filtered-dynamic-inversion controllers with anti-windup on a nonlinear aircraft model. Finally, we present results from a flight experiment comparing the filtered-dynamic-inversion controller to a classical proportional-integral controller. The experimental results show that the filtered-dynamic-inversion controller performs better than a proportional-integral controller at certain values of the parameter.

In preparation for the influx of renewable energy sources that will be added to the electrical system, flexible and adaptable control schemes are necessary to accommodate the changing infrastructure. Microgrids have been gaining much attention as the main solution to the challenges of distributed and intermittent generation, but due to their low inertia, they need fast-acting control systems in order to maintain stability. Multi-Agent Systems have been proposed as dynamic control and communication frameworks. Decentralized arrangements of agents can provide resiliency and the much-desired “plug and play” behavior. This thesis describes a control system that implements droop control and the diffusion communication scheme without the need of a centralized controller to coordinate the Microgrid agents to maintain the frequency and stable operating conditions of the system. Moreover, the inter-agent communication is unaffected by changing network configurations and can achieve optimal economic dispatch through distributed optimization.

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.

In recent years, the security of networked control systems (NCSs) has been an important challenge for many researchers. Although the security schemes for networked control systems have advanced in the past several years, there have been many acknowledged cyber attacks. As a result, this dissertation proposes the use of a novel time-delay switch (TDS) attack by introducing time delays into the dynamics of NCSs. Such an attack has devastating effects on NCSs if prevention techniques and countermeasures are not considered in the design of these systems. To overcome the stability issue caused by TDS attacks, this dissertation proposes a new detector to track TDS attacks in real time. This method relies on an estimator that will estimate and track time delays introduced by a hacker. Once a detector obtains the maximum tolerable time delay of a plant’s optimal controller (for which the plant remains secure and stable), it issues an alarm signal and directs the system to its alarm state. In the alarm state, the plant operates under the control of an emergency controller that can be local or networked to the plant and remains in this stable mode until the networked control system state is restored. In another effort, this dissertation evaluates different control methods to find out which one is more stable when under a TDS attack than others. Also, a novel, simple and effective controller is proposed to thwart TDS attacks on the sensing loop (SL). The modified controller controls the system under a TDS attack. Also, the time-delay estimator will track time delays introduced by a hacker using a modified model reference-based control with an indirect supervisor and a modified least mean square (LMS) minimization technique. Furthermore, here, the demonstration proves that the cryptographic solutions are ineffective in the recovery from TDS attacks. A cryptography-free TDS recovery (CF-TDSR) communication protocol enhancement is introduced to leverage the adaptive channel redundancy techniques, along with a novel state estimator to detect and assist in the recovery of the destabilizing effects of TDS attacks. The conclusion shows how the CF-TDSR ensures the control stability of linear time invariant systems.

This thesis presents a decentralized electromagnetic formation flying (EMFF) control method using frequency-multiplexed sinusoidal control signals. We demonstrate the EMFF control approach in open-loop and closed-loop control experiments using a single-degree-of-freedom testbed with an electromagnetic actuation system (EAS). The EAS sense the relative position and velocity between satellites and implement a frequency-multiplexed sinusoidal control signal. We use a laser-rangefinder device to capture the relative position and an ARM-based microcontroller to implement the closed-loop control algorithm. We custom-design and build the EAS that implements the formation control in one dimension. The experimental results in this thesis demonstrate the feasibility of the decentralized formation control algorithm between two satellites.

This thesis presents the design for a novel haptic interface for large-format touchscreens. Techniques such as electrovibration, ultrasonic vibration, and external braked devices have been developed by other researchers to deliver haptic feedback to touchscreen users. However, these methods do not address the need for spatial constraints that only restrict user motion in the direction of the constraint. This technology gap contributes to the lack of haptic technology available for touchscreen-based upper-limb rehabilitation, despite the prevalent use of haptics in other forms of robotic rehabilitation. The goal of this thesis is to display kinesthetic haptic constraints to the touchscreen user in the form of boundaries and paths, which assist or challenge the user in interacting with the touchscreen. The presented prototype accomplishes this by steering a single wheel in contact with the display while remaining driven by the user. It employs a novel embedded force sensor, which it uses to measure the interaction force between the user and the touchscreen. The haptic response of the device is controlled using this force data to characterize user intent. The prototype can operate in a simulated free mode as well as simulate rigid and compliant obstacles and path constraints. A data architecture has been created to allow the prototype to be used as a peripheral add-on device which reacts to haptic environments created and modified on the touchscreen. The long-term goal of this work is to create a haptic system that enables a touchscreen-based rehabilitation platform for people with upper limb impairments.

Current exercise machines create resistance to motion and dissipate energy as heat. Some companies create ways to harness this energy, but not cost-effectively. The Energy Harvesting from Exercise Machines (EHFEM) project reduces the cost of harnessing the renewable energy. The system architecture includes the elliptical exercise machines outputting power to DC-DC converters, which then connects to the microinverters. All microinverter outputs tie together and then connect to the grid. The control system, placed around the DC-DC converters, quickly detects changes in current, and limits the current to prevent the DC-DC converters and microinverters from entering failure states. An artificial neural network learns to mitigate incohesive microinverter and DC-DC converter actions. The DC-DC converter outputs 36 V DC operating within its specifications, but the microinverter drops input resistance looking for the sharp decrease in power that a solar panel exhibits. Since the DC-DC converter behaves according to Ohm’s Law, the inverter sees no decrease in power until the voltage drops below the microinverter’s minimum input voltage. Once the microinverter turns off, the converter regulates as intended and turns the microinverter back on only to repeat this detrimental cycle. Training the neural network with the back propagation algorithm outputs a value corresponding to the feedback voltage, which increases or decreases the voltage applied from the resistive feedback in the DC-DC converter. In order for the system to react well to changes on the order of tens of microseconds, it must read ADC values and compute the output neuron value quicker than previous control attempts. Measured voltages and currents entering and leaving the DC-DC converter constitute the neural network’s input neurons. Current and voltage sensing circuit designs include low-pass filtering to reduce software noise filtering in the interest of speed. The complete solution slightly reduces the efficiency of the system under a constant load due to additional component power dissipation, while actually increasing it under the expected varying loads.

This thesis presents the design, layout, and measurements of a bi-directional amplifier with variable vector (in-phase / quadrature) gain control that will be part of an electronically steered phased array system. The electronically steered phased array has many advantages over the conventional mechanically steered antennas including rapid scanning of the beam and adaptively creating nulls in desired locations. The 10-bit bi-directional Vector Variable Gain Amplifier (VVGA) is part of the transmit and receive module of each antenna element where transmit and receive functionality is determined through a simple switch. The VVGA performs amplification of the IF IQ pair by an adjustable complex coefficient. At receive, the VVGA functions as a Vector Variable Gain Current Amplifier (VVGCA) and at transmit, the VVGA functions as a Vector Variable Gain Transadmittance Amplifier (VVGTA). Design procedure, layout entry, schematic and parasitic extracted simulation results, and measurements are presented in this thesis.

BEVs are a critical pathway towards achieving energy independence and meeting greenhouse and pollutant gas reduction goals in the current and future transportation sector [1]. Automotive manufacturers are increasingly investing in the refinement of electric vehicles as they are becoming an increasingly popular response to the global need for reduced transportation emissions. Therefore, there is a desire to extract the most fuel economy from a vehicle as possible. Some areas that manufacturers spend much effort on include minimizing the vehicle’s mass, body drag coefficient, and drag within the powertrain. When these values are defined or unchangeable, interest is driven to other areas such as investigating the control strategy of the powertrain. If two or more electric motors are present in an electric vehicle, Torque Vectoring (TV) strategies are an option to further increase the fuel economy of electric vehicles. Most of the torque vectoring strategies in literature focus exclusively on enhancing the vehicle stability and dynamics with few approaches that consider efficiency or energy consumption. The limited research on TV that addresses system efficiency have been done on a small number of vehicle architectures, such as four independent motors, and are distributing torque front/rear instead of left/right which would not induce any yaw moment. The proposed research aims to address these deficiencies in the current literature. First, by implementing an efficiency-optimized TV strategy for a rear-wheel drive, dual-motor vehicle under straight-line driving as would be experienced in during the EPA drive cycle tests. Second, by characterizing the yaw moment and implementing strategies to mitigate any undesired yaw motion. The application of the proposed research directly impacts dual-motor architectures in a way that improves overall efficiency which also drives an increase in fuel economy. Increased fuel economy increases the range of electric vehicles and reduces the energy demand from an electrical source that may be of non-renewable origin such as coal.

This thesis report investigates the effects of low Reynolds number on the power performance of a 3.74 m diameter horizontal axis wind turbine. The small wind turbine was field tested at the Cal Poly Wind Power Research Center to acquire its coefficient of performance, C­p, vs. tip speed ratio, λ, characteristics. A description of both the wind turbine and test setup are provided. Data filtration and processing techniques were developed to ensure a valid method to analyze and characterize wind power measurements taken in a highly variable environment. The test results demonstrated a significant drop in the wind turbine’s power performance as Reynolds number decreased. From Re = 2.76E5 to Re = 1.14E5, the rotor’s Cp_max changed from 0.30 to 0.19. The Cp vs. λ results also displayed a clear change in shape with decreasing Reynolds number. The analysis highlights the influence of the rotor’s Cl /Cd characteristics on the Cp vs. λ curve’s Reynolds number dependency. By not accounting for the effects of varying Reynolds number below the critical value for a rotor operating at constant λ, the design of the rotor planform may overestimate the actual performance of the turbine in real-world conditions. This problem is more evident in distributed-scale wind turbines, compared to utility-scale ones, because of the significantly shorter chord lengths, and therefore increased wind speed range where this effect occurs. Lastly, the wind turbine’s future control method and annual energy production are evaluated using the test results.