This dissertation studies the simulation and control of an autonomous dragster. Four scenarios are provided that are critical to vehicle and driver safety in drag racing. Equations are then created to model the behavior during these safety scenarios. The use of a kinematic bicycle model and a Newtonian wheel stand model are discussed for plane-of-motion and out-of-plane vehicle movement, respectively. A separate controller is designed for each model by comparing different control methods. Proportional-Integral-Derivative (PID) control, optimal control, and model predictive control (MPC) are presented and applied to the models. The models are simulated from a speed of 75 m/s, being the estimated top speed of the research vehicle, up to a top speed of 150.5 m/s which is in alignment with the highest recorded speed of a dragster. The comparison of the control techniques yields MPC as superior for the bicycle model and PID as sufficient for the wheel stand model. Latency of the system is also discussed and accounted for.

The developed modeling equations are first implemented with control in a realistic simulation environment complete with synthetic sensor data and decision-making algorithms. The controller is then transformed into an embedded on-board processing unit for on-vehicle testing. Camera, lidar, and radar sensor data are investigated and algorithms are created to provide information from physical sensors rather than synthetic data. The control related to actuation of the steering, brake, throttle, and shifting systems are further discussed, along with human-vehicle interaction in terms of handoff and emergency takeovers. The control algorithms are then validated on the research vehicle. This is demonstrated by completing a fully autonomous quarter-mile drag race, complete with camera detection for the staging sequence and MPC trajectory following. Two additional safety scenarios are presented and controlled: starting the race off center and fishtailing.

In today’s automotive industry, much of the focus has shifted to advanced vehicle propulsion systems. This is due to many factors, the main ones being climate change and the diminishing supply of gasoline. This thesis addresses some of the control objectives involved when coupling two power supplies as hybrid vehicles do. The main focus of the research is the use of Model Predictive Control to achieve both thermally efficient and fuel efficient algorithms to control the internal combustion engine in a hybrid powertrain. This is done with the use of two operational modes: Fuel Use Mode which limits the fuel consumption of the engine, and Efficiency Mode which maximizes the thermal efficiency of the engine. A mathematical overview of MPC is described to bring the reader into context. Next, this type of control is applied to a series-parallel hybrid electric vehicle. The formulation of the vehicle model in Simulink is discussed in detail. Then, a practical form of MPC is implemented, by using a torque vector to estimate the outputs of engine model, and minimizing the resulting cost function. Next, MPC parameter selection is discussed, which covers the choices for receding horizon length, cost function weights, and uncertainty correction parameters. After all the parameters have been chosen, simulations are run for the US06 and FUDS drive cycles, and the results are analyzed. The results show that Fuel Use Mode yields a higher miles-per-gallon (MPG) rating, and Efficiency Mode helps maintain the charge level of the batteries.

Soft Robotic Actuators

By

Juleon Taylor Godfrey

Master of Science in Mechanical and Aerospace Engineering

University of California, Irvine, 2017

Dean Gregory N. Washington Irvine, Chair

In this thesis a survey on soft robotic actuators is conducted. The actuators are classified into three main categories: Pneumatic Artificial Muscles (PAM), Electronic Electroactive Polymers (Electric EAP), and Ionic Electroactive Polymers (Ionic EAP). Soft robots can have many degrees and are more compliant than hard robots. This makes them suitable for applications that are difficult for hard robots. For each actuator background history, build materials, how they operate, and modeling are presented. Multiple actuators in each class are reviewed highlighting both their use and their mathematical formulation. In addition to the survey the McKibben actuator was chosen for fabrication and in-depth experimental analysis. Four McKibben actuators were fabricated using mesh sleeve, barbed hose fittings, and different elastic bladders. All were actuated using compressed air. Tensile tests were performed for each actuator to measure the tension force as air pressure increased from 20 to 100 psi in 10 psi increments. To account for material relaxation properties eleven trials for each actuator were run for 2-3 days. In conclusion, the smallest outer diameter elastic bladder was capable of producing the highest force due to the larger gap between the bladder and the sleeve.

Starting with the chassis of a 1969 Volkswagen Beetle, the goal of this project was to design and build a Proton Exchange Membrane Fuel Cell (PEMFC) hybrid vehicle that is powered by hydrogen. This thesis highlights the design process, modeling, and control of a Proton Exchange Membrane Fuel Cell hybrid drive train. Finally, the vehicle was built with many of the components assembled, fabricated, and functioning.

In developing wearable controllers, or simply wearables, for gesture based applications, in addition to simply being accurate devices must further be intuitive, body conforming, and unrestricting of motion. The premise behind wearables is that some physical interaction is required to interface with every day electronic devices be it, for example, a keyboard, mouse, remote control, knob, or button. In the sense that humans have a tendency to gesticulate when verbally communicating with other humans, wearables allow humans to communicate with, or rather control, electronic devices through intuitive, programmable, gesticulations. Being presented is the design, development, and quantification of two wearable gesture based controllers.

The first controller, worn on the forearm, uses the thin film piezoelectric polymer Polyvinylidene Fluoride (PVDF) to detect hand motion based muscle contractions. The PVDF was spatially shaded to enhance its sensitivity to the forces being exerted on it. A time delay artificial neural network algorithm was used to process the PVDF signal and identify the hand motion that generated it. Two distinct sets of hand gestures were studied: fist, extension, and flexion and right and left. Using microcontrollers and Bluetooth, applications demonstrating the capabilities of the controller were developed for the right and left gesture set including an Android oscilloscope style application showing PVDF voltage and identified gesture and a Microsoft PowerPoint Add-In that allows for gestures to navigate the presentation.

The second controller uses a bimetallic thin foil strain gauge bonded to the elastomer PDMS mounted on a golf glove above the MPC joint of the right index finger. The dynamic behavior of the sensor was studied and a relation between sensor measurements and MPC joint angle developed. A custom printed circuit board (PCB) featuring a microcontroller and RF communication was developed enabling the device, in response to joint angle, to control the throttle input to a quadcopter.

The fabrication and use of PVDF, in conjunction with its electrostrictive terpolymer P(VDF-TrFE-CTFE), in a self-sensing bimorph beam is also discussed. While bending in the beam is induced by applying voltage across the terpolymer, measured voltage from the PVDF is shown to give real time tip deflection.

In hopes of lessening the reliance on fossil fuels, Plug-in Hybrid Electric Vehicles (PHEVs) have become an attractive option as an alternative fuel vehicle due to their larger electric motors and energy storage systems (ESS). PHEVs can propel themself relying solely on their internal combustion engine (ICE), electric motor (EM), and or a hybrid of both. To improve their fuel efficiency, many studies have been done to investigate the use of a priori route information to optimize the use of a PHEV’s ICE and EM. This study introduces a real-time machine learning application of a control strategy known as Trajectory Forecasting (TF). TF takes a priori knowledge of a PHEV’s pre-planned route to determine when the vehicle will use its different forms of propulsion in the form of propulsion mode scheduling. However, it assumes constant route data such as traffic and resulting driving speed for its scheduling to be applicable. To automatically account for changing traffic as well as choose better alternative routes, this study looks at the use of a Convolutional Neural Network (CNN) to simulate a PHEV’s operation along available routes beforehand according to the rules of TF to choose a route that best satisfies a driver’s want, better fuel efficiency and possibly lower emissions. This new real-time TF-based machine learning control strategy is evaluated and compared to common PHEV control strategies such as Charge Sustaining (CS) and Charge Depletion (CD) using National Renewable Energy Laboratory’s vehicle simulator ADVISOR. Results show possible increases in Mpgge from 1.72%-130%, decreases in emitted hydrocarbons (HC), carbon monoxide (CO), and nitrous oxides (NOx) from 0.05%-70%, and 0.05%-90.35% reduction in gasoline consumption depending on overall route length and PHEV configuration.

In hopes of lessening the reliance on fossil fuels, Plug-in Hybrid Electric Vehicles (PHEVs) have become an attractive option as an alternative fuel vehicle due to their larger electric motors and energy storage systems (ESS). To improve their fuel efficiency, many studies have been done to investigate the use of a priori route information to optimize the use of a PHEV’s ICE and ESS. This study introduces a new control strategy that uses a priori knowledge of a PHEV’s pre-planned route to develop a battery charge usage plan that determines when the vehicle will use its different forms of propulsion. The PHEV can propel itself relying solely on its internal combustion engine (ICE), electric motor (EM), and or a hybrid of both. The strategy uses a route’s speed limits and states of traffic to estimate the consumption of charge and resulting decrease in SOC, and determine the optimal method of propulsion for the PHEV along its route. Fuzzy logic is then used to ensure that battery use during the times of hybrid propulsion is optimized. The control strategy is evaluated and compared to common PHEV control strategies such as Charge Sustaining (CS) and Charge Depletion (CD) using National Renewable Energy Laboratory’s vehicle simulator ADVISOR, with results showing possible increases fuel efficiency starting at about 1%-10% over long traffic heavy routes within this study.

This work outlines the design, modeling and development of a competition eliminator dragster which is a specialized vehicle built to compete in the National Hot Rod Association (NHRA). A simple control strategy was also developed once the vehicle model was validated. Towards that end, redundant braking, steering, throttle and shifting systems were all designed, modeled, and then implemented in vehicle. Three sensor systems (camera, radar, lidar) were also integrated into the vehicle to enable the vehicle to know its position relative to the track, and its external environment. All these systems were developed and designed such that the vehicle could be autonomous but seamlessly overridden by an in-vehicle driver. The performance of these systems was then validated on the research vehicle. This was demonstrated by completing a fully autonomous quarter mile drag race, complete with camera detection for the staging sequence and trajectory following. Two additional safety scenarios are presented and controlled: starting the race off center and fishtailing.

As fossil fuels continue to deplete, and emission standards become more strict, plug-in electric vehicles (PEVs) become more attractive. However, higher PEV penetration increases power demand on the grid, and immediate charging can induce large peak demands. A control algorithm, Continuously Varying Valley Filling (CVVF), is presented to enhance PEV charging at the local power level while minimizing the effects of uncontrolled PEV charging. Two decision mechanisms within CVVF are explored, crisp and fuzzy logic, to implement a centralized real-time valley-filling strategy that determines the preferred charging rate for vehicles to minimize distribution transformers damage. The algorithm assessed in this analysis reduces the highest peak and the average load during charging of 75 kW transformers by 15.80% and 13.75%, respectively

Limited Fossil fuels and regulations to improve air quality are creating unprecedented changes in transportation, influencing customers to purchase zero-emission vehicles (ZEVs) instead of traditional internal combustion engine vehicles. California's Governor Brown issued Executive Order B-16-2012 to lower the greenhouse gas emissions due to transportation and promote ZEVs in California, setting a goal of reaching 1.5 million ZEVs in California by 2025. ZEVs are classified as plug-in electric vehicles (PEVs), battery electric vehicles (BEVs), and fuel cell electric vehicles (FCEVs). However, PEVs and BEV's draw power from the grid, and as these vehicles become more prevalent, multiple studies have demonstrated that they will increase the electric load and overwhelm the grid.

In this study, three new protocols, Machine Learning Valley-Filling (MLVF), Selected Rate Valley-Filling (SRVF), and Rate and Interval Valley-Filling (RIVF), are presented to enhance PEV charging at the local power level while minimizing the effects of uncontrolled plug-in vehicle (PEV) charging. The goal is to create algorithms that are computationally fast, operate in real-time, scalable, satisfy customer needs, reduce transformer loss of life, and contribute to smart transportation.

SRVF and RIVF are proposed to investigate how variable rate charging can affect PEV charging profiles. SRVF evaluates many charging options and determines the best charging rate for a vehicle, while RIVF utilizes many rates to create a charging profile for a vehicle. Both algorithms can charge vehicles in sections by selecting intervals to charge in. SRVF is a simpler algorithm than RIVF, and an evaluation will be conducted to determine if RIVF’s additional advantages are necessary by quantitively showing the difference in the results. Machine Learning Valley-Filling (MLVF), a neural network and is trained on processed data that determine the best interval to begin charging a vehicle at a constant rate, given its charge needs and dwell time. MLVF is used to investigate if machine learning can be used in valley-filling applications, as it has not yet been demonstrated. The purpose of this study is to investigate if a neural network algorithm can learn to identify when to begin charging a PEV by distinguishing low and high demand sections in the forecasted baseload.

The results demonstrate that SRVF and RIVF have the ability to reduce the absolute maximum peak power reached amongst all transformers, average maximum peak power reached peak power by each transformer, and average load during charging caused by uncontrolled charging up to 51.72%, 25.14%, and 88.51%, respectively. In addition, this study indicates that a neural network algorithm can indeed identify low and high demand sections in the forecasted baseload and learn when to begin charging electric vehicles to decrease demand loads. MLVF achieves a Micro-Averaged F1-Score, a classifier's overall accuracy, of 92.84% of selecting the correct timeslot to initiate charging.