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This paper studies the implementation and validation of control algorithms for an autonomous drag racing vehicle. The previously 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.

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 redesigned, modeled, and then implemented in vehicle. Three key sensors (camera, radar, lidar) were also integrated on to the vehicle to enable the vehicle to know its position relative to the track. All these systems were developed and designed such that the vehicle could be autonomously but seamlessly overridden by an in-vehicle driver.

This paper 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.

This paper presents an empirical dynamic model of a single spring electromagnetic solenoid actuator system, including bounce, temperature effects and coil leakage inductance. The model neglects hysteresis and saturation, the aim being to compensate for these uncertainties through estimator robustness. The model is validated for all regions of operation and there is a good agreement between model and experimental data. A nonlinear (sliding mode) estimator is developed to estimate position and speed from current measurements. Since the estimator makes use of only current measurement it is given the name sensorless. The estimator is validated in simulation and experimentally. The novelty in this paper lies in the fact that accurate state estimation can be realized on a simple linear model using a robust observer theory. Also, the formulations for leakage inductance and coil temperature are unique.

This paper presents the modeling of an Electromagnetic Valve Actuator (EMV). A nonlinear model is formulated and presented that takes into account secondary nonlinearities like hysteresis, saturation, bounce and mutual inductance. The uniqueness of the model is contained in the method used in modeling hysteresis, saturation and mutual inductance. Theoretical and experimental methods for identifying parameters of the model are presented. The nonlinear model is experimentally validated. Simulation and experimental results are presented for an EMV designed and built in our laboratory. The experimental results show that sensorless estimation could be a possible solution for position control.

The objective of this paper is to derive an accurate lumped parameter thermal model for electromagnetic actuators (EMA) that is suitable for control purposes. This model estimates the change in coil resistance due to temperature rise when energized by constant a current pulse. The dynamic equations are developed from basic thermodynamic and heat transfer principles, namely, the first law of thermodynamics, “The Law of conservation of energy” [4]. The dynamic equations are developed from the idealization of a control volume, and formulating the first law of thermodynamics on a rate basis. The resulting differential equation is based on energy generation, energy storage and energy dissipated by the coil. The result is a control-oriented model with an estimate of coil temperature and resistance change with current input. This formulation can easily be implemented without knowing the electric drive circuit and can be used as a modeling and design tool for any solenoidal actuator.

With increasing demands to reduce emissions from internal combustion engines, engine manufacturers are forced to seek out new technology. One such technology employed primarily in the diesel and two-stroke engine community is direct-injection (DI). Direct injection has shown promising results in reduction of CO and NOx for both two- and four-stroke engines. While having been used for several years in the diesel industry, direct injection has been scrutinized for an inability to meet future requirements to reduce particulate matter emissions. Direct injection has also came under fire for complicating fuel delivery systems, thus making it cost prohibitive for small utility engine manufacturers. Recent research shows that the application of piezo-driven actuators has a positive effect on soot formation reduction for diesel engines and as this paper will distinguish, has the ability to simplify direct injection fuel delivery systems in general.

Hybrid Electric Vehicles (HEVs) are projected as one of the solutions to the world's need for cleaner and more fuel-efficient vehicles. The efficacy of a Hybrid Electric Vehicle lies in its control strategy. The diligent use of the two power sources, namely the internal combustion engine (ICE) and the electric motor (EM) determines the fuel consumption, the emissions output, and the charge-sustaining behavior of the vehicle, while still maintaining drivability. In this paper, a tunable control structure is developed that enables one to determine the torque split between the IC engine and the electric motor based on efficiency and emissions. Traffic and elevation information from Global Positioning Systems (GPS) over an entire trip is assumed to be known and is used in an adaptive fuzzy logic controller (FLC). An instantaneous control strategy for a parallel HEV is continuously modified based on future driving conditions.

Currently Hybrid Electric Vehicles (HEV) are being considered as an alternative to conventional automobiles in order to improve efficiency and reduce emissions. A major concern of these vehicles is how to effectively operate the electric machine and the ICE. Towards this end two operation strategies, an best efficiency and a least fuel use strategy, are presented in this paper. To demonstrate the potential of an advanced operation strategy for HEV's, a fuzzy logic controller has been developed and implemented in simulation in the National Renewable Energy Laboratory's simulator Advisor (version 2.0.2). Results have also been gathered from chassis dynamometer tests in order to verify the effectiveness of Advisor. The Fuzzy Logic Controller (FLC) utilizes the electric motor in a parallel hybrid electric vehicle (HEV) to force the ICE (66KW Volkswagen TDI) to operate at or near its peak point of efficiency or at or near its best fuel economy.

This paper discusses the use of intelligent control techniques for the control of a parallel hybrid electric vehicle powertrain. Artificial neural networks and fuzzy logic are used to implement a load leveling strategy. The resulting vehicle control unit, a supervisory controller, coordinates the powertrain components. The presented controller has the ability to adapt to different drivers and driving cycles. This allows a control strategy which includes both fuel-economy and performance modes. The strategy was implemented on the Ohio State University FutureCar.