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A traditional method of controlling evaporator superheat in a vapor compression air conditioning system is the thermostatic expansion valve (TXV). Such systems are often used in automotive applications. The TXV depends on superheat to adjust the valve opening. Unfortunately, any amount of superheat causes that evaporator to operate at reduced capacity due to dramatically lower heat transfer coefficients in the superheated region. In addition, oil circulation back to the compressor is impeded. The cold lubricant almost devoid of dissolved refrigerant is quite viscous and clings to the evaporator walls. A system that could control an air conditioner to operate with no superheat would either decrease the size of its existing evaporator while maintaining the same capacity, or potentially increase its capacity with its original evaporator. Also, oil circulation back to the compressor would be improved.

Camless actuation offers programmable flexibility in controlling engine valve events. However, a full range of engine benefits will only be available, if the actuation system can control lift profile characteristics within a particular lift event. Control of the peak value of valve lift is a first step in controlling the profile. The paper presents an adaptive feedback control of valve lift for a springless electrohydraulic valvetrain. The adaptive control maintains peak value of lift in presence of variations in engine speed, hydraulic fluid temperature and manufacturing variability of valve assemblies. The control design includes a reduced-order model of the system dynamics. Experimental results show dynamic behavior under various operating and environmental conditions and demonstrate advantages of adaptive control over the non-adaptive type.

The development of automated guidance for agricultural tractors has addressed several basic and applied issues of agricultural equipment automation. Basic analyses have included the dynamics of steering systems and posture sensors for guidance. Applied issues have evaluated the potential of several commercial sensing systems and a commercial mechanical guidance system. A research platform has been developed based on a Case 7220 Magnum1 2-wheel drive agricultural tractor. An electrohydraulic steering system was used and characterized in support of automated guidance control. Posture sensing methods were developed using GPS, geomagnetic direction sensors (GDS), inertial, and machine vision sensing systems. Sensor fusion of GPS-inertial-machine vision and GPS-GDS-machine vision provided the most flexible and accurate guidance and capable for operation under dynamically changing field conditions.

A dynamic model for the springless electrohydraulic valvetrain has been developed. The model speeds up the valvetrain development process by simulating effects of parameter changes, thus minimizing the number of hardware variations. It includes dynamic characteristics of check valves that enable energy recovery, hydraulic snubbers that limit seating velocity of the engine valves, and leakage in the control solenoids. A good match of the experimental data has been obtained for a single valve system, and the model calibration and validation have been completed. The known parameters are used together with some unknown calibration constants which have been tuned to match the experimental data. The simulation results for a twin valve system are also presented. The model applications for system performance analysis and for the closed-loop control of the engine valve lift are described. The cyclic variability of the experimental data is also discussed.

Machine operators rely on intuition and experience to evaluate vehicle performance. As we increasingly turn to automation, it is important to automatically evaluate sensor data and system performance. Fuzzy logic allows us to take advantage of domain knowledge to evaluate data and to describe a system linguistically. In this paper, two automated fuzzy evaluation systems are described. In the first, a fuzzy quality module evaluates output from a simulated noisy sensor. In the second system, a fuzzy quality module evaluates the output from a machine vision system. Results from both systems indicate that fuzzy logic was able to accurately categorize the output in support of machinery decision making for automated control.

Understanding lubrication failures at the shoe/swashplate contact of automotive swashplate compressors will greatly enhance the reliability of the air conditioning system. Maintaining proper lubrication is not always possible during transient conditions. Therefore, a method for detection of lubricant loss is of great interest to the automotive industry. Three methods for detecting lubrication loss were examined: contact resistance, acoustic emission, and dynamic pressure oscillations. A mobile air conditioning test stand capable of recording many system parameters was used. Oil return to the compressor was monitored using an oil separator and a refrigerant/oil concentration sensor. Data were taken during steady oil return rates and after oil shut off. The electrical contact resistance between the shoe and swashplate was used to indicate changes in the lubrication conditions at this critical interface. Measurements were taken at two oil return rates during steady oil return tests.

This paper describes a ‘toolbox’ for modeling liquid cooling system networks within vehicle thermal management systems. Components which can be represented include pumps, coolant lines, control valves, heat sources and heat sinks, liquid-to-air and liquid-to-refrigerant heat exchangers, and expansion tanks. Network definition is accomplished through a graphical user interface, allowing system architecture to be easily modified. The elements of the toolbox are physically based, so that the models can be applied before hardware is procured. The component library was coded directly into MATLAB / SIMULINK and is intended for control system development, hardware-in-the-loop (HIL) simulation, and as a system emulator for on-board diagnostics and controls purposes. For HIL simulation and on-board diagnostics and controls, it is imperative that the model run in real-time.

A reconfigurable control system is an intelligent control system that detects faults within the system and adjusts its performance automatically to avoid mission failure, save lives, and reduce system maintenance costs. The concept was first successfully demonstrated by NASA between December 1989 and March 1990 on the F-15 flight control system (SRFCS), where software was integrated into the aircraft's digital flight control system to compensate for component loss by reconfiguring the remaining control loop. This was later adopted in the Boeing X-33. Other applications include modular robotics, reconfigurable computing structure, and reconfigurable helicopters. The motivation of this work is to test such control system designs for future long term space missions, more explicitly, the automation of life support systems.

Control system design is one of the most critical issues for implementation of intelligent vehicle systems. Wide ranged fundamental research has been undertaken in this area and the safety issues of the fully automated vehicles are clearly recognized. Study of vehicle performance constrains is essential for a good understanding of this problem. This paper discusses safety issues of heavy-duty vehicles under automatic steering control. It focuses on the analysis of the effect of tire force saturation. Vehicle handling characteristics are also analyzed to improve understanding of the truck dynamics and control tasks. A simple differential brake control is formulated to show its effect of on reducing trailer swing.

Automotive software complexity has been growing rapidly with time. The demand for automation in automotive segment including autonomous automobiles and software based products has caught the attention of researchers. Hence, it is necessary to check the complexity of automotive software and their reliability growth. Testing in the field of software artifact is resource intensive exercise. If project managers are able to put forward testing activities well then the testing resource consumptions may be much more resource/cost efficient. Reliability can be estimated during testing phase of software using software reliability growth models (SRGMs). A software package Computer Aided Software Reliability Estimation (CASRE) has many important SRGMs. These SRGMs are based on Non-Homogeneous Poisson Process (NHPP), Markov process or Bayesian models.

This paper presents system identification methods of using both frequency and time domain analyses to estimate system parameters for a nonlinear electrohydraulic (E/H) tractor steering system. The frequency domain method identified parameters of the system using a linear model with a nonlinear gain function. The time domain method identified those parameters using a discrete time expression. A method of multiple models was used to represent the nonlinear system for both frequency and time domain analyses. Simulation and test results showed that the modified identification method could satisfactorily identify the parameters of the nonlinear E/H tractor steering system.