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Increased Fetal Plasma Erythropoietin in Monochorionic Twin Pregnancies With Selective Intrauterine Growth Restriction and Abnormal Umbilical Artery Doppler
- Yao-Lung Chang, An-Shine Chao, Hsiu-Huei Peng, Shuenn-Dyh Chang, Sheng-Yuan Su, Kuan-Ju Chen, Po-Jen Cheng, Tzu-Hao Wang
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- Journal:
- Twin Research and Human Genetics / Volume 19 / Issue 4 / August 2016
- Published online by Cambridge University Press:
- 10 May 2016, pp. 383-388
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Hypoxia is the primary stimulus for the production of erythropoietin (EPO) in both fetal and adult life. Here, we investigated fetal plasma EPO concentrations in monochorionic (MC) twin pregnancies with selective intrauterine growth restriction (sIUGR) and abnormal umbilical artery (UA) Doppler. We diagnosed sIUGR in presence of (1) birth-weight discordance >20% and (2) either twin with a birth weight <10th percentile. An abnormal UA Doppler was defined as a persistent absent-reverse end diastolic flow (AREDF). The intertwin EPO ratio was calculated as the plasma EPO level of the smaller (or small-for-gestational-age) twin divided by the EPO concentration of the larger (or appropriate-for-gestational-age (AGA)) twin. Thirty-two MC twin pairs were included. Of these, 17 pairs were normal twins (Group 1), seven pairs were twins with sIUGR without UA Doppler abnormalities (Group 2), and eight pairs were twins with sIUGR and UA Doppler abnormalities (Group 3). The highest EPO ratio was identified in Group 3 (p < .001) but no significant differences were observed between Groups 1 and 2. Fetal hemoglobin levels did not differ significantly in the three groups, and fetal EPO concentration did not correlate with gestational age at birth. We conclude that fetal plasma EPO concentrations are selectively increased in MC twin pregnancies with sIUGR and abnormal UA Doppler, possibly as a result of uncompensated hypoxia.
7 - Control of Spark Timing
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 05 June 2012
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- 30 April 2012, pp 124-125
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Summary
The focus of this chapter is the control of spark timing. As discussed in Chapter 3, the spark is ignited in advance of TDC during the compression stroke. The exact timing can influence performance, fuel economy, emissions, and knock. As discussed in Chapter 1, advancing the spark timing can improve performance and reduce fuel consumption. However, advanced spark timing also can lead to engine knocking and potential engine damage. Spark-timing control can be used, for example, in idle-speed control (see Chapter 8) with throttle control. In this chapter, we focus on the occurrence of engine knock and the control of knock by adjustment of spark timing.
Knock Control
Knock occurs when an unburned part of the air–fuel mixture within the combustion chamber explodes prematurely. This is called knocking because it generates resonating gas-pressure oscillations, which are heard as a knocking sound. Knocking can lead to serious engine damage (Heywood 1989). Historically, a low-compression ratio or conservative spark timing was used to ensure that knocking did not occur; however, this approach sacrifices performance and fuel economy. Knock control can be used when a feedback sensor becomes available, which adjusts the spark timing based on a measured variable that indicates knock. Suitable measurements include the cylinder pressure (e.g., the 5- to 15-kHz region was found to be a good knock indicator), engine-block vibrations, light emission within the combustion chamber, and ion current through the gas mixture.
17 - Overview of Intelligent Transportation Systems
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 05 June 2012
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- 30 April 2012, pp 309-321
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Mobility is essential to the economic growth of any modern country and the well- being of its population. This is especially true for the United States because of its size and diffuse population. Without the efficient transport of people and goods, U.S. industries cannot compete effectively with overseas producers. However, the rapid growth in demand and the slower growth in the capacity of highway systems have led to congestion that is estimated to cost more than $40 billion annually. In 1970, motorists in the United States drove approximately 1 trillion vehicle-miles; by 1985, this had increased to 1.8 trillion vehicle-miles; and, by 2000, to 2.8 trillion vehicle-miles. These increases have led to serious congestion problems. For example, peak-hour traffic operating in congested conditions on urban Interstate highways increased from 40 percent in 1970 to nearly 70 percent in 1990. From 1982 to 2002, the vehicle-miles traveled increased by 79 percent, whereas highway-lane miles increased by only 3 percent. The number of roadways considered congested grew from 34 to 58 percent. However, construction of the more than 40,000 miles of the multilane, controlled-access Interstate Highway System essentially is completed. Major new construction, especially in dense urban areas, generally is not feasible and definitely cannot keep up with future traffic demand. Although some growth of the highway system is inevitable, the more efficient use of the existing system is essential.
In recent decades, there also have been tremendous changes in the areas of information technology, electronics, computers, and communications. The pace of these developments is simply astounding, and electronic devices have infiltrated every aspect of life, including vehicles (see Chapter 1). Intelligent Transportation Systems (ITS) (formerly known as Intelligent Vehicle Highway Systems [IVHS]), however, represent more than simply advances in automotive electronics. ITS incorporate a wide variety of electronic-based technologies, both on the vehicle and as part of the highway infrastructure, which collectively are moving the world into the next generation of highway operations. These technologies offer the promise of increased throughput on existing highways at reduced congestion levels as well as improved safety and convenience. The ITS vision encompasses smart (i.e., control, sensing, and communications) automobiles and highways collaborating for improved safety, mobility, trip quality, and productivity while also reducing congestion and environmental impact.
18 - Preventing Collisions
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 05 June 2012
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- 30 April 2012, pp 322-331
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Active Safety Technologies
An important motivation for AVCS technologies is safety, and a key safety technology is collision detection and avoidance systems. This type of safety enhancement is termed “active safety,” which is different from the traditional passive-safety concept (i.e., crashworthiness) (Sun and Chen 2010). The goal is to prevent collisions, not simply mitigate their effects. There are two major driving forces behind recent progress in the active-safety area, as follows:
The continuous progress in passive-safety systems has pushed the technology into a return/cost plateau. For example, a recent study shows that 42 percent of fatal-crash occupants can be saved by safety belts, and 47 percent can be saved with safety belts plus an air bag (Figure 18.1). For the remainder of accidents, the impact energy level is simply too high to be managed by reasonable engineering means using current technology. Most of these high-impact energy impacts, however, can be avoided altogether by active safety technologies (ASTs).
Recent changes in the standards for Corporate Average Fuel Economy (CAFE) continue to move toward reduced petroleum consumption in the United States. An important engineering approach for higher fuel efficiency is to lower vehicle weight; however, this solution is likely to raise safety concerns. It has been verified consistently that vehicle weight is the third-most important safety attribute for automobiles (i.e., after safety belts and air bags). Again, a possible solution to this safety concern is to apply ASTs.
Many enabling technologies and subsystems, which are useful for AST, have been widely available on passenger vehicles since 2005 (Table 18.1). Therefore, the add-on complexity and cost of introducing AST are greatly reduced. This fact, together with the obvious diminishing returns from passive-safety devices, has made active-safety systems increasingly attractive.
9 - Transmission Control
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 05 June 2012
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- 30 April 2012, pp 131-147
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Automotive transmissions are a key element in the powertrain that connects the power source to the drive wheels. To improve fuel economy, reduce emissions, and enhance drivability, many new technologies have been introduced in automotive transmissions in recent years (Sun & Hebbale 2005). Electronic control of automatic and continuously variable transmissions (CVTs) is considered briefly in this chapter, as well as the related topic of clutch control for AWD vehicles.
Electronic Transmission Control
Electronically controlled transmissions (ECT) are used to improve fuel economy, performance, drivability, and shift quality. It even may be possible – because of the flexibility provided by microcomputer software – to allow for “adaptive” shift schedules, which can be tailored for improved fuel economy, performance, or comfort. Because of the flexibility offered in ECT, the industry is seeing an accelerated trend away from traditional mechanical/hydraulic transmission control and a move toward the integrated ECT/engine/traction/speed control functions. In the following discussion, the major benefits of ECT are illustrated:
Precise Lockup Control. Historically, vehicles with an automatic transmission usually are about 10 percent less efficient than those with a manual transmission. The efficiency loss arises mainly from the slip of the torque converter. Torque-converter lockup has been used widely to improve fuel economy. With electronic control, it is possible to coordinate shift point, lockup schedule, and accurate timing of lockup release to reduce shock at gear shifting. Consequently, in recent years, the fuel economy of an ECT is only slightly worse than for a manual transmission.
Better Shift Quality. For the same reasons, clutch pressures can be controlled for improved shift quality. Coordinating with the engine-control unit (i.e., reducing engine torque during gear shifting) can improve substantially shift quality and reduce shock load on shift elements.
Flexible Driving. Different gear-shift patterns (e.g., power and economy) can be selected by a driver to better adapt to driving conditions. In general, if the information is available, the shift schedule can be programmed to consider vehicle status (e.g., warm-up and load), driving conditions (e.g., local or highway), and even driver status (e.g., passive or aggressive).
Weight Reduction. Both size and weight can be reduced because of reduced complexity. The number of component parts also was found to be greatly reduced, which is translated into higher reliability and lower cost.
Integrated Vehicle Control. Engine, cruise, and traction control functions can be integrated for superior performance.
Foolproof Design. Improper operation by a driver (e.g., down shifting or shifting into reverse gear at high vehicle speed) can be detected to avoid severe damage to the transmission.
Part IV - Intelligent Transportation Systems
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 05 June 2012
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- 30 April 2012, pp 307-308
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16 - Active Suspensions
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 30 April 2012, pp 287-306
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Automotive suspensions are discussed in Chapter 4 in connection with the vertical motion and ride properties of vehicles. A two-DOF quarter-car model was used, which is simple but sufficiently detailed to capture many of the key suspension-performance tradeoffs, such as ride quality (represented by sprung-mass acceleration); handling (represented by tire deflection); and packaging (represented by suspension stroke, also known as the rattle space). The performance index (see Chapter 4, Example 4.9) combines these three performance measures by assigning adjustable weights to the three performance terms.
Studies show that passive suspensions frequently are tuned to achieve good tradeoffs. Any improvement in one aspect of performance always is achieved at the expense of the deteriorated performance in another. The extra DOF offered by an active suspension could provide improved performance compared with a strictly passive suspension. The optimal design of a suspension for a quarter-car one-DOF model, as shown in Figure 16.1a (i.e., no unsprung-mass [wheel] dynamics), and the performance index, J1 = x21rms + ru2rms, has the structure shown in Figure 16.1b. Clearly, this structure, which includes a so-called skyhook damper, cannot be realized by the passive-suspension configuration shown in Figure 16.1c. Note that x1 in this one-DOF model represents the suspension stroke, r is a weight on control signal, and u is the control force, which also is directly proportional to sprung-mass acceleration. Clearly, an active suspension can provide performance benefits that cannot be achieved by using a strictly passive design (Figure 16.2). Furthermore, an active design can allow the performance to be user-selectable. For example, if a softer or a firmer ride characteristic is preferred by a user, the weights in the performance index used in the controller design can be changed (Hrovat 1988), leading to different controller gains and, consequently, different performance characteristics.
13 - Antilock Brake and Traction-Control Systems
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 30 April 2012, pp 232-256
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Antilock brake systems (ABS) were first introduced on railcars at the beginning of the 20th century. The original motivation was to avoid flat spots on the steel wheels; however, it soon was noted that stopping distance also was reduced by the ABS. Robert Bosch received a patent for ABS in 1936. In 1948, a Boeing B-47 was equipped with ABS to test its effectiveness in avoiding tire blowout on dry concrete and spinouts on icy runways. It used a “bang-bang” (i.e., dump brake pressure to zero, then rebuild) control strategy. Fully modulating ABS control strategies were introduced in the 1950s (e.g., Ford Lincoln, Goodyear, and HydroAire). A rear-wheels-only ABS was first available in luxury automobiles in the late 1960s. The systems used in the 1960s and 1970s were developed by Bendix, Kelsey-Hayes, and AC Electronics, among others. Legal concerns then delayed further development in the United States, and European companies took the lead in the next two decades. Demand skyrocketed in the early 1990s when the benefits of ABS for vehicle-steering control and shorter stopping distances were recognized and accepted widely. Most new passenger vehicles sold in the United States today are equipped with ABS. It is important to note that ABS will not work properly if the user input or road condition varies quickly. For example, according to a recent test report by the NHTSA (Forkenbrock et al. 1999), all of the test vehicles equipped with ABS stop within a longer distance than those without ABS on loose-gravel roads. Therefore, improvements still are needed in this relatively mature technology.
Appendices
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 30 April 2012, pp 361-390
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Review of Feedback Control
This appendix, a brief review of important concepts and methods from control theory, is intended to help readers review that material through self-study. A background in dynamic systems and control is essential for an understanding of the material presented in this textbook on automotive control systems. Furthermore, it is assumed that readers are familiar with the computational tools available for simulation and control in the MATLAB/Simulink environment. An excellent online tutorial is available at the Web site www.engin.umich.edu/class/ctms/.
Definitions and Motivation
System. A group of objects that are combined to function as an integrated part for a specific objective (e.g., an engine, a car, or a group of vehicles).
Inputs of a system are the means by which the state of the system can be changed.
Outputs of a system are the means by which the state of the system is manifested (e.g., in the speed control of a car, throttle [input] and speed [output] and in the directional control of a car, steering [input] and yaw rate [output]).
Control. This directs a system's inputs so that the outputs behave in the manner desired.
12 - Cruise and Headway Control
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 30 April 2012, pp 213-231
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One of the most widely adopted and visible control systems available on contemporary vehicles sold in the United States is the cruise control, which automatically regulates the vehicle longitudinal velocity by throttle adjustments. Typically, a vehicle cruise-control system is activated by a driver who wants to maintain a constant speed during long highway driving. This relieves the driver from having to continually adjust the throttle. The driver activates the cruise controller while driving at a particular speed, which then is recorded as the desired (or set-point) speed to be maintained by the controller.
Intelligent cruise control systems – also known as autonomous or adaptive cruise control (ACC) systems – are the next-generation product for cruise control. When no lead vehicle is within sight, an ACC vehicle behaves like a conventional cruise-control vehicle by maintaining a constant (i.e., target) speed. However, an ACC vehicle also has a headway-control mode. When the vehicle, using a range sensor, detects that it is close to the in front vehicle, the controller switches to headway-control mode and adjusts the speed to maintain a desired (i.e., safe) headway. Many field tests have been conducted to assess the real-life performance of ACC vehicles and consumers generally are receptive to the convenience provided by them. A rapidly growing number of luxury vehicles now offer ACC as an option.
Contents
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 30 April 2012, pp v-viii
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19 - Longitudinal Motion Control and Platoons
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 30 April 2012, pp 332-347
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This chapter discusses the longitudinal control of vehicle motion in the context of AVCS for ITS. It begins with a slightly refined version of a cruise-control system with preview, which uses site-specific information available through the highway infrastructure. It then builds on the ACC topic of Chapter 12 as an intermediate step to the control of vehicles operating in platoons.
Site-Specific Information
It is assumed that highway infrastructure can provide information to drivers and vehicles, which can be useful in numerous ways; for example, drivers can be warned of accidents, roadwork, inclement weather or road-surface conditions, congestion, and roadway characteristics such as grade and curvature. This already is being implemented in many areas using programmable road signs on busy urban Interstate highways (see Chapter 17). Here, we consider specifically the use of site-specific information to improve the performance of vehicle control systems discussed in previous chapters. The manner in which such site-specific information can be provided to a vehicle is discussed first and then the use of such information by vehicle control systems.
15 - Four-Wheel Steering
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 30 April 2012, pp 272-286
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5 - Human Factors and Driver Modeling
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 05 June 2012
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- 30 April 2012, pp 93-116
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Summary
It often is necessary to consider the human role (i.e., drivers and passengers) in the design of automotive systems. For example, this is evident in the discussion of passenger comfort as a key criterion for suspension design in Chapter 4. Human factors, also known as human engineering or human-factors engineering, consist of the application of behavioral and biological sciences to the design of machines and human–machine systems (Sheridan 2002). The term ergonomics is used as a synonym for human factors; however, it often is associated with narrower aspects that address anthropometry, biomechanics, and body kinematics as applied to the design of seating and workspaces. The terms cognitive engineering and cognitive ergonomics also are used to describe the sensory and cognitive aspects of human interactions with designed systems. All major automotive companies, as well as many government agencies (e.g., U.S. Department of Transportation, U.S. Department of Defense, NASA, and Federal Aviation Administration), have research and engineering groups that address human factors. This chapter is a brief introduction to human factors, especially as they apply to automotive control system design. The introduction is followed by a discussion of driver models, especially for vehicle steering.
Human Factors in Vehicle Automation
Humans (i.e., drivers and passengers) clearly interact with automotive control systems in many ways. Commercial success of a new control technology for vehicles may depend on not only the effectiveness of that technology but also acceptance by customers. Established automotive technologies (e.g., automatic transmissions and cruise control) are widely used in the United States but are less widely adopted in Europe. Navigation systems are more successful in Japan than in the United States. Customer acceptance often depends on many difficult-to-quantify factors. New automotive control technologies in which human factors must be considered carefully include electric vehicles, hybrid electric vehicles, traction control, ABS, intelligent (or adaptive) cruise control, cruise control, airbags, active suspensions, and navigation systems.
2 - Automotive Control-System Design Process
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 30 April 2012, pp 21-32
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Introduction
Generally, “solving” the controller design problem means finding the proper mathematical representation of a control action that meets a set of desired performance criteria. In reality, this is only one part of the solution (albeit an important part); the control-systems development process also includes steps for selecting the correct hardware – loaded with the proper software – for the controller module, which is the real end-product of this process (Figure 2.1).
The control-system development process begins by first developing the high-level system requirements, which are generally verbal and abstract and rarely point to a recognizable control design problem such as those that traditional engineering students would see in their control classes. The formal and technical requirements documents can be described as “wish lists” regarding the overall system features and performance. The result of the process is the controller module, which is to be deployed in bulk to the end product. The purpose of studying the control-systems development process is to provide a reliable, robust, and repeatable sequence of actions to develop ECUs.
In recent years, computer-aided design and analysis tools (e.g., MATLAB and Simulink) have improved the efficiency of design processes and increased the application of the model-based controller design and development process (Chrisofakis et al. 2011; Mahapatra et al. 2008; Michaels et al. 2010; Powers and Nicastri 2000). Figure 2.2 is a general outline of the model-based controller design and deployment process. The major components of this process (i.e., design, implementation, and testing) are discussed in the next section. The process outline in Figure 2.2 is based on development and testing portions that progress in parallel and continuously interact throughout the development cycle. This is, in fact, one of the most important features of model-based design, which enables debugging and validation of the current work while minimizing changes from the previous phase. Therefore, as the control development evolves, so does the testing platform for its debugging and validation.
4 - Review of Vehicle Dynamics
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 05 June 2012
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- 30 April 2012, pp 54-92
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Design of control systems for ground vehicles must start from an adequate understanding of their dynamic behavior. Although a detailed discussion of vehicle dynamics is beyond the scope of this chapter, simple dynamic models suitable for controller design are necessary for control studies and are developed and presented herein. These simple models are used in subsequent chapters as the basis for controller designs (e.g., cruise control, antilock brakes, traction control, steering control, and active suspensions). More complex (i.e., nonlinear, high-order, and fully coupled) models for vehicle dynamics often are needed to evaluate, using simulation studies, the controllers that are designed using simple control-design models. Such complex models are discussed in detail in the literature (Ellis 1966; Gillespie 1992; Segel 1990; Venhovens 1993; Wong 2008) and also can be implemented in commercial dynamic simulation software (e.g., ADAMS and CARSIM).
First, the standard notation and terminology for vehicle dynamics is introduced with definitions of reference frames and coordinates used to describe vehicle motion. Next, the longitudinal motion of the vehicle, including braking and acceleration, is presented. Then, lateral-motion dynamics, or vehicle steering or handling, is described. Finally, the vertical motion of vehicles is discussed.
10 - Control of Hybrid Vehicles
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 30 April 2012, pp 148-186
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Hybrid vehicles, especially hybrid electric vehicles (HEVs), demonstrate significant potential in reducing fuel consumption and exhaust emissions while maintaining driving performance. Hybrid vehicles are equipped with more than one power source, and at least one should be reversible. The reversible power source serves as an energy storage device, whereas the other power source is either the primary power source or a “range extender.” Most hybrid vehicles use a battery as the energy buffer, in which case they are known as HEVs. They can be classified as series, split, and parallel hybrids (Figure 10.1). The performance potential of the different configurations and the associated control problems are quite different.
The lower fuel consumption of a HEV typically is the result of several design features: (1) right sizing of the prime mover (i.e., internal combustion engine [ICE]); (2) load-leveling and engine shutdown to avoid inefficient engine operation; (3) regenerative braking; and (4) enhanced CVT function in certain configurations (i.e., series and split). Due to the multiple power sources and the complex configuration and operation modes associated with them, the control strategy for a hybrid vehicle is more complicated than for an engine-only vehicle. This chapter focuses on the design of control algorithms for series, parallel, and split hybrid vehicles. However, we first discuss the layout and pros and cons of the three configurations.
8 - Idle-Speed Control
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 30 April 2012, pp 126-130
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One of the most important and basic engine-control functions is idle-speed control. It requires consideration of the complete engine dynamics (as described in Chapter 3 about engine modeling) and has been a focus of various researchers to improve the performance of current and future engine designs (Grizzle et al. 2001; Wang et al. 2001). This chapter discusses engine idle-speed control.
The engine speed at idle is maintained at a desired value despite changes in engine loads (e.g., due to accessories such as an air-conditioning compressor). The controlled variable is engine idle speed and it is measured as discussed previously. The variables manipulated by the controller include the throttle angle and the spark advance. An optimal control approach provides an effective framework for the study of engine idle-speed control (Hrovat and Powers 1988). In this approach, we consider a vector, x, of state variables and a vector, u, of control variables. The problem then becomes finding the optimal controls, u*(t), which minimize an objective function J(x, u) subject to constraint equations g(x, u) = 0. In general, this is a complex problem because J and g are difficult to determine and the problem, once formulated, is difficult to solve.
11 - Modeling and Control of Fuel Cells for Vehicles
- A. Galip Ulsoy, University of Michigan, Ann Arbor, Huei Peng, University of Michigan, Ann Arbor, Melih Çakmakci, Bilkent University, Ankara
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- Automotive Control Systems
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- 05 June 2012
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- 30 April 2012, pp 187-210
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Automotive Control Systems
- A. Galip Ulsoy, Huei Peng, Melih Çakmakci
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This engineering textbook is designed to introduce advanced control systems for vehicles, including advanced automotive concepts and the next generation of vehicles for ITS. For each automotive control problem considered, the authors emphasise the physics and underlying principles behind the control system concept and design. This is an exciting and rapidly developing field for which many articles and reports exist but no modern unifying text. An extensive list of references is provided at the end of each chapter for all the topics covered. It is currently the only textbook, including problems and examples, that covers and integrates the topics of automotive powertrain control, vehicle control, and intelligent transportation systems. The emphasis is on fundamental concepts and methods for automotive control systems, rather than the rapidly changing specific technologies. Many of the text examples, as well as the end-of-chapter problems, require the use of MATLAB and/or SIMULINK.