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

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.

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.

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.

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.

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.

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.

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.

This chapter focuses on AVCS for ITS, which are concerned with vehicle lateral motion control. The first part of the discussion is about a potential AVCS-II or AVCS-III technology for lane sensing and lane tracking in a lane-departure warning system. It is an intermediate step to fully automated steering, which is a potential AVCS-III technology.

Lane Sensing

To automate the lateral control of vehicles, it is necessary to measure the position of a vehicle in the lane. Various schemes have been proposed and tested for this purpose, including the use of (1) an on-board vision system that detects the painted lane markings on a highway; (2) continuous magnetic wires imbedded in the center of the lane; (3) radar or ultrasonic waves to measure the distance to sidewalls; (4) a forward-looking laser to detect reflective markers; and (5) discrete magnetic markers imbedded in the roadway. Clearly, the onboard vision systems are more difficult to develop but offer the potential capability of autonomous operation. The latter four schemes require the necessary highway infrastructure to be in place. These cooperative systems, however, are likely to provide more accurate lane-position information at higher speeds with simpler and more inexpensive devices in a vehicle. Magnetic wires and markers also are more reliable under adverse weather conditions (e.g., fog, rain, and snow). Prototype systems based on the other sensing schemes (i.e., vision, laser, and radar) have not been shown to work in inclement weather. Recently, GPS-based systems have been proposed for lateral and longitudinal positioning; however, the accuracy is still not good enough, even when differential GPS is used. Furthermore, GPS may not be available in all areas (e.g., urban canyons); therefore, systems based on GPS measurement and road maps are still under development.

The century-old automobile – the preferred mode for personal mobility throughout the developed world – is rapidly becoming a complex electromechanical system. Various new electromechanical technologies are being added to automobiles to improve operational safety, reduce congestion and energy consumption, and minimize environmental impact. This chapter introduces these trends and provides a brief overview of the major automobile subsystems and the automotive control systems described in detail in subsequent chapters.

Motivation, Background, and Overview

The main trends in automotive technology, and major automotive subsystems, are briefly reviewed.

Trends in Automotive Control Systems

The most noteworthy trend in the development of modern automobiles in recent decades is their rapid transformation into complex electromechanical systems. Current vehicles often include many new features that were not widely available a few decades ago. Examples include hybrid powertrains, electronic engine and transmission controls, cruise control, antilock brakes, differential braking, and active/semiactive suspensions.Many of these functions have been achieved using only mechanical devices. The major advantages of electromechanical (or mechatronic) devices, as opposed to their purely mechanical counterparts, include (1) the ability to embed knowledge about the system behavior into the system design, (2) the flexibility inherent in those systems to trade off among different goals, and (3) the potential to coordinate the functioning of subsystems. Knowledge about system behavior – in terms of vehicle, engine, or even driver dynamic models or constraints on physical variables – is included in the design of electromechanical systems. Flexibility enables adaptation to the environment, thereby providing more reliable performance in a wide variety of conditions. In addition, reprogrammability implies lower cost through exchanged and reused parts. Sharing of information makes it possible to integrate subsystems and obtain superior performance and functionality, which are not possible with uncoordinated systems.

For obvious reasons, engine-control systems were among the first developed for vehicles: The engine is not only the most crucial component for automobile performance; its emission performance also significantly affects the environment. As discussed in Chapter 1, engine-control systems may include fuel-injection control (i.e., air–fuel ratio control), ignition or spark-timing control, antiknock-control systems, idle-speed control, EGR control, and transmission control. The goal of engine-control systems is to ensure that the engine operates at near-optimal conditions at all times in terms of drivability, fuel economy, and emissions.

Overall, engine-control systems are complex due to the nonlinearity of many of the components and the interactions among the several related control functions: air–fuel ratio control, idle-speed control, knock (or spark-timing) control, EGR control, and transmission control. In this chapter, each major phase of the operation of a spark-ignited gasoline engine and its dynamic modeling is discussed from the control perspective. Subsequent chapters consider specific engine-control problems (e.g., air–fuel ratio control, spark timing, EGR, and idle-speed control), as well as control problems associated with hybrid and fuel-cell vehicles.

This textbook is organized in four major parts as follows:

1. Introduction and Background is an introduction to the topic of automotive control systems and a review of background material on engine modeling, vehicle dynamics, and human factors.

2. Powertrain Control Systems includes topics such as air–fuel ratio control, idle-speed control, spark-timing control, control of transmissions, control of hybrid-electric vehicles, and fuel-cell vehicle control.

3. Vehicle Control Systems covers cruise control and headway-control systems, traction-control systems (including antilock brakes), active suspensions, vehicle-stability control, and four-wheel steering.

4. Intelligent Transportation Systems (ITS) includes an overview of ITS technologies, collision detection and avoidance systems, automated highways, platooning, and automated steering.

With multiple chapters in each part, this textbook contains sufficient material for a one-semester course on automotive control systems. The coverage of the material is at the first-year graduate or advanced undergraduate level in engineering. It is assumed that students have a basic undergraduate-level background in dynamics, automatic control, and automotive engineering.