From a broad historical perspective, each of these applications began at a low level of automation, and through the years each has evolved into a more autonomous system. For example, today's automotive cruise controllers are the ancestors of the controllers that achieve coordinated control of steering, braking, and throttle for autonomous vehicle iving. And the terrain following, terrain avoidance control systems for low-altitude flight are ancestors of an artificial ilot's associate that can integrate mission and tactical planning activities. The general trend has been for engineers to incrementally"add more intelligence"in response to consumer, industrial, and government demands and thereby creates systems with increased levels of autonomy. In this process of enhancing autonomy by adding intelligence, engineers often study how humans solve problems, then try to directly automate their knowledge and techniques to achieve high levels of automation. Other times, engineers study how intelligent biological systems perform complex tasks, then seek to automate nature's approach"in a computer algorithm or circuit implementation to solve a practical technological problem(e.g, in certain vision systems). Such approaches where we seek to emulate the functionality of an intelligent biological system (e.g, the human) to solve a technological problem can be collectively named "intelligent systems and control techniques. It is by using such techniques that some engineers are trying to create highly autonomous systems such as those listed above In this section we will explain how "intelligent"control methods can be used to create autonomous systems. First we will define intelligent control. " Next, we provide a framework for the operation of autonomous systems to clarify the ultimate goal of achieving autonomous behavior in complex technological systems 4.6.1 What Is Intelligent control? Since the answer to this question can get rather philosophical, let us focus on a working definition that does not dwell definitions of "intelligence"(since there is no widely accepted one partly because biological intelligence seems to have many dimensions and appears to be very complex) and issues of whether we truly model or emulate intelligence, but instead focuses on(1)the methodologies used in the construction of controllers and (2) the ability of an artificial system to perform activities normally performed by humans Intelligent control"techniques offer alternatives to conventional approaches by borrowing ideas from intelligent biological systems. Such ideas can either come from humans who are, for example, experts at manually solving the control problem, or by observing how a biological system operates and using analogous techniques in the solution of control problems. For instance, we may ask a human driver to provide a detailed explanation of how she or he manually solves an automated highway system intervehicle distance control problem, then use this knowledge directly in a fuzzy controller. In another approach, we may train an artificial neural network to remember how to regulate the intervehicle spacing by repeatedly providing it with examples of how to perform such a task. After the neural network has learned the task, it can be implemented on the vehicle to regulate the intervehicle distance by recalling the proper throttle input for each value of the intervehicle distance that is sensed. In another approach, genetic algorithms may be used to automatically synthesize and tune a control algorithm for the intervehicle spacing control problem by starting with a population of candidate controllers and then iteratively allowing the most fit controller, which is determined according to the performance specifications, to survive in an artificial evolution process implemented in a computer. In this way the controller evolves over time, successively improving its performance and adapting to its environment, until it meets the prespecified performance objectives Such intelligent control techniques may exploit the information represented in a mathematical model or may heavily ely on heuristics on how best to control the process. The primary difference from conventional approaches, such as PID PDF文件使用" pdffactory Pro"试用版本创建ww. fineprint,com,cnFrom a broad historical perspective, each of these applications began at a low level of automation, and through the years each has evolved into a more autonomous system. For example, today's automotive cruise controllers are the ancestors of the controllers that achieve coordinated control of steering, braking, and throttle for autonomous vehicle driving. And the terrain following, terrain avoidance control systems for low-altitude flight are ancestors of an artificial pilot's associate that can integrate mission and tactical planning activities. The general trend has been for engineers to incrementally “add more intelligence” in response to consumer, industrial, and government demands and thereby creates systems with increased levels of autonomy. In this process of enhancing autonomy by adding intelligence, engineers often study how humans solve problems, then try to directly automate their knowledge and techniques to achieve high levels of automation. Other times, engineers study how intelligent biological systems perform complex tasks, then seek to automate "nature's approach" in a computer algorithm or circuit implementation to solve a practical technological problem (e.g., in certain vision systems). Such approaches where we seek to emulate the functionality of an intelligent biological system (e.g., the human) to solve a technological problem can be collectively named "intelligent systems and control techniques." It is by using such techniques that some engineers are trying to create highly autonomous systems such as those listed above. In this section we will explain how "intelligent" control methods can be used to create autonomous systems. First we will define "intelligent control." Next, we provide a framework for the operation of autonomous systems to clarify the ultimate goal of achieving autonomous behavior in complex technological systems. 4.6.1 What Is "Intelligent Control"? Since the answer to this question can get rather philosophical, let us focus on a working definition that does not dwell on definitions of "intelligence" (since there is no widely accepted one partly because biological intelligence seems to have many dimensions and appears to be very complex) and issues of whether we truly model or emulate intelligence, but instead focuses on (1) the methodologies used in the construction of controllers and (2) the ability of an artificial system to perform activities normally performed by humans. "Intelligent control" techniques offer alternatives to conventional approaches by borrowing ideas from intelligent biological systems. Such ideas can either come from humans who are, for example, experts at manually solving the control problem, or by observing how a biological system operates and using analogous techniques in the solution of control problems. For instance, we may ask a human driver to provide a detailed explanation of how she or he manually solves an automated highway system intervehicle distance control problem, then use this knowledge directly in a fuzzy controller. In another approach, we may train an artificial neural network to remember how to regulate the intervehicle spacing by repeatedly providing it with examples of how to perform such a task. After the neural network has learned the task, it can be implemented on the vehicle to regulate the intervehicle distance by recalling the proper throttle input for each value of the intervehicle distance that is sensed. In another approach, genetic algorithms may be used to automatically synthesize and tune a control algorithm for the intervehicle spacing control problem by starting with a population of candidate controllers and then iteratively allowing the most fit controller, which is determined according to the performance specifications, to survive in an artificial evolution process implemented in a computer. In this way the controller evolves over time, successively improving its performance and adapting to its environment, until it meets the prespecified performance objectives. Such intelligent control techniques may exploit the information represented in a mathematical model or may heavily rely on heuristics on how best to control the process. The primary difference from conventional approaches, such as PID PDF 文件使用 "pdfFactory Pro" 试用版本创建 www.fineprint.com.cn