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Control Theory and Optimization Available On-Line at Britanica.Com The Place of Control Systems In Attachment Theory One of Bowlby’s key insights was that many of Freud’s best ideas about close relationships and the importance of early experience were logically independent of the drive reduction motivation theory that Freud used to explain them. In order to preserve these insights, Bowlby looked for a scientifically defensible alternative to Freud’s drive reduction motivation theory. Freud viewed infants as clingy and dependent; interested in drive reduction rather than in the environment. Ethological observations present a very different view. The notion that human infants are competent, inquisitive, and actively engaged in mastering their environments was also familiar to Bowlby from Piaget’s detailed observations of his own three children presented in The Origin of Intelligence in Infants. One of Bowlby’s key insights was that the newly emerging field of control systems theory offered a way of explaining infants’ exploration, monitoring of access to attachment figures, and awareness of the environment. This was a scientifically defensible alternative to Freud’s drive reduction theory of motivation. It placed the emphasis on adaptation to the real world rather than to drive states and emphasized actual experience rather than intra-psychic events as influences on develop- ment and individual differences. Note that the first step toward this alternative motivation model was reformulating athe infant- mother (and implicitly adult-adult) bonds in terms of the secure base phenomenon. Without the secure base concept, we have no control systems alternative to Freud’s drive theory. Thus, it is logically necessary, at every turn, to keep the secure base formulation at the center of attachment theory. See Waters & Cummings, Child Development, June 2000 for elaboration on this point. The following material was compiled from articles on control theory and optimization available on-line at Britanica.com. E.W. Control Theory General Background As long as human culture has existed, control has always meant some kind of power over man's environment. Cuneiform fragments suggest that the control of irrigation systems in Mesopotamia was a well-developed art at least by the 20th century BC. There were some ingenious control devices in the Greco-Roman culture, the details of which have been preserved. Methods for the automatic operation of windmills go back at least to the Middle Ages. Large-scale implementation of the idea of control, however, was impossible without a high-level of technological sophistication, and it is probably no accident that the principles of modern control started evolving only in the 19th century, concurrently with the Industrial Revolution. A serious scientific study of this field began only after World War II and is now a major aspect of what has come to be called the second industrial revolution. Although control is sometimes equated with the notion of feedback control (which involves the transmission and return of information)--an isolated engineering invention, not a scientific discipline--modern usage tends to favour a rather wide meaning for the term; for instance, control and regulation of machines, muscular coordination and metabolism in biological organisms, prosthetic devices; also, broad aspects of coordinated activity in the social sphere such as optimization of business operations, control of economic activity by government policies, and even control of political decisions by democratic processes. Scientifically speaking, modern control should be viewed as that branch of system theory concerned with changing the behaviour of a given complex system by external actions. (For aspects of system theory related to information, see below.) If physics is the science of understanding the physical environment, then control should be viewed as the science of modifying that environment, in the physica, biological, or even social sense. Much more than even physics, control is a mathematically-oriented science. Control principles are always expressed in mathematical form and are potentially applicable to any concrete situation. At the same time, it must be emphasized that success in the use of the abstract principles of control depends in roughly equal measure on the status of basic scientific knowledge in the specific field of application, be it engineering, physics, astronomy, biology, medicine, econometrics, or any of the social sciences. This fact should be kept in mind to avoid confusion between the basic ideas of control (for instance, controllability) and certain spectacular applications of the moment in a narrow area (for instance, manned lunar travel). Examples of modern control systems To clarify the critical distinction between control principles and their embodiment in a real machine or system, the following common examples of control may be helpful. There are several broad classes of control systems, of which some are mentioned below. Machines that cannot function without (feedback) control Many of the basic devices of contemporary technology must be manufactured in such a way that they cannot be used for the intended task without modification by means of control external to the device. In other words, control is introduced after the device has been built; the same effect cannot be brought about (in practice and sometimes even in theory) by an intrinsic modification of the characteristics of the device. The best known examples are the vacuum-tube or transistor amplifiers for high-fidelity sound systems. Vacuum tubes or transistors, when used alone, introduce intolerable distortion, but when they are placed inside a feedback control system any desired degree of fidelity can be achieved. A famous classical case is that of powered flight. Early pioneers failed, not because of their ignorance of the laws of aerodynamics, but because they did not realize the need for control and were unaware of the basic principles of stabilizing an inherently unstable device by means of control. Jet aircraft cannotbe operated without automatic control to aid the pilot, and control is equally critical for helicopters. The accuracy of inertial navigation equipment (the modern space compass) cannot be improved indefinitely because of basic mechanical limitations, but these limitations can be reduced by several orders of magnitude by computer- directed statistical filtering, which is a variant of feedback control. Robots On the most advanced level, the task of control science is the creation of robots. This is a collective term for devices exhibiting animal-like purposeful behaviour under the general command of (but without direct help from) man. Industrial manufacturing robots are already fairly common, but real breakthroughs in this field cannot be anticipated until there are fundamental scientific advances with regard to problems related to pattern recognition and the mathematical structuring of brain processes. Control Systems A control system is a means by which a variable quantity or set of variable quantities is made to conform to a prescribed norm. It either holds the values of the controlled quantities constant or causes them to vary in a prescribed way. A control system may be operated by electricity, by mechanical means, by fluid pressure (liquid or gas), or by a combination of means. When a computer is involved in the control circuit, it is usually more convenient to operate all of the control systems electrically, although intermixtures are fairly common. Development of control systems. Control systems are intimately related to the concept of automation, but the two fundamental types of control systems, feed-forward and feedback, have classic ancestry. The loom invented by Joseph Jacquard of France in 1801 is an early example of feed-forward; a set of punched cards programmed the patterns woven by the loom; no information from the process was used to correct the machine's operation. Similar feed-forward control was incorporated in a number of machine tools invented in the 19th century, in which a cutting tool followed the shape of a model. Feedback control, in which information from the process is used to correct a machine's operation, has an even older history. Roman engineers maintained water levels for their aqueduct system by means of floating valves that opened and closed at appropriate levels. The Dutch windmill of the 17th century was kept facing the wind by the action of an auxiliary vane that moved the entire upper part of the mill. The most famous example from the Industrial Revolution is James Watt's flyball governor of 1769, a device that regulated steam flow to a steam engine to maintain constant engine speed despite a changing load. The first theoretical analysis of a control system, which presented a differential-equation model of the Watt governor, was published by James Clerk Maxwell, the Scottish physicist, in the 19th century. Maxwell's work was soon generalized and control theory developed by a number of contributions, including a notable study of the automatic steering system of the U.S. battleship "New Mexico," published in 1922. The 1930s saw the development of electrical feedback in long-distance telephone amplifiers and of the general theory of the servomechanism, by which a small amount of power controls a very large amount and makes automatic corrections. The pneumatic controller, basic to the development of early automated systems in the chemical and petroleum industries, and the analogue computer followed. All of these developments formed the basis
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