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Dual Control Theory
Dual control theory is a branch of control theory that deals with the control of systems whose characteristics are initially unknown. It is called ''dual'' because in controlling such a system the controller's objectives are twofold: * (1) Action: To control the system as well as possible based on current system knowledge * (2) Investigation: To experiment with the system so as to learn about its behavior and control it better in the future. These two objectives may be partly in conflict. In the context of reinforcement learning, this is known as the exploration-exploitation trade-off (e.g. Multi-armed bandit#Empirical motivation). Dual control theory was developed by Alexander Aronovich Fel'dbaum in 1960. He showed that in principle the optimal solution can be found by dynamic programming, but this is often impractical; as a result a number of methods for designing sub-optimal dual controllers have been devised. Example To use an analogy Analogy is a comparison or corre ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Control Theory
Control theory is a field of control engineering and applied mathematics that deals with the control system, control of dynamical systems in engineered processes and machines. The objective is to develop a model or algorithm governing the application of system inputs to drive the system to a desired state, while minimizing any ''delay'', ''overshoot'', or ''steady-state error'' and ensuring a level of control Stability theory, stability; often with the aim to achieve a degree of Optimal control, optimality. To do this, a controller with the requisite corrective behavior is required. This controller monitors the controlled process variable (PV), and compares it with the reference or Setpoint (control system), set point (SP). The difference between actual and desired value of the process variable, called the ''error'' signal, or SP-PV error, is applied as feedback to generate a control action to bring the controlled process variable to the same value as the set point. Other aspects ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reinforcement Learning
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the cumulative reward (the feedback of which might be incomplete or delayed). The search for this balance is known as the exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dyn ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Alexander Aronovich Feldbaum
Alexander Aronovich Feldbaum (1913 — 1969) was a Soviet scientist in the field of automatic control and fundamental computer science. He is one of the founders of optimal control, and proposed dual control theory in the study of self-adjusting and self-learning systems. Biography He was born on August 16, 1913, in Yekaterinoslav (now Dnipro, Ukraine). In 1924, he entered directly into the fifth grade of middle school. In 1937, he graduated from the Moscow Power Engineering Institute, and in 1941, the correspondence department of the MSU Faculty of Mechanics and Mathematics. Since 1936, A. A. Feldbaum has been an employee of the All-Russian Electrotechnical Institute (Всероссийский электротехнический институт). In 1939, he published his first scientific paper dedicated to the theory of automatic control. In 1943, he defended his PhD thesis on the theory of controlling devices. Since 1945, A. A. Feldbaum taught at the Peter the Great Mil ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Optimal Control
Optimal control theory is a branch of control theory that deals with finding a control for a dynamical system over a period of time such that an objective function is optimized. It has numerous applications in science, engineering and operations research. For example, the dynamical system might be a spacecraft with controls corresponding to rocket thrusters, and the objective might be to reach the Moon with minimum fuel expenditure. Or the dynamical system could be a nation's economy, with the objective to minimize unemployment; the controls in this case could be fiscal and monetary policy. A dynamical system may also be introduced to embed operations research problems within the framework of optimal control theory. Optimal control is an extension of the calculus of variations, and is a mathematical optimization method for deriving control policies. The method is largely due to the work of Lev Pontryagin and Richard Bellman in the 1950s, after contributions to calculus of v ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Car Analogy
The car analogy is a common technique, used predominantly in engineering textbooks, to ease the understanding of abstract concepts in which a car, its composite parts, and common circumstances surrounding it are used as analogs for elements of the conceptual systems. The car analogy can be seen elsewhere, in textbooks covering other subjects and at various educational levels, such as explaining regulation of human temperature. Uses of car analogies The efficiency of car analogies reside on their capacity to explain difficult concepts (usually due to their high abstraction level) on more mundane terms with which the target audience is comfortable, and with which many also have a special interest. Due to that, car analogies appear more often on works related to applied sciences and technology. In order to work, car analogies translate agents of action as the car driver, the seller, or police officers; likewise, elements of a system are referred as car pieces, such as wheels, motor, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
