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Control theory is used in control system engineering to design automation that have revolutionized manufacturing, aircraft, communications and other industries, and created new fields such as robotics. Extensive use is usually made of a diagrammatic style known as the block diagram.
One example is the genetic algorithm for optimizing coefficients of a PID controller [2] or discrete-time optimal control. [ 3 ] Control design as regression problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for ...
Kalman filtering uses a system's dynamic model (e.g., physical laws of motion), known control inputs to that system, and multiple sequential measurements (such as from sensors) to form an estimate of the system's varying quantities (its state) that is better than the estimate obtained by using only one measurement alone.
In terms of mathematical optimization, dynamic programming usually refers to simplifying a decision by breaking it down into a sequence of decision steps over time. This is done by defining a sequence of value functions V1, V2, ..., Vn taking y as an argument representing the state of the system at times i from 1 to n.
Iterative learning control. Iterative Learning Control (ILC) is an open-loop control approach of tracking control for systems that work in a repetitive mode. [1] Examples of systems that operate in a repetitive manner include robot arm manipulators, chemical batch processes and reliability testing rigs.
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. [1] It has numerous applications in science, engineering and operations research. For example, the dynamical system might be a spacecraft with controls corresponding to ...
The algorithmic state machine (ASM) is a method for designing finite state machines (FSMs) originally developed by Thomas E. Osborne at the University of California, Berkeley (UCB) since 1960, [1] introduced to and implemented at Hewlett-Packard in 1968, formalized and expanded since 1967 and written about by Christopher R. Clare since 1970.
e. In theoretical computer science, a nondeterministic Turing machine (NTM) is a theoretical model of computation whose governing rules specify more than one possible action when in some given situations. That is, an NTM's next state is not completely determined by its action and the current symbol it sees, unlike a deterministic Turing machine.