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Adaptive Control
Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. For example, as an aircraft flies, its mass will slowly decrease as a result of fuel consumption; a control law is needed that adapts itself to such changing conditions. Adaptive control is different from robust control in that it does not need ''a priori'' information about the bounds on these uncertain or time-varying parameters; robust control guarantees that if the changes are within given bounds the control law need not be changed, while adaptive control is concerned with control law changing itself. Parameter estimation The foundation of adaptive control is parameter estimation, which is a branch of system identification. Common methods of estimation include recursive least squares and gradient descent. Both of these methods provide update laws that are used to modify estimates in real-time (i.e., as the system operates). L ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Robust Control
In control theory, robust control is an approach to controller design that explicitly deals with uncertainty. Robust control methods are designed to function properly provided that uncertain parameters or disturbances are found within some (typically compact) set. Robust methods aim to achieve robust performance and/or stability in the presence of bounded modelling errors. The early methods of Bode and others were fairly robust; the state-space methods invented in the 1960s and 1970s were sometimes found to lack robustness, prompting research to improve them. This was the start of the theory of robust control, which took shape in the 1980s and 1990s and is still active today. In contrast with an adaptive control policy, a robust control policy is static, rather than adapting to measurements of variations, the controller is designed to work assuming that certain variables will be unknown but bounded. (Section 1.5) In German; an English version is also available Criteria for robustn ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gain Scheduling
In control theory, gain scheduling is an approach to control of non-linear systems that uses a family of linear controllers, each of which provides satisfactory control for a different operating point of the system. One or more observable variables, called the ''scheduling variables'', are used to determine what operating region the system is currently in and to enable the appropriate linear controller. For example, in an aircraft flight control system, the altitude and Mach number Mach number (M or Ma) (; ) is a dimensionless quantity in fluid dynamics representing the ratio of flow velocity past a boundary to the local speed of sound. It is named after the Moravian physicist and philosopher Ernst Mach. : \mathrm = \frac ... might be the scheduling variables, with different linear controller parameters available (and automatically plugged into the controller) for various combinations of these two variables. A relatively large scope state of the art about gain scheduling has ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Intelligent Control
Intelligent control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation and genetic algorithms. Overview Intelligent control can be divided into the following major sub-domains: * Neural network control * Machine learning control * Reinforcement learning * Bayesian control * Fuzzy control * Neuro-fuzzy control * Expert Systems * Genetic control New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them. Neural network controller Neural networks have been used to solve problems in almost all spheres of science and technology. Neural network control basically involves two steps: * System identification * Control It has been shown that a feedforward network with nonlinear, continuous and differentiable activation function ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nonlinear Control
Nonlinear control theory is the area of control theory which deals with systems that are nonlinear, time-variant, or both. Control theory is an interdisciplinary branch of engineering and mathematics that is concerned with the behavior of dynamical systems with inputs, and how to modify the output by changes in the input using feedback, feedforward, or signal filtering. The system to be controlled is called the "plant". One way to make the output of a system follow a desired reference signal is to compare the output of the plant to the desired output, and provide feedback to the plant to modify the output to bring it closer to the desired output. Control theory is divided into two branches. Linear control theory applies to systems made of devices which obey the superposition principle. They are governed by linear differential equations. A major subclass is systems which in addition have parameters which do not change with time, called ''linear time invariant'' (LTI ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Robustness (computer Science)
In computer science, robustness is the ability of a computer system to cope with errors during execution1990. IEEE Standard Glossary of Software Engineering Terminology, IEEE Std 610.12-1990 defines robustness as "The degree to which a system or component can function correctly in the presence of invalid inputs or stressful environmental conditions" and cope with erroneous input. Robustness can encompass many areas of computer science, such as robust programming, robust machine learning, and Robust Security Network. Formal techniques, such as fuzz testing, are essential to showing robustness since this type of testing involves invalid or unexpected inputs. Alternatively, fault injection can be used to test robustness. Various commercial products perform robustness testing of software analysis. Introduction In general, building robust systems that encompass every point of possible failure is difficult because of the vast quantity of possible inputs and input combinations. Sin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Convergence
Convergence may refer to: Arts and media Literature *''Convergence'' (book series), edited by Ruth Nanda Anshen * "Convergence" (comics), two separate story lines published by DC Comics: **A four-part crossover storyline that united the four Weirdoverse titles in 1997 **A 2015 crossover storyline spanning the DC Comics Multiverse * ''Convergence'' (journal), an academic journal that covers the fields of communications and media * ''Convergence'' (novel), by Charles Sheffield * ''Convergence'' (Cherryh novel), by C. J. Cherryh Music * ''Convergence'' (Front Line Assembly album), 1988 * ''Convergence'' (David Arkenstone and David Lanz album), 1996 * ''Convergence'' (Dave Douglas album), 1999 * ''Convergence'' (Warren Wolf album), 2016 Other media * ''Convergence'' (2015 film), an American horror-thriller film * ''Convergence'' (2019 film), a British drama film *''Convergence'', a 2021 Netflix film by Orlando von Einsiedel * ''Convergence'' (Pollock), a 1952 oil painting by Jackso ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multiple Models
In control theory, multiple models is an approach to improve efficiency of adaptive system or observer system. It uses large number of models, which are distributed in the region of uncertainty, and based on the responses of the plant and the models. One model is chosen at every instant, which is closest to the plant according to some metric. The method offers satisfactory performance when no restrictions are put on the number of available models. Approaches There are two multiple model methods: * “Switching” the control input to the plant is based on the fixed model chosen at that instant. It is discontinuous, fast, but coarse. * “Switching and tuning”, an adaptive model is initialized from the location of the fixed model chosen, and the parameters of the best model determine the control to be used. It is continuous, slow, but accurate. Applications Multiple model method can be used for: * controlling an unknown plant - parameter estimate and the identification errors ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Loop Performance
Loop performance in control engineering indicates the performance of control loops, such as a regulatory PID loop. Performance refers to the accuracy of a control system's ability to track (output) the desired signals to regulate the plant process variables in the most beneficial and optimised way, without delay or overshoot. Importance Regulatory control loops are critical in automated manufacturing and utility industries like refining, paper and chemicals manufacturing, power generation, among others. They are used to control a particular parameter within a process. The parameter that is being controlled could be temperature, pressure, flow or level of some process. For example, temperature controllers are used in boilers which are used in production of gasoline. Software There are many software applications that help in measuring and analysing the performance of control loops in industrial plants. Benchmarking Benchmarking is the practice of comparing business processes and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Iterative Learning Control
Iterative Learning Control (ILC) is a method of tracking control for systems that work in a repetitive mode. Examples of systems that operate in a repetitive manner include robot arm manipulators, chemical batch processes and reliability testing rigs. In each of these tasks the system is required to perform the same action over and over again with high precision Precision, precise or precisely may refer to: Science, and technology, and mathematics Mathematics and computing (general) * Accuracy and precision, measurement deviation from true value and its scatter * Significant figures, the number of digit .... This action is represented by the objective of accurately tracking a chosen reference signal r(t) on a finite time interval. Repetition allows the system to improve tracking accuracy from repetition to repetition, in effect learning the required input needed to track the reference exactly. The learning process uses information from previous repetitions to improve the contro ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Parameter Estimation
Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data. An ''estimator'' attempts to approximate the unknown parameters using the measurements. In estimation theory, two approaches are generally considered: * The probabilistic approach (described in this article) assumes that the measured data is random with probability distribution dependent on the parameters of interest * The set-membership approach assumes that the measured data vector belongs to a set which depends on the parameter vector. Examples For example, it is desired to estimate the proportion of a population of voters who will vote for a particular candidate. That proportion is the parameter sought; the estimate is based on a small random sample of voters. Alternatively, it is ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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 (from Greek ''analogia'', ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
