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 ...
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Multi Observer
Multi is a shortened form of "multiple". It may refer to: * Alternate character, in online gaming * Multi two diamonds, a contract bridge convention * Multirhyme, a synonym for feminine rhyme used in hip hop music * Multi (''To Heart''), a character from the visual novel and anime series ''To Heart'' * Multi-touch display See also * Multiculturalism, a public policy approach for managing cultural diversity in a multiethnic society * Multitude, a term used by some philosophers to refer to the population of the world * ''Multitudes'' (journal), a French philosophical, political and artistic monthly review * Multiplication, an elementary arithmetic operation * Multitasking (other) Multitasking may refer to: * Computer multitasking, the concurrent execution of multiple tasks (also known as processes) over a certain period of time ** Cooperative multitasking ** Pre-emptive multitasking * Human multitasking, the apparent perf ...

Control Theory
Control theory is a field of mathematics that deals with the 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; often with the aim to achieve a degree of 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 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 which are also studied are controllability and observability. Control theory is used in control system eng ...
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State Observer
In control theory, a state observer or state estimator is a system that provides an estimate of the internal state of a given real system, from measurements of the input and output of the real system. It is typically computer-implemented, and provides the basis of many practical applications. Knowing the system state is necessary to solve many control theory problems; for example, stabilizing a system using state feedback. In most practical cases, the physical state of the system cannot be determined by direct observation. Instead, indirect effects of the internal state are observed by way of the system outputs. A simple example is that of vehicles in a tunnel: the rates and velocities at which vehicles enter and leave the tunnel can be observed directly, but the exact state inside the tunnel can only be estimated. If a system is observable, it is possible to fully reconstruct the system state from its output measurements using the state observer. Typical observer model Li ...
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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 ...
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