Editing Intelligent control

{{Short description|Artificial intelligence control techniques}}
'''Intelligent control''' is a class of [[Control theory|control]] techniques that use various [[artificial intelligence]] computing approaches like [[artificial neural networks|neural networks]], [[Bayesian probability]], [[fuzzy logic]], [[machine learning]], [[reinforcement learning]], [[evolutionary computation]] and [[genetic algorithm]]s.<ref>{{cite web|url= https://engineering.purdue.edu/ManLab/control/intell_control.htm|title= Intelligent control}}</ref>

== Overview ==
Intelligent control can be divided into the following major sub-domains:
* [[Artificial neural network|Neural network]] control
* [[Machine learning control]]
* [[Reinforcement learning]]
* [[Bayesian probability|Bayesian]] control
* [[Fuzzy control]]
* [[Neuro-fuzzy]] control
* [[Expert System]]s
* [[Genetic algorithm|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 ===
[[Artificial neural network|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 [[Feed forward (control)|feedforward]] network with nonlinear, continuous and differentiable activation functions have [[universal approximation theorem|universal approximation]] capability. [[recurrent neural network|Recurrent]] networks have also been used for system identification. Given, a set of input-output data pairs, system identification aims to form a mapping among these data pairs. Such a network is supposed to capture the dynamics of a system. For the control part, deep [[reinforcement learning]] has shown its ability to control complex systems.

=== Bayesian is controllers for online
===
[[Bayesian probability]] has produced a number of algorithms that are in common use in many advanced control systems, serving as [[State space (controls)|state space]]  [[estimator]]s of some variables that are used in the controller.

The [[Kalman filter]] and the [[Particle filter]] are two examples of popular Bayesian control components. The Bayesian approach to controller design often requires an important effort in deriving the so-called system model and measurement model, which are the mathematical relationships linking the state variables to the sensor measurements available in the controlled system. In this respect, it is very closely linked to the
[[Systems theory|system-theoretic approach]] to [[Control engineering|control design]].

== See also ==
* [[Action selection]]
* [[AI effect]]
* [[Applications of artificial intelligence]]
* [[Artificial intelligence systems integration]]
* [[Function approximation]]
* [[Hybrid intelligent system]]

; Lists
* [[List of emerging technologies]]
* [[Outline of artificial intelligence]]

== References ==
{{More footnotes needed|date=April 2011}}
{{reflist}}

* {{cite book
 | author = Antsaklis, P.J.
 | editor=Passino, K.M.
 | year = 1993
 | title = An Introduction to Intelligent and Autonomous Control
 | publisher = Kluwer Academic Publishers
 | isbn = 0-7923-9267-1
 | url = http://www.nd.edu/~pantsakl/book1/intel.html
 | archive-url = https://web.archive.org/web/20090410054107/http://www.nd.edu/~pantsakl/book1/intel.html
 | archive-date = 10 April 2009
}}

* {{cite journal
 | author = Liu, J.
 |author2=Wang, W. |author3=Golnaraghi, F. |author4=Kubica, E.  
 | year = 2010
 | title = A Novel Fuzzy Framework for Nonlinear System Control
 | journal = Fuzzy Sets and Systems
 | volume = 161
 | issue = 21
 | pages = 2746–2759
 | doi = 10.1016/j.fss.2010.04.009 
}}

== Further reading ==
* Jeffrey T. Spooner, Manfredi Maggiore, Raul Ord onez, and Kevin M. Passino, ''Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques'', John Wiley & Sons, NY;
* {{cite book
 | author = Farrell, J.A., Polycarpou, M.M.
 | year = 2006
 | title = Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches
 | publisher = Wiley
 | isbn = 978-0-471-72788-0
}}
* {{cite book
 | author = Schramm, G.
 | year = 1998
 | title = Intelligent Flight Control - A Fuzzy Logic Approach
 | publisher = TU Delft Press
 | isbn = 90-901192-4-8
}}

[[Category:Control theory]]
[[Category:Artificial intelligence]]
[[Category:Applications of Bayesian inference]]