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<title>Intelligent control</title>

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<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">Intelligent control</span></span>

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<p><b>Intelligent control</b> is a class of <a href="Control_theory" title="Control theory">control</a> techniques that use various <a href="Artificial_intelligence" title="Artificial intelligence">artificial intelligence</a> computing approaches like <a href="Artificial_neural_networks" class="mw-redirect" title="Artificial neural networks">neural networks</a>, <a href="Bayesian_probability" title="Bayesian probability">Bayesian probability</a>, <a href="Fuzzy_logic" title="Fuzzy logic">fuzzy logic</a>, <a href="Machine_learning" title="Machine learning">machine learning</a>, <a href="Reinforcement_learning" title="Reinforcement learning">reinforcement learning</a>, <a href="Evolutionary_computation" title="Evolutionary computation">evolutionary computation</a> and <a href="Genetic_algorithm" title="Genetic algorithm">genetic algorithms</a>.<sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup>

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<div class="mw-heading mw-heading2"><h2 id="Overview">Overview</h2></div>

<p>Intelligent control can be divided into the following major sub-domains:

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<ul><li><a href="Artificial_neural_network" class="mw-redirect" title="Artificial neural network">Neural network</a> control</li>

<li><a href="Machine_learning_control" title="Machine learning control">Machine learning control</a></li>

<li><a href="Reinforcement_learning" title="Reinforcement learning">Reinforcement learning</a></li>

<li><a href="Bayesian_probability" title="Bayesian probability">Bayesian</a> control</li>

<li><a href="Fuzzy_control" class="mw-redirect" title="Fuzzy control">Fuzzy control</a></li>

<li><a href="Neuro-fuzzy" title="Neuro-fuzzy">Neuro-fuzzy</a> control</li>

<li><a href="Expert_System" class="mw-redirect" title="Expert System">Expert Systems</a></li>

<li><a href="Genetic_algorithm" title="Genetic algorithm">Genetic control</a></li></ul>

<p>New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them.

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<div class="mw-heading mw-heading3"><h3 id="Neural_network_controller">Neural network controller</h3></div>

<p><a href="Artificial_neural_network" class="mw-redirect" title="Artificial neural network">Neural networks</a> have been used to solve problems in almost all spheres of science and technology. Neural network control basically involves two steps:

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<ul><li>System identification</li>

<li>Control</li></ul>

<p>It has been shown that a <a href="Feed_forward_(control)" title="Feed forward (control)">feedforward</a> network with nonlinear, continuous and differentiable activation functions have <a href="Universal_approximation_theorem" title="Universal approximation theorem">universal approximation</a> capability. <a href="Recurrent_neural_network" title="Recurrent neural network">Recurrent</a> 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 <a href="Reinforcement_learning" title="Reinforcement learning">reinforcement learning</a> has shown its ability to control complex systems.

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<div class="mw-heading mw-heading3"><h3 id="Bayesian_controllers">Bayesian controllers</h3></div>

<p><a href="Bayesian_probability" title="Bayesian probability">Bayesian probability</a> has produced a number of algorithms that are in common use in many advanced control systems, serving as <a href="State_space_(controls)" class="mw-redirect" title="State space (controls)">state space</a> <a href="Estimator" title="Estimator">estimators</a> of some variables that are used in the controller.

</p><p>The <a href="Kalman_filter" title="Kalman filter">Kalman filter</a> and the <a href="Particle_filter" title="Particle filter">Particle filter</a> 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

<a href="Systems_theory" title="Systems theory">system-theoretic approach</a> to <a href="Control_engineering" title="Control engineering">control design</a>.

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<ul><li><a href="Action_selection" title="Action selection">Action selection</a></li>

<li><a href="AI_effect" title="AI effect">AI effect</a></li>

<li><a href="Applications_of_artificial_intelligence" title="Applications of artificial intelligence">Applications of artificial intelligence</a></li>

<li><a href="Artificial_intelligence_systems_integration" title="Artificial intelligence systems integration">Artificial intelligence systems integration</a></li>

<li><a href="Function_approximation" title="Function approximation">Function approximation</a></li>

<li><a href="Hybrid_intelligent_system" title="Hybrid intelligent system">Hybrid intelligent system</a></li></ul>

<dl><dt>Lists</dt></dl>

<ul><li><a href="List_of_emerging_technologies" title="List of emerging technologies">List of emerging technologies</a></li>

<li><a href="Outline_of_artificial_intelligence" title="Outline of artificial intelligence">Outline of artificial intelligence</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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</style><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://engineering.purdue.edu/ManLab/control/intell_control.htm">"Intelligent control"</a>.</cite></span>

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<ul><li><cite id="CITEREFAntsaklis,_P.J.1993" class="citation book cs1">Antsaklis, P.J. (1993). Passino, K.M. (ed.). <a rel="nofollow" class="external text" href="https://web.archive.org/web/20090410054107/http://www.nd.edu/~pantsakl/book1/intel.html"><i>An Introduction to Intelligent and Autonomous Control</i></a>. Kluwer Academic Publishers. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>0-7923-9267-1</bdi>. Archived from <a rel="nofollow" class="external text" href="http://www.nd.edu/~pantsakl/book1/intel.html">the original</a> on 10 April 2009.</cite></li></ul>

<ul><li><cite id="CITEREFLiu,_J.Wang,_W.Golnaraghi,_F.Kubica,_E.2010" class="citation journal cs1">Liu, J.; Wang, W.; Golnaraghi, F.; Kubica, E. (2010). "A Novel Fuzzy Framework for Nonlinear System Control". <i>Fuzzy Sets and Systems</i>. <b>161</b> (21): <span class="nowrap">2746–</span>2759. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.fss.2010.04.009">10.1016/j.fss.2010.04.009</a>.</cite></li></ul>

<div class="mw-heading mw-heading2"><h2 id="Further_reading">Further reading</h2></div>

<ul><li>Jeffrey T. Spooner, Manfredi Maggiore, Raul Ord onez, and Kevin M. Passino, <i>Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques</i>, John Wiley & Sons, NY;</li>

<li><cite id="CITEREFFarrell,_J.A.,_Polycarpou,_M.M.2006" class="citation book cs1">Farrell, J.A., Polycarpou, M.M. (2006). <i>Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches</i>. Wiley. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-471-72788-0</bdi>.</cite><span class="cs1-maint citation-comment"><code class="cs1-code">{{cite book}}</code>: CS1 maint: multiple names: authors list (link)</span></li>

<li><cite id="CITEREFSchramm,_G.1998" class="citation book cs1">Schramm, G. (1998). <i>Intelligent Flight Control - A Fuzzy Logic Approach</i>. TU Delft Press. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>90-901192-4-8</bdi>.</cite></li></ul></div><div><div class="zim-footer">

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