Intelligent control
Intelligent control is a class of
Overview
Intelligent control can be divided into the following major sub-domains:
- Neural networkcontrol
- 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
- System identification
- Control
It has been shown that a feedforward network with nonlinear, continuous and differentiable activation functions have universal approximation capability. 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 controllers
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 system-theoretic approach to control design.
See also
- Action selection
- AI effect
- Applications of artificial intelligence
- Artificial intelligence systems integration
- Function approximation
- Hybrid intelligent system
- Lists
References
This article includes a list of general references, but it lacks sufficient corresponding inline citations. (April 2011) |
- Antsaklis, P.J. (1993). Passino, K.M. (ed.). An Introduction to Intelligent and Autonomous Control. Kluwer Academic Publishers. ISBN 0-7923-9267-1. Archived from the originalon 10 April 2009.
- Liu, J.; Wang, W.; Golnaraghi, F.; Kubica, E. (2010). "A Novel Fuzzy Framework for Nonlinear System Control". Fuzzy Sets and Systems. 161 (21): 2746–2759. .
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;
- Farrell, J.A., Polycarpou, M.M. (2006). Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches. Wiley. ISBN 978-0-471-72788-0.)
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: CS1 maint: multiple names: authors list (link - Schramm, G. (1998). Intelligent Flight Control - A Fuzzy Logic Approach. TU Delft Press. ISBN 90-901192-4-8.