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.
[1] Intelligent control can be divided into the following major sub-domains: New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them.
Neural networks have been used to solve problems in almost all spheres of science and technology.
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.