All control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms can be put into the class of intelligent control.
So we can subdivide intelligent control into following major sub-domains:
- Neural network control
- Bayesian control
- Fuzzy (logic) control
- Neuro-fuzzy control
- Expert Systems
- Genetic control
- Intelligent agents (Cognitive/Conscious control)
New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them.
Neural network controllers
Neural networks have been used to solve problems in almost all spheres of science of technology. Neural network control basically involves two steps:
- System identification
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.
Some references for more information: 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 ; Jay Farrell, Marios Polycarpou, Adaptive Approximation Based Control:Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches, John Wiley & Sons, NJ
Bayesian probability has produced a number of algorithms that are in common use in many advanced control systems, serving as state space estimators 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 requires often 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.