Control theory, the grandchild of cybernetics, has many important applications, especially in robotics.
Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms.
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
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.