Introduction of Intelligent Control

Intelligent Control is a control methods are developed that attempt to emulate important characteristics of human intellligent. Control system have two loops, there are open loops and close loop. Close loop have feedback that reduce the effects of parameter changes (disturbances). Comparison of open loop is little feedback will affect the changes compared to the open loop very much changed, so that the open loop is not appropriate if its used for system that require precision and comfortability.

Intelligent control methodologies are being applied to robotics and automation, communications, manufacturing, traffic control, etc.Characteristics of Intelligent control include adaptation and learning, planning under large uncertainty and coping with large amounts of data.Neural networks (NN), Fuzzy control, genetic algorithms (GA), planning systems, expert systems, hybrid system are all areas where related work is taking place. There are many areas in control where learning can be used to advantage and these needs can be briefly classified as follows : 1. Learning about the plant; how to incorporate changes and then how to derive new plant models, 2. Learning about the controller; how to adjust certain controller parameter to enhance performance, 3. Learning about the environment; this can be done using methods ranging from passive observation to active experimentation, 4. Learning new design goals and constraints. The relation between adaptive control and learning control is achieved, learning is achieved. When an adaptive control algorithm is used to adapt the controller parameter s so that for example stability is maintained. In this case the system learns and the knowledge acquired is the new values for the parameters. Note however, that if later the same changes occur again and the system is described by exactly the same parameters identified earlier, the adaptive control algorithm still needs to recalculate the controller and perhaps the plant parameters since nothing was kept in memory. So, in that sense the system has not learned. It has certainly learned what to do when certain type of changes take place. In particular, it has been told exactly what to do, that is it was given the adaptive algorithm, and this is knowledge by rote learning. The knowledge represented by the new values of the controller and the plant parameters and the circumstances under which these values are appropriate, are not retained. Adaptation means system is able to adapt to environmental changes.Learning ability is done by reward and punishment (behaviour based control), model reference and reinforcement learning. Planning under large uncertainty means system capable of planning even though many uncertainties.Coping with large amount data (generalization) how to handle data that is too much. (tr)