30-04-2012, 04:16 PM
INTELLIGENT CONTROL
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INTRODUCTION
Process control and monitoring is becoming increasingly automated. In today’s environment, large process control systems and distributed control systems are being automated using computers. Such automation is absolutely essential for fast and complex processes like power generation, transmission and distribution systems, petrochemical processing, metal industries, discrete manufacturing, etc…
these manufacturing and processing environments are exceedingly complex with volumes of data and more opportunities to go wrong than to go right. The management of complex processes is a difficult task requiring a wide range of knowledge and expertise.
In intelligent control, the control algorithms are developed by emulating certain characteristics of intelligent biological systems. An intelligent control techniques that holds tremendous promise combines the methodologies of artificial intelligence.(AI)
ARTIFICIAL INTELLIGENCE
Artificial intelligence(AI) is a field of computer science with the development and deployment of machines that mimic human behaviour. It attempts to emulate certain mental processes of humans by using computer models. AI can be viewed as a collection of tools, concepts, techniques and methodologies that allows certain problems to be addressed that cannot easily be modelled in a closed from analytic solution. It can also be viewed as a way attacking a problem for which there is no rational design center without AI, or as a way of increasing the design center of an existing problem whose solution is not really satisfactory.
Many techniques are used to make the building of intelligent reasoning systems feasible in realistic time frames with proper software engineering practices. AI is not a magic, and it is these techniques along with software environments that translate what seems to be magic into actual step and repeat methodological approaches.
AI can be divided into the following major categories.
Expert systems
Robotics
Vision
Natural language processing
Artificial neural networks(ANN)
Fuzzy logic
ADVANTAGES:-
expert need to be present for a consultation, it may be delivered to remote location where expertise may not be available.
Expert systems do not suffer from some of the shortcoming of human beings, e.g. tiredness or carelessness the workload increases.
Expert knowledge is saved and readily available because the expert system the become a repository for undocumented knowledge that might otherwise be lost.
DISADVANTAGES :-
They must be kept up to date as the condition changes.
Result are very dependent on the correctness of the knowledge incorporated into expert systems.
They do not benefit from experience except through updating of the knowledge base(base on human experience).
ARTIFICIAL NEURAL NETWORKS(ANN)
ANNs are large scale parallel distributed processing, nonlinear dynamic systems. ANNs also exhibit a surprising number of the human brain characteristics. Neural networks are built of many simple nodes, called neurons, which are distributed.
These nodes are arranged in layers or slabs and are often connected to many nodes in other layers. A ‘layer’ is set of nodes whose weights are actively manipulated, and serves as a buffer between input or output or other layers.
A slab is a set of nodes that may be different in terms of internal specification or connectivity but which share the same later. A single layer may consist of multiple slabs. Each node processes the input it receives via these connections and provides a continuous analog value to other processing elements via its outgoing connections.