Elpo solution 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.
Intelligent control is a rapidly developing, complex and challenging field with great practical importance and potential.
Because of the rapidly developing and interdisciplinary nature of the subject, there are only a few edited volumes consisting of research papers on intelligent control systems but little is known and published about the fundamentals and the general know-how in designing, implementing and operating intelligent control systems.
Intelligent control system emerged from artificial intelligence and computer controlled systems as an interdisciplinary field.
Therefore Elpo summarizes the fundamentals of knowledge representation, reasoning, expert systems and real-time control systems and then discusses the design, implementation verification and operation of real-time expert systems using G2 as an example. Special tools and techniques applied in intelligent control are also described including qualitative modelling, Petri nets and fuzzy controllers.
The material is illlustrated with simple examples taken from the field of intelligent process control.
Artificial intelligence (AI) as a field of research and development emerged and developed in parallel with the
development of the theory of automatic control, starting around 50-th years, with the first major applications in
computing and information science, and later in automatic control1
. The first commercial and industrial applications
of AI belong to the 80-th years of the last century2
. During this period, AI has reached some level of stability and
An important factor that can lead to a rethinking of today's achievements and make new ups of the theory and
practice of AI is the sharp increase in possibilities of computer technology, including hardware implementation of
logical and other means of AI.
The term "intellectual control system" refers to any combination of hardware and software, which is joined by
general information process, operating autonomously or in man-machine mode, and capable to synthesize the
control goal and to find rational ways to achieve the control goal (in the presence of motivation and knowledge
including information about the environment and its internal status)1,3. Today the capacity to synthesize the control
goal is realised by human-machine interaction, and the autonomous control systems capable only to find rational
ways to achieve the control goal are called as "intelligent control systems".
Currently, the science and practice of control retains a keen interest in the integration of classical methods of
automatic control with methods of AI and in AI applications in the field of control for complex weakly-formalized
objects and processes. In particular, when the information, system status, control criteria, and control goals change
over time and are fuzzy and sometimes contradictory.
The report considers a hierarchy of levels of intellectual control and a comparative analysis of different means of
AI. Due to the fact that the past decade has seen a rapid increase in the number of theoretical and applied research
in the field of fuzzy controllers, the main focus of the report is to review the major achievements in this area.
Though, unfortunately, even this field doesn't allow to make a complete review free from the authors’ predilections.