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In recent years, intelligent control has emerged as one of the most active and fruitful areas of research and development. Intelligent systems are usually described by analogies with biological systems by, for example, looking at how human beings perform control tasks, recognize patterns, or make decisions. Such area is a fusion of systems and control, computer science and operations research. Intelligent control systems are typically able to perform one or more of the following functions: planning actions at different levels, learning from past experience, identifying changes against the system behavior, such as performance degradation, failures, cross-coupling and then reacting appropriately. The field of intelligent control has been applied to modern industrial systems, which are under dominance by diverse technical spheres of knowledge, specially containing mechanical, electrical, hydraulic, control system and drive train devices, where large models are required. To keep up the driving technology, engineers need to build systems orders of magnitude more complex than previous ones and deploy them faster. Therefore, intelligent control techniques are important for dealing with complex systems under such a new paradigm. This paper will focus on neural networks and fuzzy logic applications into the design of control systems.

2. TECHNIQUES FOR INTELLIGENT CONTROL
The area of Intelligent Control is a fusion of a number of research areas in Systems and Control, Computer Science, and Operations Research among others, coming together, merging and expanding in new directions and opening new horizons to address the problems of this challenging and promising area. Intelligent control systems are typically able to perform one or more of the following functions to achieve autonomous behavior: planning actions at different levels of detail, emulation of human expert behavior, learning from past experiences, integrating sensor information, identifying changes that threaten the system behavior, such as failures, and reacting appropriately. This identifies the areas of Planning and Expert Systems, Fuzzy Systems, Neural Networks, Machine Learning, Multi-sensor Integration, Failure Diagnosis, and Reconfigurable Control, to mention but a few, as existing research areas that are related and important to Intelligent Control.
While these techniques provide several key approaches to Intelligent Control, for complex systems they are often interconnected to operate within an architecture which is hierarchical and often distributed. It is for this reason that the areas of hierarchical intelligent control, distributed intelligent control, and architectures for intelligent systems are of significant importance in the design and construction of the overall intelligent controller for complex dynamical systems.

Finally, it is of fundamental importance to recognize that (i) intelligent controllers are nonlinear (possibly hierarchical and distributed) controllers that are constructed in non conventional ways, and (ii) intelligent controllers are often designed to operate in "critical environments" where, for example, the safety of a crew (e.g., in an aircraft/spacecraft), or environmental issues are of concern (e.g., from nuclear power plants or process control). Hence, it is both possible, and of significant importance to introduce mathematical modeling and analysis techniques to be used in the verification and certification of the behavior of intelligent control systems.