Problems and progress in intelligent systems control

Research in intelligent control (IC) methods has enhanced the capability and flexibility of controllers at servo levels, at process design and at process management and coordination levels. The methods utilised employ techniques such as neural networks and fuzzy logic which interact and reason about the process and its environment by iterative learning. This paper reviews the requirements, progress and problems of IC for real time complex large scale plant such as chemical and aerospace processes, for air traffic and intelligent vehicle control systems. A review of learning systems, neurofuzzy algorithms, and intelligent command and control systems developments is made