Intelligent control systems

An intelligent control system, as considered by Johnson and Picton (1995), comprises of a number of subsystems as shown in Figure 10.1. 

Fig. 10.1 Intelligent control system structure (adapted from Johnson and Picton).

Household appliances make up one of the largest markets for electrotechnical and electronic products. While comparatively simple versions of sensors and microsystem products, such as temperature sensors or level sensors, have long been used in household appliances, new and improved sensors conquer the market at a breathtaking rate. The way modern sensors with intelligent control systems are used is one of the main distinguishing features between the various products and companies. 

Chapter 11 presents models used to study the continual interaction of the respiratory mechanical system and pulmonary gas exchange. Traditionally, the respiratory system is described as a chemo-stat — ventilation increases with increased chemical stimulation. Alternatively, the author proposes that quantitative description of the respiratory central pattern generator, a network of neuronal clusters in the brain, is a much more sophisticated and realistic approach. Study of this dynamic, optimized controller of a nonlinear plant is interesting from both physiological and engineering perspectives, the latter due to applications of these techniques for new, intelligent control system design. 

The process operators control strategy is based on anticipation, knowledge (know-how), and experience, and can be considered as a set of heuristic decision rules or rules of thumb. In order to recognize the human s basic elements and to include the operator s control actions in an automatic scheme, a good user-system interface is required. A useful way to combine this user-system interface with process control is to use intelligent control systems. 

Three approaches dominate the real-time intelligent control field (1) expert systems, (2) neural net controllers (neurocontrollers), and (3) fuzzy logic controllers [50]. These intelligent control systems are based on two types of information processing symbolic and subsymbolic processing [15,51]. 

Shahdi and Bagheri Shouraki (2003) used of ALM for design of an intelligent control system in an automated vehicle. The problem was the control of a truck which is moving at the back of another truck in one line. The automated truck doesn t contact to another truck The ALM has been used to extract driver s behavior and control rules for control system. Also, the effective parameters of controller were derived using ALM. 

From Chapter VII readers may form an idea about what a safe PLC is and why it is necessary. In this clause short discussions will be presented on the internals of a safe PLC. It is needless to mention that there may be quite different configurations of safe PLCs from different manufacturers. Naturally, it is impossible to cover all these configurations within the limited space. Therefore discussions will be based on one internationally popular PLC in a generalized maimer to understand the fundamentals only (so that further discussions on logic solvers in Chapter IX will be easier). For details of PLCs and intelligent control systems readers may refer to [1]. 

---