From Fuzzy Models to Fuzzy Control

Overall intelligent control system which runs on fuzzy, genetic and neural algorithm is a promising engine for large –scale development of control systems . Its development relies on creating environments where anthropomorphic tasks can be performed autonomously or proactively with a human operator. Certainly, the ability to control processes with a degree of autonomy is depended on the quality of an intelligent control system envisioned. In this paper, a summary of published techniques for intelligent fuzzy control system is presented to enable a design engineer choose architecture for his particular purpose. Published concepts are grouped according to their functionality. Their respective performances are compared. The various fuzzy techniques are analyzed in terms of their complexity, efficiency, flexibility, start-up behavior and utilization of the controller with reference to an optimum control system condition.

Proceedings of IEEE 5th International Fuzzy Systems

System modelling is an important subject in engineering, both from a research point of view and from a practical perspective. Models have an important role in several intelligent systems and, as it is observed from the research developed in the last years, fuzzy logic is relevant to this topic. This is mainly due to the expressiveness of fuzzy logic that permits the treatment of some kinds of uncertainty largely present in real systems. Also, the if-then rule mechanism is easy to manipulate and, in a certain extent, domain independent. A modelling procedure must produce good models in an effective way. This is the main concern of this paper. 1. No special assumption is made about the system, except that it must be causal.

2009

Fuzzy PI controllers are using in the place of linear PI controller with success in process control, assuring better control quality criteria. The paper presents some methods, with a higher grade of generality, to design PI fuzzy controllers, to assure internal and external stability to control systems and better control quality criteria. Families of input-output transfer characteristics and gain characteristics of fuzzy blocks are used in the design procedures. An equivalence of PI fuzzy controller with linear PID controllers is done in a first approximation. Then, the fuzzy controller is design to assure absolute global internal stability and external BIBO stability of control systems. An application is presented with modeling and simulations. Results of simulation demonstrate the advantages of these design methods, assuring better quality control criteria and robustness at error in parameter estimation and at disturbance influence for the control systems.

Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, 1994

The standard computation taking place in a fuzzy logic controller proceeds from crisp inputs and via the consecutive steps of fuzzification, inference, and defuzzification computes a crisp control output. However, this computational practice simplifies to an extent the actual developments taking place in the closed loop. In reality, the knowledge about the current values of the controller input is very often available via sensory measurements. In this case, one has to take into account the negative side effects that come up with the use of sensors, in particular the presence of noisy measurements.