In this paper, those parameters that can characterize design procedures for a Fuzzy Logic Controller (FLC) are defined, and a novel design method using the characteristic parameters is proposed. The design parameters of an FLC are a fuzzifier and defuzzifier, decision-making logic, and a database and rule base. Among these design parameters, the database design represented by membership functions, and the rule base represented by the rule table are considered. To define the characteristic parameters, several common senses that appear in general design procedures of the database and the rule base were carried out. As a result, three characteristic parameters for the database and two characteristic parameters for the rule base were obtained. It was thus proved that design procedures for an FLC can be greatly simplified by using cleverly defined characteristic parameters. The applicability of these results is illustrated.
Electro-hydraulic servo systems have been frequently used in the fin position servo system of a missile because of their high power and good positioning capabilities. The objective of this paper is to realize a fuzzy logic controller using genetic algorithm for the position control of an electro-hydraulic fin servo system. In this paper, a design method of a fuzzy logic controller using genetic algorithm is proposed, and it is applied to an electro-hydraulic fin servo position system. To simplify the design of a fuzzy logic controller, we introduce characteristic parameters that include the design parameters of a fuzzy logic controller. Then the characteristic parameters are encoded to a chromosome that has been presented as an integer string, and are optimized through genetic operations. The effectiveness of this control scheme is verified by comparison with PID control through a series of simulation studies.
An on-line measurement of the workpiece hardening depth in laser surface hardening processes is very much difficult to achieve, since the hardening process occurs in depth wise direction. In this paper, the hardening depth is estimated using a multilayered neural network. Input data of the neural network are the surface temperatures at arbitrary chosen five surface points, laser power and traveling speed of laser beam torch. To simulate the actual hardening process, a finite difference method(FDM) is used to model the process. Since this model yields the calculation results of the temperature distribution around the workpiece volume in the vicinity of the laser torch, this model is used to obtain the network`s training data and later to evaluate the performance of the neural network estimator. The simulation results shows that the proposed scheme can be used to estimate the hardening depth with reasonable accuracy.
The strip casting process characterized to produce steel strips of thickness ranging 1~5 mm directly from molten metal has been drawing increasing interests because it skips over some of the conventional hot rolling processes. However, since there are a number of process parameters and the relationship between the parameters is very complex, realizations of the process design and the quality control are extremely difficult. To overcome these difficulties, a new design technique of a fuzzy logic controller is proposed that greatly simplified the design procedure by defining several characteristic parameters associated with the controller. In the design procedure, the major design parameters of the controller are characterized by identifying several common aspects that appear in general design procedures of the database and the rule base. As a result, three characteristic parameters for the database and two characteristic parameters were obtained. From a series of simulation results, we found that the proposed design technique can be effectively used to design of a fuzzy logic controller for the strip casting process.