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This paper presents a comparison of the two important inference schemes: "individual-rule-based inference" and "compositional rule of inference" as applied to fuzzy logic control, through experimental investigation. The techniques are implemented on a hydraulic manipulator of an industrial machine with P-type fuzzy control. The fuzzy logic controller is designed for automatic positioning of the cutter blade of an automated fish-cutting machine. The features of the machine, which uses hydraulic servo control for cutter positioning, are outlined. The performance of the machine under the two inference schemes is examined and contrasted. Some practical implementations of the results are indicated.

Proper control of the wood-drying kiln is crucial in ensuring satisfactory quality of dried wood and in minimizing drying time. This paper presents the development, implementation, and evaluation of a control system for a lumber drying kiln process incorporating sensory feedback from in-wood moisture content sensors and intelligent control such that the moisture content of lumber will reach and stabilize at the desired set point without operator interference. The drying process is difficult to model and control due to complex dynamic nonlinearities, coupling effects among key variables, and process disturbances caused by the variation of lumber sizes, species, and environmental factors. Through system identification scheme using experimental data and recursive least-squares algorithm for parameter estimation, appropriate models are developed for simulation purpose and controller design. Two different control methodologies are employed and compared: a conventional proportional-integral-derivative (PID) controller and a direct fuzzy logic controller (FLC), and system performance is evaluated through simulations. The developed control system is then implemented in a downscaled industrial kiln located at the Innovation Centre of National Research Council (NRC) of Canada. This experimental set-up is equipped with a variety of sensors, including thermocouples for temperature feedback, an air velocity transmitter for measuring airflow speed in the plenum, relative humidity sensors for measuring the relative humidity inside the kiln, and in-wood moisture content sensors for measuring the moisture content of the wood pieces. For comparison, extensive experimental studies are carried out on-line using the two controllers, and the results are evaluated to tune the controller parameters to achieve good performance in the wood-drying kiln. The combination of conventional control with the intelligent control promises improved performance. The control system developed in this study may be applied in industrial wood-drying kilns, with a clear potential for improved quality and increased speed of drying. Copyright © 2001 John Wiley & Sons, Ltd.