An update to experimental models for validating computer technology
Through several case study problems from industrial and scientific research laboratory applications, Mathematical and Experimental Modeling of Physical and Biological Processes provides students with a fundamental understanding of how mathematics is applied to problems in science and engineering.
For each case study problem, the authors discuss why a model is needed and what goals can be achieved with the model.
Both conventional and fuzzy-logic proportional-integral-derivative control are developed. The resulting heat control action which is pulse-width-modulated (PWM) is used to control the heater on duration within each sampling time interval.
In order to achieve the research goals, this investigation is divided into two parts. Typical simulation results of the PID control system are presented in the next section.
The second part presents the controller design, development, and implementation. The control system is aimed at reducing the moisture content by 3% and 5%, in the cases shown in Figures 5-3 and 5-4, respectively.Thus, we replace static claims on the impossibility of validating a given model by a dynamic process of constructive approximation.This approach is better adapted to the fuzzy, coarse-grained nature of validation.Both simulation studies and experimental studies on a prototype wood-drying kiln are carried out. 5.2.1 PID simulation results Both the moisture response and the moisture error are computed during the entire drying simulation.System modeling is performed by system identification scheme using experimental data and recursive least squares algorithm for parameter estimation. The M-PID output which depends on the error between the moisture setpoint and the actual moisture response, is computed and has a physical meaning of the desired temperature.