Peter Galan
Peter Galan is a (retired) control system designer with extensive experience in electronics, control systems and software design. He worked for many companies like ZTS, GE, Husky, Nortel, JDSU, Emerson (in Canada and the U.S.A.) and previously at the Technical University in Kosice (former Czechoslovakia). He holds a Ph.D. degree in Automated Control Systems and M.Eng degree in Applied Cybernetics from Czech Technical University in Prague.
Articles
Tutorial: How to find the best controller
Control system designers consistently seek the best control method for an application. See examples, equations and graphics.
Soft computing in action with an intelligent battery pack
The intelligent battery pack can be made safer by using soft computing techniques to make process variables more reliable and consistent.
PID-correction-based control system implementation
The analog PID controller, still considered as the most powerful, can be modified as a discrete-time control system. Equations and examples follow.
Introduction to artificial neural networks in control applications
Practical applications of artificial neural networks (ANNs) for control systems, especially for non-linear systems, include simulating time-optimal controllers and for ANN-based controlled system (plant) models. Such models, combined with classical proportional-integral-derivative (PID) controllers, can enable adaptive and other, more sophisticated, control systems.
Event-driven applications for embedded systems: Summary of PDF
C code is provided and explained for creating event-driven applications for embedded systems, and simulating a task-manager application.
Finite-state machine for embedded systems
Get help for finite-state machine programming for embedded systems using C programming language.
Control system improvements: Feed-forward, adaptive, fuzzy control
Control methods that can be more effective than proportional-integral-derivative (PID) controllers, include feed-forward control, disturbance compensation, adaptive control, optimal PID control and fuzzy control.
From simulation to computer-aided design of control systems
Cover Story: While simulation systems can help for control system programming design, a general-purpose programming language like C# can be used: First, some basic control system theory.
Temperature control: PID vs. Fuzzy Logic
The common perception is that temperature control is a mature and largely unchanging area of technology. There are still industrial applications (for example, injection molding processes), which desire not only precise temperature control, but also a faster warm-up phase and quicker response to disturbances with minimal overshoot and undershoot when the setpoint changes.