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.