DESCRIPTION

Technical Description

Fuzzy methods are particularly suitable for systems that mathematics cannot describe adequately or easily. Fuzzy algorithms can offer major advantages, as the control strategy is developed not on the basis of exact mathematical modelling, but on a linguistic description of the process. Additional input variables and the rule base can be easily added.

This experimental unit forms part of a series of teaching systems developed in collaboration with the Department of Automation and Information Technology at the Harz University of Applied Studies and Research.

The RT 121 provides an introduction to fast, digital real-time control by fuzzy methods. A ball-beam model acts as a mechanical single-variable system. A fuzzy control is used to attempt to hold the ball in a specific position by tilting the beam, even when the position of the ball is modified by external influences.

The position of the ball is determined using a resistive measuring system. A potentiometer detects the inclination of the beam. These sensors supply crisp signals to the fuzzy controller, where the signals are transformed into fuzzy input values and inferenced before being transformed back into a crisp output value. A servo motor equipped with a drive rod modifies the inclination of the beam and acts as an actuator.

The control algorithms are initially written in the user-friendly development software FSH-Shell and then compiled to generate microcontroller code. The control strategy can be optimised at a later date.
A joystick can be used to control the system manually. This allows the degree of difficulty of the control process to be estimated very accurately. 

The well-structured instructional material sets out the fundamentals and provides a step-by-step guide through the experiments.

Learning Objectives / Experiments

- introduction to the fundamentals of fuzzy control and microcontroller technology
- working with the development software FSH-Shell 
- development of a simple fuzzy control for a single-variable system using the elements
  * fuzzification, rule base, inference, defuzzification
- implementation of fuzzy algorithms in the mechatronic system using microcontrollers 
- optimising the algorithms on the mechatronic system using the online debugger