Safety Licensable and High Speed Programmable Digital Controllers Providing any Required Control Behaviour

  • Peter Vogrin
  • Wolfgang A. Halang
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 76)


The design principle of control algorithms working with set-point pre-processors, called SPP controllers, is described. Set-point pre-processors calculate internal set-point graphs of controlled variables in such a way that very high controller gains are attainable and, thus, stability is increased. The behaviour of SPP controllers is normally much closer to the “best physically possible” controller performance, and much more predictable than the one of conventional control structures. Furthermore, otherwise conflicting design objectives can nearly all be achieved, e.g., stability, safety, high speed, small energy consumption, or steady and harmonic temporal controller output values. Since SPP control algorithms allow for extremely fast reactions, their implementation in form of sequential software could be counter-productive by ruining the speed advantages. This problem was solved by approximating the algorithms by rule base tables allowing to implement them on safety licensable inference controllers, which are characterised by fuzzification with analogue-to-digital converters and inference by look-up in tables containing fuzzy rule sets. An inference controller consists of a few elements, only. Thus, it is reliable, safe, verifiable, cheap and small. Owing to the simplicity of both its hardware and software, safety licensing of the controller is facilitated. With regard to software, this can easily be carried out by inspecting the table content. The controller is very fast, with its speed mainly determined by the EPROM access time, and works almost jitter-free. Operating in a strictly cyclic fashion, the controller exhibits fully predictable real time behaviour. Its hardware operation is supervised by a fail-safe logic immediately initiating an emergency shut-down in case of a malfunction.


Fuzzy Controller Inference Engine Dynamic Controller Conventional Controller Temporal Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Physica-Verlag Heidelberg 2001

Authors and Affiliations

  • Peter Vogrin
    • 1
  • Wolfgang A. Halang
    • 1
  1. 1.Faculty of Electrical EngineeringFernUniversitätHagenGermany

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