The Use of Short Sampling Period in Adaptive Control

The Use of Short Sampling Period in Adaptive Control

Peer-reviewed article not indexed in WoS or Scopus

The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. On one hand faster disturbance rejection due to short sampling period can be an advantage but on the other hand it brings us some practical problems. Particularly, quantization error and finite numerical precision of industrial controller must be considered in the real process control. The quantization effect is more known for example in instrumentation theory or signal processing theory than in control theory. Furthermore, in control theory the phenomenon has been usually disregarded. It is due to the fact that the conditions used in process control allow the quantization effect to be ignored. Nowadays, when the sampling period is demanded to be very short and the requirements for the control precision are higher then before, the quantization effect plays considerable role in the practical control. Presented paper shows a comparison of two real-time identification methods with improved adaptive linear optimal controller.

The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. On one hand faster disturbance rejection due to short sampling period can be an advantage but on the other hand it brings us some practical problems. Particularly, quantization error and finite numerical precision of industrial controller must be considered in the real process control. The quantization effect is more known for example in instrumentation theory or signal processing theory than in control theory. Furthermore, in control theory the phenomenon has been usually disregarded. It is due to the fact that the conditions used in process control allow the quantization effect to be ignored. Nowadays, when the sampling period is demanded to be very short and the requirements for the control precision are higher then before, the quantization effect plays considerable role in the practical control. Presented paper shows a comparison of two real-time identification methods with improved adaptive linear optimal controller.

Adaptive control, A/D converters, Delta models, Identification algorithms, Linear quadratic regulators, Neural networks, Quantization errors, Recursive algorithm, Sampling period

Pivoňka, P., Švancara, K.

01.09.2005

1109-2777

WSEAS Transactions on Systems

10

4

United States of America

1627

10

http://hdl.handle.net/