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The Short Sampling Period in Adaptive Control

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. The new approach to analysis of on-line identification methods based on one-step-ahead prediction clears up their sensitivity to disturbances in control loop and explain why should be neural network based identification better then classical by using of short sampling period. 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. We will in this paper show why neural networks based identification enables using of shorter sampling period in adaptive control process and therefore faster disturbance rejection.