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The subject of the research is measuring systems and primary measuring transducers operating in a dynamic mode. It is aimed at the reduction of a dynamic measurements error due to the primary measuring transducer inertia and random high-frequency noise at the output. Three general approaches to the dynamic measurements error correction based on the dynamic measuring system structure with a dynamic model of the primary measuring transducer (sensor) are considered. The first approach implements the adaptive control of measuring system parameters method, the second one is based on the method of sliding mode control in the dynamic measuring systems, and the third one uses the neural network control method. The approaches mentioned above are based on the principles of modern and classical theory of automatic control and provide the dynamic measurements error correction by the sensor input signal recovery. In the classical problem statement this requires solution to the convolution integral equation and usage of the inverse Fourier transformation.

General Approaches to Dynamic Measurements Error Correction Based on the Sensor Model