Influence of Random Error of Temperature Sensors on the Quality of Temperature Compensation of Fog Bias by the Neural Network

The formulas are obtained for estimating the «random walk» type noise of algorithmic compensation for the gyro bias. An example of estimating the statistical significance of the factors influencing the bias when calibrating a fiber-optic gyroscope in the operating temperature range and at different rates of their change is given. It is shown that the random error of temperature sensors can play a major role in the “random walk” noise of the algorithmic compensation for the gyro bias and exceed the gyro self noise. An example of obtaining a regression dependence of algorithmic compensation for gyro bias using a neural network with a multilayer perceptron is given. The factors influencing the choice of the time constant of the differentiating low-frequency temperature filter are considered. Experimental dependences of the random error of the bias algorithmic compensation on the value of the random error of temperature sensors are presented and the necessity of using temperature sensors with a minimum random error is shown.

DRIVE UNIT MATHEMATICAL MODEL OF ROBOT GRIPPING DEVICES

Today, the task of increasing the drive unit technical characteristics of robot gripping devices is relevant. In many respects, the drive motor and gear unit determine the quality of work of such modules, which can introduce additional elasticity, backlash and friction into the system. In turn, backlash and friction in the control loop decrease the speed and accuracy of the mechatronic module and can cause self-oscillations. However, significant progress in hardware performance of mechatronic and robotic systems makes it possible to more accurately estimate and control the force and torque state parameters in the output link and to carry out active algorithmic compensation for the effects of friction and elasticity. Such observers are usually based on detailed mathematical models and allows to implement force control without the use of special sensors, which is especially important in advanced gripping devices due to the requirements imposed on the mass-dimensional parameters of the product. Therefore, the purpose of this article is to make a general mathematical model of the robot drive unit with a rational relationship of complexity and accuracy. The models’ parameters should be easily identified from the constituent elements of passport data or the experiment results. The model should allow estimating the state parameters of mechanical transmission in real time; take into account the main friction effects. However, it should be simple enough for analytical conclusion and numerical modeling. As part of the article, various friction models are considered. On the basis of this analysis, a modified friction model is proposed. It makes possible to estimate the state parameters of mechanical transmission in real time. It is built into the general mathematical model of the mechatronic module. The results of mathematical simulation are close to experimental data. The considered drive unit mathematical model can be used to identify the state parameters of the mechatronic module in real time and implement force control based on estimation. In addition, the obtained model allows to conduct mathematical simulation of the robot taking into account the drive unit dynamics.

Modeling of temperature dependency of structural waves in an ultrasonic flow measurement system

Structural waves transmitted solely through the pipe wall influence the accuracy in a clamp-on ultrasonic flow measurement system because of the superposition with the signals of interest. To improve the measurement against temperature variations, an algorithmic compensation of the structural waves using a temperature model is required. This paper proposes a temperature model for structural waves, using the Matching Pursuit method. In the first section, a sparse signal representation is presented to approximate the structural wave signals. The resulting signal coefficients are used to describe the temperature dependency in a linear model. The method is validated using measurements of structural waves in a circular pipe over a temperature range between 20\hspace{0.1667em}^\circ \text{C} and 80\hspace{0.1667em}^\circ \text{C}. Based on these measurements, the accuracy of the approximated temperature model is evaluated and compared against the baseline signal-stretch method.