Abstract
The paper proposes a method for calculating the probabilistic characteristics of signals for a wide class of nonlinear torque transducers. The presented approach is based on modeling the process of processing the input signal, taking into account the multiplicative error of the transducer. A methodology has been developed for estimating the probability density function and numerical characteristics of the output signal of a nonlinear torque transducer for different distribution laws of the input signal and types of nonlinear transformation functions. To achieve this goal, the following steps were performed: algorithms and computer programs were developed for numerical simulation and calculation of the characteristics of signals at the output of a nonlinear transducer for given input parameters, a series of computational experiments were conducted for different combinations of distribution laws of input signals and nonlinear transformation functions, the influence of different distribution laws of the input signal on the probability density function and numerical characteristics of the output signal was investigated, the influence of different types of nonlinear transformation functions on the probability density function and numerical characteristics of the output signal was studied, the obtained results were analyzed, and recommendations were formulated for the choice of the nonlinear transformation function and the distribution law of the input signal to achieve the desired characteristics of the output signal of the torque transducer. The paper discusses the features of the proposed algorithm, as well as the simulation results for a number of examples of transducers with different nonlinear characteristics and parameters of multiplicative disturbance. An analysis of the accuracy of the obtained estimates of probabilistic characteristics is carried out. The developed algorithm allows estimating the probabilistic characteristics of the output signals of nonlinear torque transducers at the design stage, which contributes to improving the quality of the development of control systems. This method can be useful for engineers and scientists involved in the design and analysis of automatic control systems with nonlinear elements.
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