Abstract
High potential capabilities of control systems with state controllers can be realized only if automatic tuning tools are available. Since the tuning is carried out in real-time mode, which places increased demands on performance, it is proposed to use an artificial neural network to reduce its duration. However, under the conditions of noise in the measurement channels, the quality of identification of the parameters of the control object is significantly reduced. In this regard, the aim of the study is to find the optimal composition of measurement channels at the network input, which allows minimizing the influence of noise on the estimates of object parameters to improve the quality of tuning. During the study, state space methods are used to design a vector-matrix model of an object and synthesize a state controller. A radial artificial neural network is used to solve the problem of identifying the parameters of a vector-matrix model. The training of networks, the study of the effectiveness of their work, as well as the development of models is carried out using the tools of the MatLab software package. The authors have developed the method to select the optimal composition of measurement channels which gives the maximum signal-to-noise ratio and forming the corresponding structure of a radial artificial neural network to solve the problems of object parameters identification and control system tuning with state controller. It is proposed to use the sensitivity functions of the state coordinates of control object parameters variation to estimate power of information signals at the inputs of neural network. The results of the conducted computational experiments have confirmed the effectiveness of the developed method, which makes it possible to increase the accuracy of identification and tuning of systems with state regulators under noise conditions. The obtained results can be used to ensure a given quality of control with parametric uncertainty of the object.
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