Neural Network Identifi cation and Tuning of Mechatronic Systems with State Controllers

The article deals with the problem of identification and tuning of mechatronic systems with state controllers using artificial neural networks (ANN) in conditions of wideband interference in measurement channels by optimizing the structure and parameters of a neural network identifier. The method based on application of radial ANN is proposed to reduce the tuning time of systems with state controllers. Discrete values of dynamics characteristics of system state coordinates are connected to the identifier inputs, according to which the ANN estimates of the variable parameters of the controlled object. Based on the estimates obtained, the parameters of the state controller are automatically calculated using the modal control method. The optimal composition of measurement channels is selected based on the ratio of information signal and interference powers, where signal is considered as the difference of dynamics characteristics with nominal and changed values of the object parameters. For each variable parameter, a state coordinate is found that gives a maximum value of the criterion, after which the optimal ANN structure is formed, providing a minimum identification error under interference conditions. In case of small variations in the parameters of object, it is recommended to determine the power of information signal using sensitivity functions of state coordinates. With large variations in parameters and different powers of noise in the measurement channels, direct calculation using matrix expressions provides more accurate estimates. The developed training algorithm allows us to determine optimal value of radial basis functions spread, which ensures a given accuracy in identifying the object parameters with minimum number of neurons in the network first layer. The proposed method of systems tuning makes it possible to obtain a given quality of control in conditions of parametric uncertainty of mechatronic object. At the same time, the optimal combination of measurement channels at ANN input according to the proposed criterion provides a minimum value of the identification error in conditions of wideband interference.

1. Anisimov A. A., Tararykin S. V. Structural-parametric synthesis, optimization and tuning of control systems for technological objects, Ivanovo, ISPU, 2015, 296 p. (in Russian).

2. Anisimov A. A., Tararykin S. V. Automatic tuning of polynomial controllers of electromechanical systems using an artificial neural network, Mekhatronika, Avtomatizaciya, Upravlenie, 2008, no. 8, pp. 13—18 (in Russian).

3. Omatu S., Khalid M., Yusof R. Neuro-Control and its Applications. Advances in Industrial Control, London, Springer, 1996, 255 p.

4. Eremenko Yu. I., Poleshchenko D. A., Glushchenko А. I. Analysis of methods for implementing of neural network control scheme with self-tuning, Pribory i sistemy. Upravlenie, kontrol’, diagnostika, 2012, no. 6, pp. 50—55 (in Russian).

5. D’Emilia G., Marra A., Natale E. Use of neural networks for quick and accurate auto-tuning of PID-controller, Robotics and Computer-Integrated Manufacturing,2007, vol. 23, no. 2, pp. 170—179.

6. Osovskiy S. Neural networks for information processing, Moscow, Finansy i statistika, 2002, 344 p. (in Russian).

7. Piegat А. Fuzzy Modeling and Control, Moscow, Binom. Laboratoriya znaniy,2012, 208 p. (in Russian)

8. Paluszek M., Thomas S. MATLAB Machine Learning Recipes: А Problem-Solution Approach, NewYork, Apress Media, 2024, 447 p.

9. Anisimov A. A., Sorokovnin M. E., Tararykin S. V. Improving the accuracy of identification and tuning of linear systems with state controllers using an artificial neural network, Vestnik ISPU, 2023, no. 6, pp. 57—68(in Russian).

10. Kokotovich P. V., Rutman R. S. Sensitivity of automatic control systems, Avtomatika i telemekhanika, 1965, no. 4, pp. 730—750 (in Russian).

11. Kokotovich P. V. The method of sensitivity points in the research and optimization of linear control systems, Avtomatika i telemekhanika, 1964, no. 1, pp. 1670—1676 (in Russian).

12. Basharin A. V., Novikov V. A., Sokolovskiy G. G. Control of electric drives, Leningrad, Energoizdat, 1982, 392 p. (in Russian).