A CONTROL SYSTEM ON A CONTROLLED OBJECT STATE WITH AN OBSERVER AND A STATE REGULATOR-Reference-Cited by-同舟云学术

A CONTROL SYSTEM ON A CONTROLLED OBJECT STATE WITH AN OBSERVER AND A STATE REGULATOR

Published:2023-01-16 Issue:1 Volume:8 Page:102-120
ISSN:2071-7318
Container-title:Bulletin of Belgorod State Technological University named after. V. G. Shukhov
language:en
Short-container-title:

Author:

Bezmen P.¹

Affiliation:

1. Southwest State University

Abstract

The paper proposes the model of a control system based on the controlled object state. The model includes a state observer and a state controller. The reference signal for this control system is the required values of the controlled object state variables. As a state observer of a controlled object in the control system model, the system "extended Kalman filter - adaptive digital filter" (the EKF-ADF system) is used. The structure and operation principle of the control system state controller are described. The adaptive algorithm of the control system state controller is presented. The control system state controller with the adaptive algorithm uses the output data of the EKF-ADF system to form the controller output (error) vector. The output data consist of the state estimation vector performed by the EKF of the EKF-ADF system and the vector of the corrected state estimation performed by the ADF of the EKF-ADF system. The adaptive algorithm of the control system state controller takes into account the output data of the EKF-ADF system in such a way as to form the most reliable state controller output vector. To confirm the effectiveness of the considered control system, the control process numerical simulation results of a mobile robot with a caterpillar mover are presented: the proposed control system simulation results are compared with the simulation results of the control system that uses the EKF as a state observer. The combination of the state observer and the state regulator as part of the proposed control system makes it possible to control dynamic objects with state variables which are inaccessible to direct measurement and have non-periodic external disturbances.

Publisher

BSTU named after V.G. Shukhov

Subject

Psychiatry and Mental health,Neuropsychology and Physiological Psychology

Reference22 articles.

1. Kalman R.E. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME (American Society of Mechanical Engineers) // Journal of Basic Engineering. 1960. Vol. 82 (1). Pp. 35–45. DOI: 10.1115/1.3662552., Kalman R.E. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME (American Society of Mechanical Engineers), Journal of Basic Engineering. 1960. Vol. 82 (1). Pp. 35–45. DOI: 10.1115/1.3662552.

2. Kalman R.E., Busy R.S. New Results in Linear Filtering and Prediction Theory. Transactions of the ASME (American Society of Mechanical Engineers) // Journal of Basic Engineering. 1961. Vol. 83 (1). Pp. 95–108. DOI: 10.1115/1.3658902., Kalman R.E., Busy R.S. New Results in Linear Filtering and Prediction Theory. Transactions of the ASME (American Society of Mechanical Engineers), Journal of Basic Engineering. 1961. Vol. 83 (1). Pp. 95–108. DOI: 10.1115/1.3658902.

3. Bar-Shalom Y., Li X.R., Kirubarajan T. Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. John Wiley & Sons, 2001. 584 p., Bar-Shalom Y., Li X.R., Kirubarajan T. Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. John Wiley & Sons, 2001. 584 p.

4. Певзнер Л.Д. Теория систем управления. Москва: Издательство Московского государственного горного университета, 2002. 472 с., Pevzner L.D. Theory of control systems [Teoriya sistem upravleniya]. Moscow: Publishing house of the Moscow State Mining University, 2002. 472 p. (rus.).

5. Gyorgy K. The LQG Control Algorithms for Nonlinear Dynamic Systems. Procedia Manufacturing. 2019. Vol. 32. Pp. 553–563.DOI: 10.1016/j.promfg.2019.02.252., Gyorgy K. The LQG Control Algorithms for Nonlinear Dynamic Systems. Procedia Manufacturing. Vol. 32. 2019. Pp. 553–563. DOI: 10.1016/j.promfg.2019.02.252.