Approximate Method for Synthesizing Continuous Joint Estimation and Control Systems Via the State-Dependent Riccati Equation Technique

 
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Abstract

The problem of approximate synthesis of a closed loop nonlinear continuous system of joint estimation and control is considered. An approach is used based on the application of the idea of the separation theorem for linear dynamical systems. Using the factorization operation, a nonlinear system is transformed into a structure similar to a linear system, and algorithms for synthesizing an optimal linear controller and state observer are applied to the transformed system, a feature of which is the dependence of the matrices included in the corresponding Riccati equations on the state vector. An example of the synthesis of a state observer and a controller is given, demonstrating the application of the proposed algorithm.

General Information

Keywords: optimal linear controller, state observer, separation principle, state-dependent Riccati equation, nonlinear dynamical system

Journal rubric: Optimization Methods

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OpenAlex trends: Control Systems and Identification, Advanced Control Systems Optimization, Fault Detection and Control Systems

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Control Systems and Identification

This cluster of papers covers advances in system identification techniques, including parameter estimation for nonlinear models, data-driven control, model-based control, feedback controllers, state estimation for multivariable systems, and the development of recursive algorithms. The papers also discuss model selection approaches and optimal experiment design for system identification.

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Advanced Control Systems Optimization

This cluster of papers focuses on the application of Model Predictive Control (MPC) in industrial processes, covering topics such as optimization, robust control, stability analysis, and real-time implementation. It explores the use of MPC for handling nonlinear systems, distributed control strategies, and its application in various industrial settings.

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Fault Detection and Control Systems

This cluster of papers focuses on the application of various data-driven and statistical techniques for process fault detection and diagnosis in industrial settings. It covers topics such as process monitoring, fault isolation, soft sensors, model-based diagnosis, and the use of machine learning in analyzing and improving industrial processes.

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Article type: scientific article

DOI: https://doi.org/10.17759/mda.2024140303

Received 25.05.2024

Accepted

Published

For citation: Panteleev, A.V., Khvoshnyanskaya, E.A. (2024). Approximate Method for Synthesizing Continuous Joint Estimation and Control Systems Via the State-Dependent Riccati Equation Technique. Modelling and Data Analysis, 14(3), 41–62. (In Russ.). https://doi.org/10.17759/mda.2024140303

© Panteleev A.V., Khvoshnyanskaya E.A., 2024

License: CC BY-NC 4.0

References

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Information About the Authors

Andrey V. Panteleev, Doctor of Physics and Matematics, Professor, Professor, Head of the Department of Mathematical Cybernetics, Institute of Information Technologies and Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-2493-3617, e-mail: avpanteleev@inbox.ru

Elizaveta A. Khvoshnyanskaya, Postgraduate student, Assistant, Department of Mathematical Cybernetics, Institute of Computer Science and Applied Mathematics, Moscow Aviation Institute (National Research University) (MAI), Moscow, Russian Federation, ORCID: https://orcid.org/0009-0009-6901-8720, e-mail: liza190401@mail.ru

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