A predictive control method based on a dual-frequency extended state observer and related equipment

By using a dual-frequency extended state observer and a predictive control method based on Gaussian process regression, the gain coefficient and the predicted disturbance sequence are dynamically adjusted, which solves the problem of multivariable coupling and time-varying disturbance in complex environments by traditional control methods, thereby improving control accuracy and robustness.

CN122131644APending Publication Date: 2026-06-02BEIHANG UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
BEIHANG UNIV
Filing Date
2026-01-28
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Traditional control methods struggle to effectively handle multivariate coupling, time-varying disturbances, and noise effects in complex environments, resulting in insufficient control accuracy and robustness, especially posing safety and stability risks in high-precision control scenarios.

Method used

A predictive control method based on a dual-frequency extended state observer is adopted. By dynamically adjusting the gain coefficient of the dual-frequency extended state observer and the Gaussian process regression predicting the disturbance sequence, an updated prediction model is constructed, and the optimal control sequence is generated under the rolling optimization framework.

Benefits of technology

It improves control accuracy and robustness in environments with strong disturbances and high noise levels, achieves accurate estimation of disturbances and coordination of multiple variables, and enhances the overall control quality and anti-interference capability of the system.

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Abstract

This invention discloses a predictive control method and related equipment based on a dual-frequency extended state observer, belonging to the field of automatic control technology. The method includes: establishing a predictive model corresponding to the controlled object; obtaining the state variables of the controlled object at time k; obtaining the state estimate of the controlled object at time k; calculating the state estimation error at time k; dynamically switching the gain coefficient of the dual-frequency extended state observer to obtain the disturbance estimate at time k; predicting the disturbance sequence for the next N times starting from time k+1; constructing an updated predictive model containing disturbance information; designing and solving a cost function to obtain the optimal control sequence; and extracting the control quantity corresponding to time k+1 from the optimal control sequence to control the controlled object. This invention can coordinate multiple variables and handle constraints, and finally extract and apply the control quantity corresponding to the next time, improving the control accuracy, robustness, and safety under strong disturbances, high noise, and complex constraint environments.
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