Model prediction control algorithm based on extended-state Kalman filter

Abstract

The invention discloses a model prediction control algorithm based on an extended-state Kalman filter. The algorithm comprises the following steps: (1), integrating nonlinearity and uncertainty of a system and external disturbance into a new state quantity, extending a state space model of the original system, and designing a state quantity and a lumped disturbance amount of an extended-state Kalman filter observation system; and (2), on the basis of a known state quantity and disturbance quantity, designing a model prediction controller by considering the input, output and state constraints of the system. According to the invention, a model prediction control algorithm based on an extended-state Kalman filter is provided, thereby solving problems of noises in the system process, noise measurement, input and output constraints, improving the observation performance of the observer with the noise existence, and improving the control performance.

Description

technical field [0001] The invention relates to the technical field of industrial process control, in particular to a model predictive control algorithm based on an extended state Kalman filter. Background technique [0002] Extended state observer (Extended state observer, ESO) expands the internal and external lumped disturbances in the system into a new first-order state of the system, selects appropriate observer parameters, and obtains the observed values ​​of all state quantities of the system including the lumped disturbance. Because of its obvious advantages in dealing with uncertain problems such as unknown system parameters, unmodeled dynamics, and unknown disturbances, it has gradually attracted extensive attention from researchers and has been successfully applied to various systems. Existing studies have shown that the observation performance of ESO is directly related to the bandwidth of the observer. The larger the bandwidth, the higher the observation accurac...

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Problems solved by technology

Existing studies have shown that the observation performance of ESO is directly related to the bandwidth of the observer. The larger the bandwidth, the higher the observation accuracy of the state quantity, but at the same time, it is more sensitive to noise.

However, the existing extended state observers are not designed considering the process noise and measurement noise in the system, and these noises are widespread in reality, which will affect the performance of the observer

In addition, the gain of the general linear extended state observer is generally adjusted by the bandwidth method, and decoupling design is required when dealing with multivariable systems, which is more complicated

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