As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely...As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.展开更多
This paper is concerned with the control design and the theoretical analysis for a class of input time-delay systems with stable, critical stable or unstable poles. In order to overcome the time delay, a novel feed-fo...This paper is concerned with the control design and the theoretical analysis for a class of input time-delay systems with stable, critical stable or unstable poles. In order to overcome the time delay, a novel feed-forward compensation active disturbance rejection control(FFC-ADRC) approach is proposed. It combines advantages of the Smith predictor and the traditional active disturbance rejection control(ADRC). The tracking differentiator(TD) is designed to predict the control signal, which adds an anticipatory control to the control signal and allows a higher observer bandwidth to obtain better disturbance rejection. The modified extended state observer(ESO) is designed to estimate both system states and the total disturbances(internal disturbance, uncertainties and delayed disturbance). Then the Lyapunov theory and the theory of the input-output stability are applied to prove the asymptotic stability of the closed-loop control system. Finally, numerical simulations show the effectiveness and practicality of the proposed design.展开更多
Not so much had been talked about equilibrium in control area. On the basis of the phenomenon of balance, the concept of control-equilibrium and control-equilibrium of a control system is proposed. According to this t...Not so much had been talked about equilibrium in control area. On the basis of the phenomenon of balance, the concept of control-equilibrium and control-equilibrium of a control system is proposed. According to this theory, a perfect control method should not only guarantee stability of the system, but also ensure the control-equilibrium of the system. To achieve the control-equilibrium, feed-forward control is required.展开更多
基金supported by the National Natural Science Foundation of China(62375013).
文摘As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.
基金supported by the National Natural Science Foundation of China(61304026)
文摘This paper is concerned with the control design and the theoretical analysis for a class of input time-delay systems with stable, critical stable or unstable poles. In order to overcome the time delay, a novel feed-forward compensation active disturbance rejection control(FFC-ADRC) approach is proposed. It combines advantages of the Smith predictor and the traditional active disturbance rejection control(ADRC). The tracking differentiator(TD) is designed to predict the control signal, which adds an anticipatory control to the control signal and allows a higher observer bandwidth to obtain better disturbance rejection. The modified extended state observer(ESO) is designed to estimate both system states and the total disturbances(internal disturbance, uncertainties and delayed disturbance). Then the Lyapunov theory and the theory of the input-output stability are applied to prove the asymptotic stability of the closed-loop control system. Finally, numerical simulations show the effectiveness and practicality of the proposed design.
文摘Not so much had been talked about equilibrium in control area. On the basis of the phenomenon of balance, the concept of control-equilibrium and control-equilibrium of a control system is proposed. According to this theory, a perfect control method should not only guarantee stability of the system, but also ensure the control-equilibrium of the system. To achieve the control-equilibrium, feed-forward control is required.
