Coordinated optimization control of shield machine based on dynamic fuzzy neural network direct inverse control

Liu, Xuanyu; Wang, Wentao; Wang, Yudong; Shao, Cheng; Cong, Qiumei

doi:10.1177/0142331220980274

Cited by 8 publications

(3 citation statements)

References 8 publications

(8 reference statements)

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“…Thus, the time series of construction data is established. A prediction model expression, as shown in equation (11).…”

Section: Constructing Time Seriesmentioning

confidence: 99%

“…Xuanyu Liu [10] proposed an intelligent online prediction of the rotation speed of the Earth pressure balance shield screw machine based on convolutional neural networkgate recurrent unit (CCN-GRU) and controlled the sealed cabin pressure by predicting the screw conveyor speed. In order to further improve the accuracy of the control effect, a dynamic fuzzy neural network (D-FNN) control system is established to output screw conveyor speed and propulsion speed at the next moment, which further improves the control effect on the sealed cabin pressure [11]. Kong Xiangxun [12] proposed a prediction method based on random forest and selected geological conditions and shield operation data as features to establish a prediction model to achieve thrust prediction.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent real-time prediction of multi-region thrust of EPB shield machine based on SSA-LSTM

et al. 2023

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During the tunneling process of shield machine, the accurate determination of tunneling parameters is the guarantee of safe operation of shield machine. Through the real-time construction data analysis and mining of shield machine, the tunneling parameters at the next moment are obtained in real-time. Based on this, this paper proposes an intelligent real-time prediction method for multi-region thrust of EPB shield machine based on SSA-LSTM (Sparrow Search algorithm-Long and Short-term Memory). By correlation analysis of the construction big data, the data features with a great correlation of shield machine thrust are obtained. And it is used as the input of LSTM prediction model to explore the nonlinear relationship between inputs and output. SSA is used to optimize LSTM prediction model to establish a more accurate nonlinear relationship, and then the multi-region thrust of shield machine at the next moment is accurately obtained. The simulation results show that SSA-LSTM model can accurately predict the thrust of each region of shield machine at the next moment, and the prediction performance is better than other models. The method provides a reference basis for shield machine to implement accurate thrust regulation and provides a guarantee for effective control of earth pressure balance (EPB) in sealed cabin to ensure construction safety, which has engineering application value.

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“…Thus, the time series of construction data is established. A prediction model expression, as shown in equation (11).…”

Section: Constructing Time Seriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent real-time prediction of multi-region thrust of EPB shield machine based on SSA-LSTM

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…In view of the strong coupling between channels and highly nonlinear characteristics of UAV, dynamic inverse control is used to cancel the nonlinearity of the controlled object itself [33], which requires that the mathematical model of the aircraft should be accurate. In the actual flight environment, the inverse errors are affected by the disturbance of the external turbulent wind and the uncertainty of the model, which will lead to the inability to completely cancel the nonlinearity.…”

Section: Control System Designmentioning

confidence: 99%

Fuzzy Neural Network Dynamic Inverse Control Strategy for Quadrotor UAV Based on Atmospheric Turbulence

Yang

Cheng

et al. 2022

Applied Sciences

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Quadrotor UAV is vulnerable to external interference, which affects search and rescue. In this paper, a fuzzy neural network dynamic inverse controller (FNN-DIC) is designed to eliminate the instability of the attitude angle caused by atmospheric turbulence. Considering the complexity of atmospheric turbulence, the component model of atmospheric turbulence is obtained firstly based on the Dryden model, using Gaussian white noise as a random input signal and a designed shaping filter. Combined with the Newton-Euler equation, a nonlinear dynamic model for the quadrotor UAV with atmospheric disturbance is established. While the traditional nonlinear dynamic inverse cancels the nonlinearity of the controlled object, it relies on precise mathematical models. The fuzzy neural network can adaptively compensate for the inaccurate part of the model and the inverse error of the model caused by the external disturbance, and the stability of the control system is strictly proved by using the Lyapunov function. The experiments are carried out on the simulation platform, and the results show that the FNN method can ensure that the quadrotor UAV can still fly smoothly against strong disturbances, and that robustness of the system is significantly improved.

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Geological adaptive intelligent control of earth pressure balance shield machine based on deep reinforcement learning

Liu,

Zhang,

Shao

et al. 2024

Artif Intell Rev

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Scientific and precise control of tunnelling parameters is of utmost importance during the construction of shield machines. Given the complexity of the working environment, manual operation is highly prone to causing safety accidents. Therefore, achieving intelligent control of the shield machine is crucial. Based on this, this paper proposes a geological adaptive intelligent control method of earth pressure balance shield machine using the Deep Deterministic Policy Gradient (DDPG) algorithm as the framework, with Actor-Critic as the basis. Firstly, DDPG agent is constructed to replace the screw conveyor control system as the main body of strategy implementation. Secondly, an environmental model is established by utilizing the mechanism model between the sealed cabin pressure and the screw conveyor speed. The real-time sealed cabin pressure, target pressure, and pressure error serve as the state space, while the screw conveyor speed is used as the action space. A combined reward function is set based on safety and accuracy. Finally, the Actor network interacts with the environment under the supervision of the reward function and Critic network. Successful training is achieved when the cumulative reward value is maximized, resulting in the output of optimal control strategy. In this paper, the method dynamically regulates the screw conveyor speed by interacting with the geological environment, to realize the precise control of the sealed cabin pressure and ensure the dynamic balance between sealed cabin pressure and excavation face pressure. The test results show that this method has a good control effect on the sealed cabin pressure under various geological conditions, and can complete 72 kinds of soil transition tasks. It has strong soil adaptability and can respond well to the dynamic changes of soil conditions. This approach enhances the intelligence of the shield machine, mitigating inaccuracies attributed to human operation, which provides a guarantee of safe shield machine operation, whilst exhibiting valuable engineering applications.

show abstract

Coordinated optimization control of shield machine based on dynamic fuzzy neural network direct inverse control

Cited by 8 publications

References 8 publications

Intelligent real-time prediction of multi-region thrust of EPB shield machine based on SSA-LSTM

Intelligent real-time prediction of multi-region thrust of EPB shield machine based on SSA-LSTM

Fuzzy Neural Network Dynamic Inverse Control Strategy for Quadrotor UAV Based on Atmospheric Turbulence

Geological adaptive intelligent control of earth pressure balance shield machine based on deep reinforcement learning

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