Intelligent direct thrust control for multivariable turbofan engine based on reinforcement and deep learning methods

Zhu, Yiyang; Pan, Muxuan; Zhou, Wenxiang; Huang, Jinquan

doi:10.1016/j.ast.2022.107972

Cited by 16 publications

(3 citation statements)

References 15 publications

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“…The actuators of the "SZ-1" AUV consist of eight thrusters arranged in space. Therefore, we establish the mathematical models of its horizontal and vertical propulsion systems and analyze the spatial thrust distribution of the thrusters, making the modeling more refined and more in line with the actual physical model while discussing the thrust distribution methods [35].…”

Section: Modelling and Thrust Allocation Of The "Sz-1" Auv Propulsion...mentioning

confidence: 99%

Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

Xu,

Zhang,

Zhong

2024

JMSE

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Autonomous Underwater Vehicles (AUVs) are widely used for the inspection of seabed pipelines. To address the issues of low trajectory tracking accuracy in AUV inspection processes due to uncertain ocean current disturbances, this paper designs a new dual-loop controller based on Model Predictive Control (MPC) and Variable Universe S-plane algorithms (S-VUD FLC, where VUD represents Variable Universe Discourse and FLC represents Fuzzy Logic Control) to achieve three-dimensional (3-D) trajectory tracking of an over-actuated AUV under uncertain ocean current disturbances. This paper uses MPC as the outer-loop position controller and S-VUD FLC as the inner-loop speed controller. The outer-loop controller generates desired speed instructions that are passed to the inner-loop speed controller, while the inner-loop speed controller generates control input and uses a direct logic thrust distribution method that approaches optimal energy consumption to distribute the thrust generated by the propellers to the over-actuated AUV, achieving closed-loop tracking of the entire trajectory. When designing the outer-loop MPC controller, the actual control input constraints of the system are considered, and control increments are introduced to reduce control model errors and the impact of uncertain external disturbances on the actual AUV model parameters. When designing the inner-loop S-VUD FLC, the strong robustness of the variable universe fuzzy controller and the easy construction characteristics of the S-plane algorithm are combined, and integral action is introduced to improve the system’s tracking accuracy. The stability of the outer loop controller is proven by the Lyapunov method, and the stability of the inner loop controller is verified by simulation. Finally, simulations show that the over-actuated AUV has fast tracking processes and high tracking result accuracy under uncertain ocean current disturbances, demonstrating the effectiveness of the designed dual-loop controller.

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Section: Modelling and Thrust Allocation Of The "Sz-1" Auv Propulsion...mentioning

confidence: 99%

Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

Xu,

Zhang,

Zhong

2024

JMSE

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“…These all impose strict demands on the engine's control system. With the increasing demands, traditional control techniques cannot handle the tasks well [2,3]. Therefore, the engineering community of aero-engine control is urged to seek more advanced control techniques.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Reduced-Dimensional Scheme of Model Predictive Control for Aero-Engines

Jiang,

Wang,

Liu

et al. 2024

Actuators

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Model Predictive Control (MPC) has many advantages in controlling an aero-engine, such as handling actuator constraints, but the computational burden greatly obstructs its application. The current multiplex MPC can reduce computational complexity, but it will significantly decrease the control performance. To guarantee real-time performance and good control performance simultaneously, an intelligent reduced-dimensional scheme of MPC is proposed. The scheme includes a control variable selection algorithm and a control sequence coordination strategy. A constrained optimization problem with low computational complexity is first constructed by using only one control variable to define a reduced-dimensional control sequence. Therein, the control variable selection algorithm provides an intelligent mode to determine the control variable that has the best control effect at the current sampling instant. Furthermore, a coordination strategy is adopted in the reduced-dimensional control sequence to consider the interaction of control variables at different predicting instants. Finally, an intelligent reduced-dimensional MPC controller is designed and implemented on an aero-engine. Simulation results demonstrate the effectiveness of the intelligent reduced-dimensional scheme. Compared with the multiplex MPC, the intelligent reduced-dimensional MPC controller enhances the control quality significantly by 34.06%; compared with the standard MPC, the average time consumption is decreased by 64.72%.

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“…However, in industrial settings, most systems are complex, multivariable, coupled, and laggy, and this method may result in imprecise and unstable system control. Zhu et al [20] implemented intelligent, direct thrust control for multivariable turbofan engines using proximal policy optimization (PPO), a deep reinforcement learning algorithm. However, this method did not deeply investigate the impact of the PPO algorithm's activation and advantage functions on system performance.…”

Section: Introductionmentioning

confidence: 99%

Multivariable Coupled System Control Method Based on Deep Reinforcement Learning

Xu,

Li,

Zhang

2023

Sensors

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Due to the multi-loop coupling characteristics of multivariable systems, it is difficult for traditional control methods to achieve precise control effects. Therefore, this paper proposes a control method based on deep reinforcement learning to achieve stable and accurate control of multivariable coupling systems. Based on the proximal policy optimization algorithm (PPO), this method selects tanh as the activation function and normalizes the advantage function. At the same time, based on the characteristics of the multivariable coupling system, the reward function and controller are redesigned structures, achieving stable and precise control of the controlled system. In addition, this study used the amplitude of the control quantity output by the controller as an indicator to evaluate the controller’s performance. Finally, simulation verification was conducted in MATLAB/Simulink. The experimental results show that compared with decentralized control, decoupled control and traditional PPO control, the method proposed in this article achieves better control effects.

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Intelligent direct thrust control for multivariable turbofan engine based on reinforcement and deep learning methods

Cited by 16 publications

References 15 publications

Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

Intelligent Reduced-Dimensional Scheme of Model Predictive Control for Aero-Engines

Multivariable Coupled System Control Method Based on Deep Reinforcement Learning

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