A New Force Control Method by Combining Traditional PID Control with Radial Basis Function Neural Network for a Spacecraft Low-Gravity Simulation System

Cao, Jun; Zhang, Yang; Ju, Changcheng; Xue, Xinyi; Zhang, Jiyuan

doi:10.3390/aerospace10060520

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Furthermore, a novel rigid-elastic coupling suspension gravity compensation system was proposed by Jia [9] that significantly improved the compensation accuracy of the system. De Stefano and others [10] proposed a gravity compensation strategy for a seven-degree-of-freedom space manipulator to solve the torque limitation problem encountered in a 1 g experimental environment by a joint motor designed for 0 g. Cao et al [11] aimed at the control problem of low-gravity simulation systems based on the suspension method, and used a traditional PID control method with a radial basis function (RBF) neural network to reduce the error of the constant tension control problem. The large-scale gravity compensation system developed by Liu et al [12] has been successfully applied to validate the maneuverability of the lunar rover, which compensates for 5/6 of the rover's gravity on Earth in a square test site of 900 square meters.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Low-Gravity Simulation Scheme for Mars Ascent Vehicle

Li,

Wang,

et al. 2024

Aerospace

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The sample carried back by the Mars Ascent Vehicle (MAV) is a potential flagship mission of deep space exploration in recent years. A low-gravity simulation experiment is an effective method and a necessary stage for verifying the performance of the MAV launch dynamic in Earth’s gravity. In this paper, the uniqueness of low-gravity simulation is illustrated by the classical pulley balance method for the high dynamic process of a test model of the MAV. Its movement direction is the same as the compensation force, which leads to the relaxation of the sling and the failure of the compensation force in traditional cable suspension. Here, three cable suspension schemes including an improved pulley balancing scheme based on a coordinate transformation scheme and based on a dynamic pulley group scheme are proposed. For the actual launch condition of the MAV, the motion state of the ascent under the schemes and the real Mars launch are compared, which proves the feasibility of the schemes. Among them, the improved pulley balancing scheme has the best gravity compensation effect, and the error between the average value and the required value is the smallest, only 1%.

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Section: Introductionmentioning

confidence: 99%

Design and Analysis of Low-Gravity Simulation Scheme for Mars Ascent Vehicle

Li,

Wang,

et al. 2024

Aerospace

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“…In utilizing radial basis neural networks for optimizing controller parameters, Reference [17] employs RBF neural networks to optimize the coefficients of PID controllers to enhance control performance. In a comparable approach within the aerospace industry, Reference [18] incorporates RBF neural networks with PID to resolve the constant tension issue. In [19][20][21], they mainly use RBF neural networks to improve controller control capability by optimizing its parameters when faced with external interference.…”

Section: Introductionmentioning

confidence: 99%

Brain Emotional Learning Control Based on Radial Basis Function for Permanent Magnet Synchronous Motor

Li,

Liang

et al. 2023

Electronics

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It is proposed to use a brain emotional learning control (BELC) system that is based on radial basis function (RBF) in order to enhance the performance of the speed control system of a permanent magnet synchronous motor (PMSM) and its capacity to remain stable following an unexpected load. First, the shortcomings of the traditional PI control in the PMSM speed-control system are explained. The intelligent control system has excellent learning ability and can effectively improve the control effect. The brain emotional learning control is great for nonlinear system control. Thus, it was utilized as the PMSM speed controller in place of the conventional PI control. The RBF neural network was used to optimize some parameters of BELC. Therefore, the process of adjusting parameters in BELC was simplified and the controller ability to resist disturbances was enhanced. The results showed that the brain-based emotional learning control based on RBF optimization (RBF-based BELC) not only improved the speed-control effect of the PMSM system but also enhanced the stability of the torque and current.

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Actor-Critic Neural-Network-Based Fractional-Order Sliding Mode Control for Attitude Tracking of Spacecraft with Uncertainties and Actuator Faults

Jing,

Ma,

Zhang

et al. 2024

Fractal Fract

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This paper investigates the attitude control of rigid spacecraft in the presence of uncertainties, disturbances, and actuator faults. In order to effectively address these challenges and improve the performance of the system, a novel actor-critic neural-network-based fractional-order sliding mode control (ACNNFOSMC) has been developed for spacecraft. The integration of actor-critic neural network, fractional-order theory, and sliding mode control enables dual functionality: the actor-critic neural network serves to approximate the aggregate of uncertain parameters, disturbances, and actuator faults, thereby facilitating their compensation, while the fractional-order sliding mode control mechanism significantly improves the system’s tracking precision and overall robustness against uncertainties. Theoretical analyses are presented to analyze the stability of the proposed control framework. Thorough examination via simulation experiments affirms the effectiveness and control precision of attitude of our proposed control strategy, even in complex operational scenarios.

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A New Force Control Method by Combining Traditional PID Control with Radial Basis Function Neural Network for a Spacecraft Low-Gravity Simulation System

Cited by 3 publications

References 13 publications

Design and Analysis of Low-Gravity Simulation Scheme for Mars Ascent Vehicle

Design and Analysis of Low-Gravity Simulation Scheme for Mars Ascent Vehicle

Brain Emotional Learning Control Based on Radial Basis Function for Permanent Magnet Synchronous Motor

Actor-Critic Neural-Network-Based Fractional-Order Sliding Mode Control for Attitude Tracking of Spacecraft with Uncertainties and Actuator Faults

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