Reconfigurable Fault-Tolerant Control for Spacecraft Formation Flying Based on Iterative Learning Algorithms

Gui, Yule; Jia, Qingxian; Li, Huayi; Cheng, Yuehua

doi:10.3390/app12052485

Cited by 13 publications

(4 citation statements)

References 29 publications

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“…The IL control has the major advantage that it does not require an accurate system model or even does not need any prior system information, which brings a great convenience to the controller design. Since the pioneering work by Arimoto et al [23] in 1984, the IL control has been broadly utilized for the control of a large range of mechanical systems, such as robotic manipulators [24][25][26][27][28][29][30][31][32][33], robotic fish [34,35], mobile robots [36,37], spacecraft [38][39][40], and permanent magnet spherical actuators [41].…”

Section: Introductionmentioning

confidence: 99%

Gain-Scheduled Sliding-Mode-Type Iterative Learning Control Design for Mechanical Systems

et al. 2022

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In this paper, a novel gain-scheduled sliding-mode-type (SM-type) iterative learning (IL) control approach is proposed for the high-precision trajectory tracking of mechanical systems subject to model uncertainties and disturbances. Based on the SM variable, the proposed controller is synthesized involving a feedback regulation item, a feedforward learning item, and a robust switching item. The feedback regulation item is adopted to regulate the position and velocity tracking errors, the feedforward learning item is applied to handle the model uncertainties and repetitive disturbance, and the robust switching item is introduced to compensate the nonrepetitive disturbance and linearization residual error. Moreover, the gain-scheduled mechanism is employed for both the feedback regulation item and feedforward learning item to enhance the convergence speed. Convergence analysis illustrates that the position and velocity tracking errors can eventually regulate to zero under the proposed controller. By combining the advantages of both SM control and IL control, the proposed controller has strong robustness against model uncertainties and disturbances. Lastly, simulations and comparisons are provided to evaluate the efficiency and excellent performance of the proposed control approach.

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Section: Introductionmentioning

confidence: 99%

Gain-Scheduled Sliding-Mode-Type Iterative Learning Control Design for Mechanical Systems

et al. 2022

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“…In the active FTC design, actuator faults are diagnosed, and parameters can be reconfigured online to achieve the desired performance [ 15 ]. An iterative learning observer-based reconstructive-FTC protocol for spacecraft formation was designed in [ 16 ]. A reinforcement learning-based data-driven active FTC method for multiple QRs was studied in [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Target Enclosing and Coverage Control for Quadrotors with Constraints and Time-Varying Delays: A Neural Adaptive Fault-Tolerant Formation Control Approach

Zhao

Zhu

Zhang

2022

Sensors

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This paper investigates the problem of formation fault-tolerant control of multiple quadrotors (QRs) for a mobile sensing oriented application. The QRs subject to faults, input saturation and time-varying delays can be controlled to perform a target-enclosing and covering task while guaranteeing the state constraints will not be exceeded. A distributed formation control scheme is proposed, using a radial basis function neural network (RBFNN)-based time-delay position controller and an adaptive fault-tolerant attitude controller. The Lyapunov–Krasovskii approach is used to analyze the time-varying delay. Barrier Lyapunov function is deployed to handle the prescribed constraints, and an auxiliary system combined with a command filter is designed to resolve the saturation problem. An RBFNN and adaptive estimators are deployed to provide estimates of disturbances, fault signals and uncertainties. It is proven that all the closed-loop signals are bounded under the proposed protocol, while the prescribed constraints will not be violated, which enhances the flight safety and QR formation’s applicability. Comparative simulations based on application scenarios further verify the effectiveness of the proposed method.

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“…Zou et al studied the robust attitude-cooperative control of a spacecraft formation flying with actuator failure and proposed a distributed adaptive fault-tolerant attitude cooperative control law, which did not require online fault identification and could ensure that a group of spacecrafts simultaneously track the same time-varying reference attitude [2]. In reference [3], a reconfigurable fault-tolerant control method for spacecraft formation based on an iterative learning algorithm was proposed, which achieved the accurate maintenance of the spacecraft formation configuration in the case of space disturbance and thruster failure. Zhang et al designed a fast, non-singular terminal sliding-mode finite-time fault-tolerant controller for a spacecraft formation with some actuators that completely failed and realized the synchronous tracking control of the spacecraft formation attitude [4].…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Attitude-Orbit Coupled Fault-Tolerant Control for Multi-Spacecraft Formation

2022

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In this paper, the attitude-orbit coupled control problem for multi-spacecraft formation with limited communication capability and actuator failure is investigated. For the purpose of solving this problem, an event-triggered attitude-orbit coupled fault-tolerant control strategy is proposed. First, an integrated nonlinear dynamic model including the coupling characteristics of the attitude and orbit is established based on the Kane equation. Second, the nonlinear dynamic model is linearized at the reference state to facilitate the controller design. Third, a dynamic event-triggered mechanism is designed and an event-triggered fault-tolerant control law is developed. The stability of closed-loop control systems can be ensured under the designed control law and a sufficient condition that Zeno’s behavior can be avoided is presented. Finally, simulation results are given to show the effectiveness of the proposed control method.

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Reconfigurable Fault-Tolerant Control for Spacecraft Formation Flying Based on Iterative Learning Algorithms

Cited by 13 publications

References 29 publications

Gain-Scheduled Sliding-Mode-Type Iterative Learning Control Design for Mechanical Systems

Gain-Scheduled Sliding-Mode-Type Iterative Learning Control Design for Mechanical Systems

Target Enclosing and Coverage Control for Quadrotors with Constraints and Time-Varying Delays: A Neural Adaptive Fault-Tolerant Formation Control Approach

Event-Triggered Attitude-Orbit Coupled Fault-Tolerant Control for Multi-Spacecraft Formation

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