Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation

Xia, Kewei; Huo, Wei

doi:10.1016/j.isatra.2016.01.017

Cited by 70 publications

(29 citation statements)

References 21 publications

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“…All the relative variables can be measured by relative measuring sensors (such as cameras), but the angular velocity of the target cannot be got directly due to the non-cooperative target. However, from (15) and (16) we can see that, as long as the relative attitude is got, the angular velocity of target can be calculated from the angular velocity of chaser that can be easily measured by gyroscopes. [6,8,9]) introduce a set of first-order filters at each step of the standard backstepping approach to avoid the problem of "explosion of complexity", which makes the control law be complex in structure.…”

Section: Remarkmentioning

confidence: 99%

Integrated Translational and Rotational Control for the Final Approach Phase of Rendezvous and Docking

Han¹,

Shi²,

Xie³

2018

Asian Journal of Control

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This paper presents an approach of integrated translational and rotational control (ITRC) design for the final approach phase of rendezvous and docking on an ellipse orbit. First, considering the uncertainties, the integrated translation and rotation dynamics model is formulated, and backstepping scheme is used to design an ITRC law such that the states of the closed‐loop system can converge near zero. Then, by taking into consideration the input dynamics, the integrated translational and rotational model is transformed into the form of interconnected system, and thus, the small‐gain theorem can be utilized to guarantee the stability of the over all system. Stability analysis as well as simulation results shows the effectiveness of the presented approach.

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Section: Remarkmentioning

confidence: 99%

Integrated Translational and Rotational Control for the Final Approach Phase of Rendezvous and Docking

Han¹,

Shi²,

Xie³

2018

Asian Journal of Control

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“…However, controllers in [2][3][4][5] have only been carried out in the presence of exact inertial parameters or disturbances (or their bounds). A number of techniques have been developed to solve the control problems of 6DOF spacecraft with unavailable uncertainties [6][7][8][9][10][11][12][13]. For 6DOF spacecraft operations subject to unknown parameters and disturbances, adaptive controllers [6,14,15] are synthesized.…”

Section: Introductionmentioning

confidence: 99%

“…For formation flying control problems with parametric uncertainties and disturbances, a NN-based adaptive sliding mode controller is proposed in [12]. For cooperative rendezvous and docking maneuvers, a NN-based switching saturated control is investigated in [13]. Besides, adaptive NN controllers are also studied in helicopter [16] and marine surface vessel [17].…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the aforementioned works, the main contributions of this paper are threefold. First, in contrast to the NN employed in [12,13,16,17], a saturated NN is developed to approximate the dynamics uncertainties while avoiding the long time saturation arising from the large tracking errors at the beginning of the operation. Second, to satisfy the magnitude constraints of the actuators installed in spacecraft, an auxiliary variable generated by a dynamical system is introduced in the control design.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Saturated Neural Network Tracking Control of Spacecraft: Theory and Experimentation

Xia

Lee

Park

2019

International Journal of Aerospace Engineering

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An adaptive saturated neural network (NN) controller is developed for 6 degree-of-freedom (6DOF) spacecraft tracking, and its hardware-in-the-loop experimental validation is tested on the ground-based test facility. To overcome the dynamics uncertainties and prevent the large control saturation caused by the large tracking error at the beginning operation, a saturated radial basis function neural network (RBFNN) is introduced in the controller design, where the approximate error is counteracted by an adaptive continuous robust term. In addition, an auxiliary dynamical system is employed to compensate for the control saturation. It is proved that the ultimate boundedness of the closed-loop system is achieved. Besides, the proposed controller is implemented into a testbed facility to show the final operational reliability via hardware-in-the-loop experiments, where the experimental scenario describes that the simulator is tracking a planar trajectory while synchronizing its attitude with the desired angle. Experimental results illustrate that the proposed controller ensures that the simulator can track a preassigned trajectory with robustness to unknown inertial parameters and disturbances.

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“…Besides, from a practical viewpoint, the control input is restrained to prevent the actuators from going beyond their natural capabilities [14,15]. And radical basis function neural 2 Mathematical Problems in Engineering networks (RBFNNs) are used to stabilize complex nonlinear dynamic systems and deal with model uncertainties [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Safety-Guaranteed Trajectory Tracking Control for the Underactuated Hovercraft with State and Input Constraints

Gao

Wang

2017

Mathematical Problems in Engineering

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This paper develops a safety-guaranteed trajectory tracking controller for hovercraft by using a safety-guaranteed auxiliary dynamic system, an integral sliding mode control, and an adaptive neural network method. The safety-guaranteed auxiliary dynamic system is designed to implement system state and input constraints. By considering the relationship of velocity and resistance hump, the velocity of hovercraft is constrained to eliminate the effect of resistance hump and obtain better stability. And the safety limit of drift angle is well performed to guarantee the light safe maneuvers of hovercraft tracking with high velocities. In view of the natural capabilities of actuators, the control input is constrained. High nonlinearity and model uncertainties of hovercraft are approximated by employing adaptive radical basis function neural networks. The proposed controller guarantees the boundedness of all the closed-loop signals. Specifically, the tracking errors are uniformly ultimately bounded. Numerical simulations are implemented to demonstrate the efficacy of the designed controller.

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Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation

Cited by 70 publications

References 21 publications

Integrated Translational and Rotational Control for the Final Approach Phase of Rendezvous and Docking

Integrated Translational and Rotational Control for the Final Approach Phase of Rendezvous and Docking

Adaptive Saturated Neural Network Tracking Control of Spacecraft: Theory and Experimentation

Safety-Guaranteed Trajectory Tracking Control for the Underactuated Hovercraft with State and Input Constraints

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