Inertial parameter identification using contact force information for an unknown object captured by a space manipulator

Chu, Zhongyi; Ma, Yuanlin; Yueyang, Hou; Fengwen, Wang

doi:10.1016/j.actaastro.2016.11.019

Cited by 51 publications

(20 citation statements)

References 24 publications

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“…Space robots including the ETS-VII and the Orbit Express have been launched successfully [7], [8]. For a space robot, the base is free-floating or free-flying, motion of the manipulator and the base is strongly coupled, estimating the inertial parameters of the captured unknown target is beneficial for path planning, as well as the control system [9].…”

Section: Introductionmentioning

confidence: 99%

“…Ma et al used motion of the robotic arm to estimate the inertial parameters of the base [14], [15]. With the combination of momentum theorem and integration of the contact force, a two-step identification equation was derived by Chu to avoid the accumulated errors of measurements, and recursive least square (RLS)-affine projection sign algorithm was used for the equation [9]. Feng et al only used the conservation of angular momentum to derive the identification equation, and the probability distribution evolution algorithm was used to identify the inertial parameters of the target [16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Online One-Step Method to Identify Inertial Parameters of the Base and the Target Simultaneously for Space Robots in Postcapture

2020

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Space robots are in free-flying or free-floating mode, motions especially attitude motion of the base and motion of the manipulator are strongly coupled. Regarding to the uncertainties of the target's inertial parameters and variation of the base's inertial parameters, this paper presents a novel online one-step parameter identification method to estimate all the inertial parameters of the target and the base simultaneously. Momentum-and force-based identification equations are derived from the linear and angular momentum equations of the system and their derivation, and the modified recursive least square method is used for solving the equations efficiently. Compared with the traditional methods, the momentumbased equation can estimate all the inertial parameters of the base and the target simultaneously at each steps, while the force-based equation does not require torque of the joints. To verify the validity and feasibility of the proposed methods, 2D and 3D models with different targets and initial velocities are simulated and analyzed. The results show that all the estimated values show convergence to their ideal values and the method can be easily achieved online via recursive techniques.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Online One-Step Method to Identify Inertial Parameters of the Base and the Target Simultaneously for Space Robots in Postcapture

2020

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“…Manipulator systems are an important component of the spacecraft structure. These systems are currently widely applied to the on-orbit servicing missions, such as space object capture and structure assembly [1][2][3]. Manipulator systems in space are generally designed as long span and lightweight to increase workspace and reduce launch mass.…”

Section: Introductionmentioning

confidence: 99%

Payload Parameter Identification of a Flexible Space Manipulator System via Complex Eigenvalue Estimation

Zhang

et al. 2020

International Journal of Aerospace Engineering

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Manipulator systems are widely used in payload capture and movement in the ground/space operation due to their dexterous manipulation capability. In this study, a method for identifying the payload parameters of a flexible space manipulator using the estimated system of complex eigenvalue matrix is proposed. The original nonlinear dynamic model of the manipulator is linearized at a selected working point. Subsequently, the system state-space model and corresponding complex eigenvalue parameters are determined by the observer/Kalman filter identification algorithm using the torque input signal of the motor and the vibration output signals of the link. Therefore, the inertia parameters of the payload, that is, the mass and the moment of inertia, can be derived from the identified complex eigenvalue system and mode shapes by solving a least-squares problem. In numerical simulations, the proposed parameter identification method is implemented and compared with the classical recursive least-squares and affine projection sign algorithms. Numerical results demonstrate that the proposed method can effectively estimate the payload parameters with satisfactory accuracy.

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“…The inertia parameters of these systems were estimated, respectively, through the motion of manipulator. 9,10 Besides, Ni et al. estimated the mass parameter of captured payload according to the changes of space manipulator system frequencies between pre-capturing and post-capturing.…”

Section: Introductionmentioning

confidence: 99%

“…On account of some disturbance conditions not solely including the Gaussian white noise, the measurement noise could be hybrid and very complex. 10 Therefore, the novel identification approaches should be developed for complex operating environments of combined spacecraft.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based inertia tensor identification of the combined spacecraft

Chu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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The identification accuracy of inertia tensor of combined spacecraft, which is composed by a servicing spacecraft and a captured target, could be easily affected by the measurement noise of angular rate. Due to frequently changing operating environments of combined spacecraft in space, the measurement noise of angular rate can be very complex. In this paper, an inertia tensor identification approach based on deep learning method is proposed to improve the ability of identifying inertia tensor of combined spacecraft in the presence of complex measurement noise. A deep neural network model for identification is constructed and trained by enough training data and a designed learning strategy. To verify the identification performance of the proposed deep neural network model, two testing set with different ranks of measure noises are used for simulation tests. Comparison tests are also delivered among the proposed deep neural network model, recursive least squares identification method, and tradition deep neural network model. The comparison results show that the proposed deep neural network model yields a more accurate and stable identification performance for inertia tensor of combined spacecraft in changeable and complex operating environments.

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Inertial parameter identification using contact force information for an unknown object captured by a space manipulator

Cited by 51 publications

References 24 publications

An Online One-Step Method to Identify Inertial Parameters of the Base and the Target Simultaneously for Space Robots in Postcapture

An Online One-Step Method to Identify Inertial Parameters of the Base and the Target Simultaneously for Space Robots in Postcapture

Payload Parameter Identification of a Flexible Space Manipulator System via Complex Eigenvalue Estimation

Deep learning-based inertia tensor identification of the combined spacecraft

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