Observer-based two-time scale robust control of free-flying flexible-joint space manipulators with external disturbances

Yu, Xiao; Chen, Li

doi:10.1017/s0263574716000801

Cited by 33 publications

(13 citation statements)

References 42 publications

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“…The above two sections mainly design the controller for the slow subsystem. In this section, based on the fast subsystem, Equation (19), the control scheme to suppress the flexible vibration of the base and joint is designed. The performance index function is constructed as follows:…”

Section: Controller Design Of the Fast Subsystemmentioning

confidence: 99%

“…Yang et al [18] discussed the motion control problem of ground flexible base robots and verified the effectiveness of the augmented method in the design of adaptive output feedback control. Yu et al [19] discussed the problem of the robust control of flexible joint space robots, and Pradhan et al [20] discussed the problem of the adaptive control of flexible link robots. The above studies provide a theoretical basis for the study of flexible robots; however, they only consider the influence of the single structural flexibility of the base, joint, or links.…”

Section: Introductionmentioning

confidence: 99%

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Repetitive Learning Sliding Mode Stabilization Control for a Flexible-Base, Flexible-Link and Flexible-Joint Space Robot Capturing a Satellite

Chen

2021

Applied Sciences

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During the process of satellite capture by a flexible base–link–joint space robot, the base, joints, and links vibrate easily and also rotate in a disorderly manner owing to the impact torque. To address this problem, a repetitive learning sliding mode stabilization control is proposed to stabilize the system. First, the dynamic models of the fully flexible space robot and the captured satellite are established, respectively, and the impact effect is calculated according to the motion and force transfer relationships. Based on this, a dynamic model of the system after capturing is established. Subsequently, the system is decomposed into slow and fast subsystems using the singular perturbation theory. To ensure that the base attitude and the joints of the slow subsystem reach the desired trajectories, link vibrations are suppressed simultaneously, and a repetitive learning sliding mode controller based on the concept of the virtual force is designed. Moreover, a multilinear optimal controller is proposed for the fast subsystem to suppress the vibration of the base and joints. Two sub-controllers constitute the repetitive learning sliding mode stabilization control for the system. This ensures that the base attitude and joints of the system reach the desired trajectories in a limited time after capturing, obtain better control quality, and suppress the multiple flexible vibrations of the base, links and joints. Finally, the simulation results verify the effectiveness of the designed control strategy.

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Section: Controller Design Of the Fast Subsystemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Repetitive Learning Sliding Mode Stabilization Control for a Flexible-Base, Flexible-Link and Flexible-Joint Space Robot Capturing a Satellite

Chen

2021

Applied Sciences

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“…Substituting the adaptive NN control law (24) and the RBFNN adaptive updating laws (25) and (26) into Eq. (32) yields…”

Section: Adaptive Nn Control Designmentioning

confidence: 99%

“…Rybus et al [24] presented a new control system for a free-floating space robot which consists of two modules, i.e., an optimal trajectory planning module and a nonlinear model predictive controller. Yu and Chen [25] designed an observer-based two-time scale robust control scheme for a free-flying flexible-joint space manipulator with external disturbances. Yao and Ge [26] presented a control strategy combining optimal motion planning and feedback control for the optimal reorientation of a free-floating space robot subject to initial state uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Adaptive trajectory tracking control of a free-flying space robot subject to input nonlinearities

Yao

2020

J Braz. Soc. Mech. Sci. Eng.

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The input nonlinearities widely exist in a large range of mechanical systems. Nonetheless, they were relatively less considered in the trajectory tracking control of a space robot in the previous studies. In this paper, an adaptive neural network (NN) control method is proposed for the fast and exact trajectory tracking control of an attitude-controlled free-flying space robot subject to input nonlinearities. The parametric uncertainties and external disturbances are also taken into the consideration. First, a model-based controller is designed to track the desired trajectory of the space robot within a framework of backstepping technique. Then, an adaptive NN controller is designed by using two NNs to compensate for the lumped uncertainties caused by parametric uncertainties and external disturbances and the input nonlinearities, respectively. Rigorous theoretical analysis for the semiglobal uniform ultimate boundedness of the whole closed-loop system is provided. The proposed adaptive NN controller is structurally simple and model-independent, which makes the controller affordable for practical applications. In addition, the proposed adaptive NN controller can guarantee the position and velocity tracking errors converge to the small neighborhoods about zero even in the presence of parametric uncertainties, external disturbances, and input nonlinearities. To the best of the authors' knowledge, there are really limited existing controllers can achieve such excellent performance in the same conditions. Numerical simulations illustrate the effectiveness and superiority of the proposed control method.

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“…The unknown payload of robot manipulators and other mechatronic systems have been estimated using several methods in the literature [18,19]. Off-line trained fuzzy/neural systems have been designed in [20,21].…”

Section: Introductionmentioning

confidence: 99%

State and Parameter Estimation of a Nonlinear Servo System Handling Noises and Varying Payloads

Çetin

Beyhan

2021

International Journal of Applied Mathematics Electronics and Computers

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Payload estimators have many parameters, which are trained using the recorded position, velocity and known payload information. To use these payload estimators in the real-time applications, accurate position and velocity information of the system are required. In this paper, first recently proposed sliding-mode observers (SMOs) are designed and compared for velocity estimation of a nonlinear servo system. Second, a parameter estimation based on sliding-mode super-twisting approach is designed to estimate unknown and varying parameter for a class of nonlinear systems. The convergence property of observers is considered using Lyapunov stability method. In the applications, the constant and varying payloads of the servo system have been estimated using the designed method and compared with Extended-Kalman Filter (EKF). In the final section, artificial noises with different SNR are applied to the measurement signal. When the less amplitude of noise signal is applied, second order SMO estimated the states and the payload better than EKF. However, EKF provides much better estimation results than second order SMO for large amplitude of noise signals. For the sake of generalization, second order SMO is a fast and robust observer for small noise cases. In addition, the filtering property of EKF has still importance for large noise cases.

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Observer-based two-time scale robust control of free-flying flexible-joint space manipulators with external disturbances

Cited by 33 publications

References 42 publications

Repetitive Learning Sliding Mode Stabilization Control for a Flexible-Base, Flexible-Link and Flexible-Joint Space Robot Capturing a Satellite

Repetitive Learning Sliding Mode Stabilization Control for a Flexible-Base, Flexible-Link and Flexible-Joint Space Robot Capturing a Satellite

Adaptive trajectory tracking control of a free-flying space robot subject to input nonlinearities

State and Parameter Estimation of a Nonlinear Servo System Handling Noises and Varying Payloads

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