Iterative Learning-Based Robotic Controller With Prescribed Human–Robot Interaction Force

Maqsood, Kamran; Huang, Deqing; Yang, Chenguang; Li, Yanan

doi:10.1109/tase.2021.3119400

Cited by 14 publications

(13 citation statements)

References 42 publications

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“…By observing Fig. 2B, it is known that the initial trajectory of the human user learned by DMPs has an irregular shape, which is hard to be learned by the method in [15].…”

Section: )mentioning

confidence: 99%

“…In [14], with the proposed spacial ILC, robot's learning is space-based instead of time-based, and a desired interaction force is thus achieved with varying motion speed. To further reduce the time-related uncertainties, [15] designs a performance index function and iteratively updates the robot's trajectory by parameterization for a desired interaction force. Although this method has good robustness against human motion uncertainties, it needs to know the shape of the robot's desired trajectory in advance, which limits its applications.…”

Section: Introductionmentioning

confidence: 99%

“…3) Compared with [15], the shape of the robot's desired reference trajectory does not need to be known as a priori, which significantly expands its application domains.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Motion Primitives-Based Trajectory Learning for Physical Human–Robot Interaction Force Control

Maqsood

Zeng

Yang

et al. 2024

IEEE Trans. Ind. Inf.

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One promising function of interactive robots is to provide a specific interaction force to human users. For example, rehabilitation robots are expected to promote patients' recovery by interacting with them with a prescribed force. However, motion uncertainties of different individuals, which are hard to predict due to the varying motion speed and noises during motion, degrade the performance of existing control methods. This paper proposes a method to learn a desired reference trajectory for a robot based on dynamic motion primitives (DMPs) and iterative learning (IL). By controlling the robot to follow the generated desired reference trajectory, the interaction force can achieve a desired value. In our proposed approach, DMPs are first employed to parameterize the demonstration trajectories of the human user. Then a recursive least square (RLS)-based estimator is developed and combined with the Adam optimization method to update the trajectory parameters so that the desired reference trajectory of the robot is iteratively obtained by resolving the DMPs. Since the proposed method parameterizes the trajectories depending on the phrase variable, it removes the essential assumption of traditional IL methods where the iteration period should be invariant, and thus has improved robustness compared with the existing methods. Experiments are performed using an interactive robot to validate the effectiveness of our proposed scheme.

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“…By observing Fig. 2B, it is known that the initial trajectory of the human user learned by DMPs has an irregular shape, which is hard to be learned by the method in [15].…”

Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Motion Primitives-Based Trajectory Learning for Physical Human–Robot Interaction Force Control

Maqsood

Zeng

Yang

et al. 2024

IEEE Trans. Ind. Inf.

Self Cite

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“…Regrettably, the transmission mechanism of the traditional manipulator, which typically includes gears, lead screws, or belts, renders it more susceptible to factors like friction, deflection, backlash, and compressibility. Consequently, the transmission mechanism is prone to aging and wear, resulting in a decrease in the positioning accuracy of the manipulator 3,4 . To ensure precise control and extend the working life of the manipulator, routine maintenance or the replacement of worn transmission components becomes necessary.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the transmission mechanism is prone to aging and wear, resulting in a decrease in the positioning accuracy of the manipulator. 3,4 To ensure precise control and extend the working life of the manipulator, routine maintenance or the replacement of worn transmission components becomes necessary. However, this not only increases maintenance expenses but also reduces operational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Parameters self‐turning controller fused with an adaptive nominal model for disturbance observer: Application to direct drive manipulator with significant payload changes

Liu,

Chen,

Xie

2024

Adaptive Control & Signal

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SummaryThe direct drive manipulator, which directly links the joint shaft to the motor rotor, is susceptible to external disturbances and model perturbations. When the direct drive manipulator operates with varying payloads, the disturbance observer (DOB) with a constant nominal model struggles to achieve satisfactory performance. To minimize estimation errors caused by disturbance, enhance trajectory tracking accuracy, and improve system robustness, this article proposes a controller parameter self‐tuning strategy with settling time, fused with a nominal model adaptive disturbance observer. Specifically, by optimizing the gain coefficient of the model reference adaptive identification (MRAI), the significant changes in payload at the manipulator's end joint can be quickly and accurately estimated. The inertia introduced by the payload is then employed to continuously update the nominal model of the manipulator, incorporating information about the inertia of the coupling joints. In addition, the controller parameters are updated using a self‐tuning link with settling time, which guarantees that the closed‐loop characteristics of the system remain unaffected by payload changes. Finally, the proposed method was validated using a two‐joint direct drive manipulator. The results demonstrate the robustness of the proposed method in the presence of significant payload changes, and the accuracy of disturbance estimation and the tracking performance of the proposed control strategy is significantly improved.

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A fast transfer reinforcement learning model for transferring force-based human speed adjustment skills to robots for collaborative assembly posture alignment

Sun,

Zhang,

Han

et al. 2024

Advanced Engineering Informatics

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Iterative Learning-Based Robotic Controller With Prescribed Human–Robot Interaction Force

Abstract: Iterative learning-based robotic controller with prescribed human-robot interaction force. IEEE Transactions on Automation Science and Engineering.

Cited by 14 publications

References 42 publications

Dynamic Motion Primitives-Based Trajectory Learning for Physical Human–Robot Interaction Force Control

Dynamic Motion Primitives-Based Trajectory Learning for Physical Human–Robot Interaction Force Control

Parameters self‐turning controller fused with an adaptive nominal model for disturbance observer: Application to direct drive manipulator with significant payload changes

A fast transfer reinforcement learning model for transferring force-based human speed adjustment skills to robots for collaborative assembly posture alignment

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