Force sensorless interaction wrench estimation for neural-learning impedance control of a flying parallel robot with
Minglei Zhu1, Yuhui Guo2, Dawei Gong3,4
1Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu, China.
Scientific Reports
|March 9, 2026
Summary
This study introduces a novel sensorless adaptive neural-learning impedance controller for flying parallel robots (FPRs). This controller ensures stable and compliant physical interaction, even with actuator saturation.
Area of Science:
- Robotics
- Control Systems
- Artificial Intelligence
Background:
- Flying parallel robots (FPRs) require precise control for compliant physical interaction.
- Actuator saturation poses a significant challenge in achieving robust control for FPRs.
- Existing control methods often struggle with system uncertainties and external disturbances.
Purpose of the Study:
- To develop a sensorless adaptive neural-learning impedance controller for FPRs.
- To explicitly address and accommodate actuator saturation in the control system.
- To ensure stable and compliant physical interaction under various contact conditions.
Main Methods:
- Establishment of the dynamic model for a multi-unmanned aerial vehicle (UAV) heterogeneous cooperative FPR.
- Development of an external wrench observer for estimating contact-induced torque.
- Design of a Lyapunov-based radial basis function neural network (RBFNN) impedance controller with an auxiliary compensation system for actuator saturation.
Main Results:
- The proposed controller effectively handles system uncertainties and disturbances.
- The auxiliary compensation system mitigates the adverse effects of actuator input saturation.
- Closed-loop stability of the FPR system is rigorously guaranteed under the proposed control law.
Conclusions:
- The sensorless adaptive neural-learning impedance controller enables compliant and stable physical interaction for FPRs.
- The approach demonstrates robustness against system uncertainties and actuator saturation.
- ADAMS-Simulink co-simulation validates the controller's effectiveness in diverse contact scenarios.
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