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Discrete-time neural dynamics-based online quadratic programming for discrete-time trajectory tracking of
Weicheng Xu1, Yuhui Bao1, Yifan Zhang1
1School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China.
Summary
This study introduces a discrete-time neural dynamics (DTND) framework for precise trajectory tracking in cable-driven planar robots. The method enables real-time control, overcoming challenges in nonlinear kinematics for applications like wall-drawing robots.
Area of Science:
- Robotics
- Control Systems Engineering
- Computational Dynamics
Background:
- Cable-driven planar robots offer mechanical simplicity and workspace flexibility.
- Accurate trajectory tracking in discrete-time is difficult due to nonlinear kinematics and computational demands.
Purpose of the Study:
- Develop a discrete-time neural dynamics (DTND)-based online quadratic programming (QP) framework.
- Enable real-time, accurate trajectory tracking for cable-driven planar robots in discrete-time settings.
Main Methods:
- Formulate the inverse kinematics problem as an online QP.
- Utilize a DTND-based iterative solver for real-time computation of cable-length rates.
- Implement the framework entirely in discrete-time for digital systems.
Main Results:
- Achieved stable error convergence and accurate trajectory tracking in numerical simulations.
- Validated the practical feasibility and real-world applicability through physical experiments.
- Demonstrated real-time performance without requiring explicit offline optimization.
Conclusions:
- The DTND-based online QP framework effectively addresses discrete-time trajectory tracking challenges for cable-driven planar robots.
- The proposed method is suitable for digital implementation and real-world engineering applications.
- Confirms the DTND approach's capability for stable and precise robotic control.
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