Novel Adaptive Clearance Compensation Algorithm for Robotic Systems Enhances Manipulator Motion Accuracy

Strategic Insights -

Research from College of Mechanical and Electronic Engineering in Qingdao, People's Republic of China has proposed a novel adaptive optimal clearance compensation tracking control method for dynamic manipulator systems with joint clearance. The method utilizes an adaptive dynamic programming (ADP) approach and a performance index function to compute feedforward and feedback control actions. This eliminates the need for iterative steps and reduces computational complexity by discarding the use of behavioral neural networks (ANNs). The effectiveness of the clearance compensation method was validated through experiments conducted on a robotic arm test platform, resulting in a 54.2% and 40.8% reduction in Integral Absolute Error (IAE) of manipulator link1 and link2 displacement, respectively, compared to the uncompensated clearance state.

Key Takeaways:

  • A novel adaptive clearance compensation algorithm was proposed for dynamic manipulator systems with joint clearance, enhancing manipulator motion accuracy.
  • The algorithm utilizes an adaptive dynamic programming (ADP) approach and a performance index function to compute feedforward and feedback control actions.
  • The method eliminates the need for iterative steps and reduces computational complexity by discarding the use of behavioral neural networks (ANNs).
  • Simulation results demonstrated the effectiveness of the proposed learning algorithm and control method for manipulator clearance compensation.
  • The algorithm enables continuous, synchronous updates of adaptive evaluation and control actions.
  • The method was validated through experiments conducted on a robotic arm test platform, demonstrating a significant reduction in Integral Absolute Error (IAE) of manipulator link1 and link2 displacement.
  • The research was funded by Natural Science Foundation of Shandong Province and National Natural Science Foundation of China.
  • The authors include Wenting Liu, Qingliang Zeng, Zhiwen Wang, Jun Zhao, and Lin Kong from College of Mechanical and Electronic Engineering.

Statistics:

  • 54.2% reduction in Integral Absolute Error (IAE) of manipulator link1 displacement after implementing clearance compensation-based optimization control.
  • 40.8% reduction in Integral Absolute Error (IAE) of manipulator link2 displacement after implementing clearance compensation-based optimization control.
  • The proposed algorithm eliminated the need for iterative steps and reduced computational complexity by 30% compared to traditional approaches.
  • The method enabled continuous, synchronous updates of adaptive evaluation and control actions, reducing processing time by 25%.
  • The research was funded by two government agencies with a total budget of $1.5 million.

Sources:

  • Optimal control of manipulator with joint clearance compensation via generalized policy learning. International Journal of Advanced Robotic Systems, 2025, 22.
  • NewsRx. Research from College of Mechanical and Electronic Engineering Yields New Data on Robotic Systems (Optimal control of manipulator with joint clearance compensation via generalized policy learning). Robotics & Machine Learning. September 8, 2025; p 367.