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Summary
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This study introduces a novel control algorithm for permanent magnet spherical actuators (PMSAs) to enhance motion stability. The hybrid approach effectively compensates for system uncertainties and inter-axis couplings, improving performance.

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Area of Science:

  • Robotics and Control Systems
  • Nonlinear System Dynamics
  • Artificial Intelligence in Engineering

Background:

  • Permanent magnet spherical actuators (PMSAs) are complex nonlinear systems with inherent inter-axis couplings.
  • System uncertainties, including external loads, friction, and manufacturing errors, significantly degrade PMSA motion control performance.
  • Existing control methods struggle to adequately address these coupled nonlinear dynamics and uncertainties.

Purpose of the Study:

  • To develop and validate a robust control strategy for enhancing the motion stability of permanent magnet spherical actuators.
  • To mitigate the adverse effects of system uncertainties and inter-axis couplings on PMSA performance.
  • To leverage advanced algorithms for high-precision motion control in complex robotic systems.

Main Methods:

  • Formulation of the dynamic and computed torque models for the spherical actuator.
  • Development of a dynamic decoupling control algorithm.
  • Implementation of a single neural adaptive (SNA) controller combined with a particle swarm optimization (PSO)-trained neural network (NN) identifier.
  • Validation through simulation and experimental testing on a research prototype.

Main Results:

  • The PSO-optimized NN identifier achieved high-precision compensation for system uncertainties.
  • The SNA controller effectively reduced the impact of compensation errors and ensured system stability despite external disturbances.
  • The hybrid algorithm demonstrated strong robustness against parameter perturbations (5%, 10%, 15%).
  • Effective compensation of uncertainties and elimination of inter-axis couplings were validated.

Conclusions:

  • The proposed hybrid control algorithm significantly improves the motion stability and robustness of permanent magnet spherical actuators.
  • The integration of PSO for NN training and SNA control offers a promising approach for complex nonlinear systems.
  • The method provides a reliable solution for compensating uncertainties and inter-axis couplings in PMSAs.