Physics-informed data-driven control for energy-efficient spherical robot maneuvering
Spherical robots operating in unstructured environments face substantial challenges due to their nonlinear, underactuated dynamics and onboard power limitations. Traditional model-based controllers often lose effectiveness under dynamic uncertainties and actuator constraints, while purely data-drive...
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2025-08-01
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| Series: | Bulletin of Mathematical Sciences |
| Subjects: | |
| Online Access: | https://www.worldscientific.com/doi/10.1142/S1664360725500092 |
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| Summary: | Spherical robots operating in unstructured environments face substantial challenges due to their nonlinear, underactuated dynamics and onboard power limitations. Traditional model-based controllers often lose effectiveness under dynamic uncertainties and actuator constraints, while purely data-driven schemes may require extensive training or produce unrealistic control signals. This paper proposes a hybrid, physics-informed, data-driven control strategy for a pendulum-driven spherical robot. A neural network (NN) controller is trained in simulation only with a loss function that penalizes trajectory errors, large control signals, and torque saturation. The control law leverages a modified terminal sliding mode formulation that guarantees finite-time convergence, validated through Lyapunov arguments. Numerical simulations demonstrate that the proposed method consumes significantly less control energy than conventional strategies and remains robust under disturbances, thus promoting energy-efficient trajectory tracking and offering a promising solution to the challenges faced by spherical robots in unstructured environments. |
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| ISSN: | 1664-3607 1664-3615 |