Deep learning-based smith predictor design for a remote grasping control system

• Published in: Journal of mechanical science and technology 2022-05, Vol.36 (5), p.2533-2545
• Main Authors: Kim, Dong-Eon, Li, Ailing, Dau, Mai-Ngoc, Kim, Hyun-Hee, Chung, Wan-Young
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Control; Control systems design; Deep learning; Dynamical Systems; End effectors; Engineering; Gloves; Industrial and Production Engineering; Mechanical Engineering; Multilayer perceptrons; Multilayers; Neural networks; Original Article; Predictive control; Recurrent neural networks; Remote control; Robot control; Time lag; Vibration
• ISSN: 1738-494X; 1976-3824
• DOI: 10.1007/s12206-022-0435-9

In this study, a robotic hand control system was designed based on data gloves, aiming to provide more intuitive control and improved operational performance for a remote robotic hand. Compensation measures were proposed for the time lag effect on the remote-control system to address the input and feedback time delays of the remote robot system. A Smith predictor structure was modified by replacing the linear estimator with a recurrent neural network. A convolutional neural network was applied to the long short-term memory (LSTM) model, as it had a better convergence time and learning performance than the multi-layer perceptron model during training. The experimental results demonstrate that the control effect of this scheme is approximately 0.5 s faster than the normal Smith predictive control, proving its effectiveness.