Payload-invariant servo control using artificial neural networks

Mark E. Johnson; Michael B. Leahy Jr.; Steven K. Rogers

doi:10.1117/12.21184

1 August 1990 Payload-invariant servo control using artificial neural networks

Mark E. Johnson, Michael B. Leahy Jr., Steven K. Rogers

Proceedings Volume 1294, Applications of Artificial Neural Networks; (1990) https://doi.org/10.1117/12.21184
Event: 1990 Technical Symposium on Optics, Electro-Optics, and Sensors, 1990, Orlando, FL, United States

Abstract

A new form of adaptive model-based control is proposed and experimentally evaluated. An Adaptive Model-Based Neural Network Controller (AMBNNC) uses multilayer perceptron artificial neural networks to estimate the payload during high speed manipulator motion. The payload estimate adapts the feedforward compensator to umnodeled system dynamics and payload variations. The neural nets are trained through repetitive presentation of trajectory tracking error data. The AMBNNC was experimentally evaluated on the third link of a PUMA56O manipulator. Servo tracking performance was evaluated for a wide range of payload and trajectory conditions and compared to a non-adaptive model-based controller. The superior tracking accuracy of the AMBNNC demonstrates the potential of our proposed technique. 1.

Citation Download Citation

Mark E. Johnson, Michael B. Leahy Jr., and Steven K. Rogers "Payload-invariant servo control using artificial neural networks", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); https://doi.org/10.1117/12.21184

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