Control of a flexible-joint robot using neural networks

• Published in: IEEE transactions on control systems technology Vol. 5; no. 4; pp. 453 - 462
• Main Authors: Zeman, V., Patel, R.V., Khorasani, K.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.1997; Institute of Electrical and Electronics Engineers
• Subjects: Adaptive control; Algorithm design and analysis; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Connectionism. Neural networks; Control systems; Control theory. Systems; Exact sciences and technology; Lighting control; Manipulator dynamics; Neural networks; Nonlinear dynamical systems; Open loop systems; Optical coupling; Robot control; Robotics; Learning; Backpropagation; Intelligent robot; Feedback regulation; Neural network; Non linear system; Flexibility; Comparative study; Robotics; Manipulator
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/87.595927

Traditional robot control strategies assume both joint and link rigidity for the purpose of simplifying the control problem. The demand for greater precision coupled with the increased use of lightweight materials necessitates the inclusion of elastic dynamics in the control strategy. These highly nonlinear dynamics which increase the order of the system are extremely difficult to formulate with sufficient accuracy. The standard form of adaptive control does not appear to be applicable, since the basic assumptions on the system dynamics and nonlinear characteristics are rarely satisfied. For the case of manipulators with flexible joints, we propose an alternate control scheme which does not rely on accurate a priori knowledge of the manipulator dynamics, but instead can "learn" these dynamics by using a neural network.