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In this paper, we consider a feedforward neural network for the control of a class of multi-input, multi-output nonlinear systems. While feedforward neural networks offer a simple and appealing approach to enhance the trajectory tracking performance of the closed loop system, stability analysis is often more difficult than the conventional implementation of a neural network embedded within the feedback...
This paper describes the control of an autonomous biped robot capable to be subjected to external pushing forces applied in the sagittal plane. A 1st order Takagi-Sugeno-Kang (TSK) type neuro fuzzy net, that was trained based on experimental and simulation data, is used. This net uses the zero moment point (ZMP) position and its variation as inputs, and the sagittal correction of the robotpsilas body...
This paper addresses the issue of trajectory tracking control based on a neural network controller for industrial manipulators. A new control scheme is proposed based on neural network technology and linear feedback approach for tracking a planned trajectory. The control system is established with two parallel subsystems designed separately. One is a linear controller based on state feedback with...
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