In this paper, a motion and balance control scheme is introduced for inverted pendulums using artificial neural network (ANN). The control strategy uses a trade-off strategy to achieve motion tracking and balance control simultaneously with a single controller. Unlike other neural control strategies, no offline learning or a priori system's dynamics knowledge is required. The controller is trained online to learn the nonlinear inverted pendulum system's dynamics. Simulation results for different situations highlight the performance of the proposed controller in compensating for friction nonlinearities and for external disturbance. Furthermore, ANNs' inherent parallelism makes them a good candidate for real-time implementation.

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Conference 2011 Canadian Conference on Electrical and Computer Engineering, CCECE 2011
Chaoui, H, & Sicard, P. (Pierre). (2011). Motion and balance neural control of inverted pendulums with nonlinear friction and disturbance. In Canadian Conference on Electrical and Computer Engineering (pp. 1222–1227). doi:10.1109/CCECE.2011.6030657