The University of Texas System

Rastko R Selmic

Anthony J Calise

Frank L Lewis

Michael B McFarland

Methods and systems for backlash compensation. Restrictive assumptions on the backlash nonlinearity (e.g. the same slopes of the lines, etc.) are not required. The compensator scheme has dynamic inversion structure, with a neural network in the feedforward path that approximates the backlash inversion error plus filter dynamics needed for backstepping design. The neural network controller does not require preliminary off-line training. Neural network tuning is based on a modified Hebbian tuning law, which requires less computation than backpropagation. The backstepping controller uses a practical filtered derivative, unlike the usual differentiation required by earlier backstepping routines. Rigorous stability proofs are given using Lyapunov theory. Simulation results show that the proposed compensation scheme is an efficient way of improving the tracking performance of a vast array of nonlinear systems with backlash.

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Dallas, TX, United States of America

Rastko R Selmic