Backlash Compensation with Filtered Prediction in Discrete Time Nonlinear Systems by Dynamic In- Version Using Neural Networks

A dynamics inversion compensation scheme is designed for control of nonlinear discrete-time systems with input backlash. This paper extends the dynamic inversion technique to discrete-time systems by using a filtered prediction, and shows how to use a neural network (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamics preinverse of an invertible discrete time dynamical system. A discrete-time tuning algorithm is given for the NN weights so that the backlash compensation scheme guarantees bounded tracking and backlash errors, and also bounded parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard discrete-time adaptive control techniques, no certainty equivalence (CE) or linear-inthe-parameters (LIP) assumptions are needed.