Self-tuning RLS based multirate adaptive robust position control: A tilting-table test-bench study

Multirate adaptive robust controllers (MARC) have been shown to be a promising position control candidate for high-accuracy machine tools. However, its practical application is still restricted due to the lack of nonlinearity compensation capability as well as the slow convergence speed and limited accuracy of its traditional recursive least-square (RLS) algorithm. To address the above issues, this paper proposes a self-tuning multiple forgetting factor RLS algorithm, which simultaneously identifies the parameters of the linear and nonlinear parts of the system dynamics. Consequently, an adaptive multirate nonlinear compensator was designed to enhance position control performance. The effectiveness of the proposal was evaluated using a qualified test-bench developed in our collaboration with DMG MORI Co., Ltd. Both simulations and experiments confirmed the improved convergence speed and accuracy of the parameter estimation. In particular, the experimental results showed that the root mean square error of position tracking can be reduced by 12. 1% compared to the previous research. • Multiple self-tuning RLS achieves precise real-time estimation of system parameters. • Position- and velocity-dependent nonlinearities are separately identified. • Multirate feedforward control employs nonlinear compensation to enhance tracking.