Motion control with optimal nonlinear damping: from theory to experiment

TL;DR

This paper presents the theoretical foundation and experimental validation of an optimal nonlinear damping control method, demonstrating its advantages over traditional PD control in realistic motion systems with noise.

Contribution

It extends the theoretical analysis of optimal nonlinear damping to complex motion systems and provides the first experimental validation of its effectiveness.

Findings

Optimal nonlinear damping outperforms PD control in trajectory tracking.

The method shows robustness against noise and disturbances.

Experimental results confirm theoretical advantages.

Abstract

Optimal nonlinear damping control was recently introduced for the second-order SISO systems, showing some advantages over a classical PD feedback controller. This paper summarizes the main theoretical developments and properties of the optimal nonlinear damping controller and demonstrates, for the first time, its practical experimental evaluation. An extended analysis and application to more realistic (than solely the double-integrator) motion systems are also given in the theoretical part of the paper. As comparative linear feedback controller, a PD one is taken, with the single tunable gain and direct compensation of the plant time constant. The second, namely experimental, part of the paper includes the voice-coil drive system with relatively high level of the process and measurement noise, for which the standard linear model is first identified in frequency domain. The linear approximation by two-parameters model forms the basis for designing the PD reference controller, which fixed feedback gain is the same as for the optimal nonlinear damping control. A robust sliding-mode based differentiator is used in both controllers for a reliable velocity estimation required for the feedback. The reference PD and the proposed optimal nonlinear damping controller, both with the same single design parameter, are compared experimentally with respect to trajectory tracking and disturbance rejection.