Design Constraints of Disturbance Observer-based Motion Control Systems are Stricter in the Discrete-Time Domain
Emre Sariyildiz

TL;DR
This paper reveals that the design constraints for disturbance observer-based motion control systems are more restrictive in discrete-time implementations, affecting robustness and stability, and highlights the limitations of continuous-time analysis methods.
Contribution
It introduces a unified analysis framework using Bode Integral Theorems to derive fundamental design constraints for digital DOb-based controllers.
Findings
Stricter design constraints in discrete-time systems limit robustness.
Continuous-time analysis methods are insufficient for digital DOb design.
Simulation and experiments confirm the proposed analysis accuracy.
Abstract
This paper shows that the design constraints of the Disturbance Observer (DOb) based robust motion control systems become stricter when they are implemented using computers or microcontrollers. The stricter design constraints put new upper bounds on the plant-model mismatch and the bandwidth of the DOb, thus limiting the achievable robustness against disturbances and the phase-lead effect in the inner-loop. Violating the design constraints may yield severe stability and performance issues in practice; therefore, they should be considered in tuning the control parameters of the robust motion controller. This paper also shows that continuous-time analysis methods fall-short in deriving the fundamental design constraints on the nominal plant model and the bandwidth of the digital DOb. Therefore, we may observe unexpected stability and performance issues when tuning the control parameters…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdaptive Control of Nonlinear Systems · Teleoperation and Haptic Systems · Iterative Learning Control Systems
