English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This research work is focused on solving the practical problems that arise in trajectory tracking control of Hilare robots when the conventional Linear Time-Variant Controller (LTVC) is employed. The trajectory tracking of a Hilare robot on a flat 2D ground is considered. The left and the right wheel speeds of the robot are considered as the control inputs. It is assumed that the control inputs have a maximum affordable limit. The practical problems arise when the LTVC calculates control inputs that are beyond the affordable limit, and hence are truncated when applied to the real robot. The application of truncated control inputs causes system instability (and control failure) in practical situations. The failure of the LTVC when the control inputs are saturated is demonstrated by experimentation on a real robot. The Nonlinear Model Predictive Controller (NMPC) is proposed for eliminating the system instability due to an input saturation. The maximum affordable wheel speeds of the robot are implemented as constraints for the control input calculation by the NMPC. So, the calculated control inputs never exceed the affordable limit. The experimentation on a robot with the NMPC exhibits that the system instability due to the input saturation is eliminated.

Nonlinear Model Predictive Control versus Linear Time-Variant Control for Mobile Robots Prone to Input Saturation