Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/161314
Title: | Nonlinear predictive motion control for autonomous mobile robots considering active fault-tolerant control and regenerative braking | Authors: | Hang, Peng Lou, Baichuan Lv, Chen |
Keywords: | Engineering::Mechanical engineering | Issue Date: | 2022 | Source: | Hang, P., Lou, B. & Lv, C. (2022). Nonlinear predictive motion control for autonomous mobile robots considering active fault-tolerant control and regenerative braking. Sensors, 22(10), 3939-. https://dx.doi.org/10.3390/s22103939 | Project: | ICP1900093 | Journal: | Sensors | Abstract: | To further advance the performance and safety of autonomous mobile robots (AMRs), an integrated chassis control framework is proposed. In the longitudinal motion control module, a velocity-tracking controller was designed with the integrated feedforward and feedback control algorithm. Besides, the nonlinear model predictive control (NMPC) method was applied to the four-wheel steering (4WS) path-tracking controller design. To deal with the failure of key actuators, an active fault-tolerant control (AFTC) algorithm was designed by reallocating the driving or braking torques of the remaining normal actuators, and the weighted least squares (WLS) method was used for torque reallocation. The simulation results show that AMRs can advance driving stability and braking safety in the braking failure condition with the utilization of AFTC and recapture the braking energy during decelerations. | URI: | https://hdl.handle.net/10356/161314 | ISSN: | 1424-8220 | DOI: | 10.3390/s22103939 | Schools: | School of Mechanical and Aerospace Engineering | Rights: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | MAE Journal Articles |
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sensors-22-03939.pdf | 13.2 MB | Adobe PDF | ![]() View/Open |
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