AI Article Synopsis
- Mobile robots often face localization fluctuations that lead to issues like jittering and poor trajectory tracking, particularly in dynamic environments, which common controllers fail to address effectively.
- The paper introduces an adaptive model predictive control (MPC) method that improves localization assessment using fuzzy logic and modifies the kinematics model to handle external disturbances, enhancing computation efficiency.
- The proposed MPC shows significant improvements over traditional PID controllers, reducing tracking distance and angle errors by 74.3% and 95.3%, respectively, as demonstrated through real-life robot experiments.
Article Abstract
Mobile robots are widely employed in various fields to perform autonomous tasks. In dynamic scenarios, localization fluctuations are unavoidable and obvious. However, common controllers do not consider the impact of localization fluctuations, resulting in violent jittering or poor trajectory tracking of the mobile robot. For this reason, this paper proposes an adaptive model predictive control (MPC) with an accurate localization fluctuation assessment for mobile robots, which balances the contradiction between precision and calculation efficiency of mobile robot control. The distinctive features of the proposed MPC are three-fold: (1) Integrating variance and entropy-a localization fluctuation estimation relying on fuzzy logic rules is proposed to enhance the accuracy of the fluctuation assessment. (2) By using the Taylor expansion-based linearization method-a modified kinematics model that considers that the external disturbance of localization fluctuation is established to satisfy the iterative solution of the MPC method and reduce the computational burden. (3) An improved MPC with an adaptive adjustment of predictive step size according to localization fluctuation is proposed, which alleviates the disadvantage of a large amount of the MPC calculation and improves the stability of the control system in dynamic scenes. Finally, verification experiments of the real-life mobile robot are offered to verify the effectiveness of the presented MPC method. Additionally, compared with PID, the tracking distance and angle error of the proposed method decrease by 74.3% and 95.3%, respectively.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006979 | PMC |
| http://dx.doi.org/10.3390/s23052501 | DOI Listing |
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