A robust interpolated model predictive control based on recurrent neural networks for a nonholonomic differential-drive mobile robot with quasi-LPV representation: computational complexity and conservatism

This paper presents an improved Model Predictive Control (MPC) for path tracking of a nonholonomic mobile robot with a differential drive. Nonlinear dynamics and nonholonomic constraints make the optimisation problem of MPC for the robot challenging. Nonlinear dynamics of the robots are expressed by...

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Bibliographic Details
Published in:International journal of systems science Vol. 55; no. 15; pp. 3257 - 3271
Main Authors: Hadian, Mohsen, Zhang, W. J., Etesami, Danial
Format: Journal Article
Language:English
Published: London Taylor & Francis 17.11.2024
Taylor & Francis Ltd
Subjects:
Algorithms
Computing costs
Control stability
Control theory
linear parameter varying
Model predictive control
Nonlinear control
nonlinear control systems
Nonlinear dynamics
Optimization
Path tracking
Predictive control
Recurrent neural networks
Robot control
Robust control
Stability augmentation
ISSN:0020-7721, 1464-5319
Online Access:Get full text
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Summary:This paper presents an improved Model Predictive Control (MPC) for path tracking of a nonholonomic mobile robot with a differential drive. Nonlinear dynamics and nonholonomic constraints make the optimisation problem of MPC for the robot challenging. Nonlinear dynamics of the robots are expressed by a Linear Parameter Varying (LPV), and a Recurrent Neural Network (RNN) solves the constrained optimisation problem, providing optimal velocities. Moreover, an interpolation-based approach has been introduced to augment the region of attraction. The algorithm ensures stability in the presence of bounded disturbances through the inclusion of free control moves in the control law. The controller efficiency has been evaluated in two scenarios in a hospital setting. The simulation results illustrate that the proposed method performs better than nonlinear MPC and standard LPV-based MPC in terms of computational cost, disturbance rejection, and region of attraction.
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ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2024.2367711