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Application of Iterative LQR on a Mobile Robot With Simple Dynamics (2404.18312v1)

Published 28 Apr 2024 in eess.SY and cs.SY

Abstract: The aim in this paper is to apply the iLQR, iterative Linear Quadratic Regulator, to control the movement of a mobile robot following an already defined trajectory. This control strategy has proven its utility for nonlinear systems. As follows up, this work intends to concertize this statement and to evaluate the extent to which the performance is comparatively improved against the ordinary, non-iterative LQR. The method is applied to a differential robot with non-holonomic constraints. The mathematical equations, resulting description and the implementation of this method are explicitly explained, and the simulation studies are conducted in the Matlab and Simulink environment.

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References (5)
  1. Ooi. Rich Chi, “Balancing a Two-Wheeled Autonomous Robot,” B.Sc. thesis, Faculty of Engineering and Mathematical Sciences, University of Western Australia, Crawley, Wesrern Australia, 2003.
  2. Travis DeWolf, “THE ITERATIVE LINEAR QUADRATIC REGULATOR ALGORITHM,”[Online]. Available: https://studywolf.wordpress.com/2016/02/03/the-iterative-linear-quadratic-regulator-method/ Accessed on: December, 2020.
  3. Zhang, Hj., Gong, Jw., Jiang, Y., “An iterative linear quadratic regulator based trajectory tracking controller for wheeled mobile robot,” in J. Zhejiang Univ. - Sci. C 13, 2012, pp. 593–600., doi: 10.1631/jzus.C1100379.
  4. Travis DeWolf, “LINEAR-QUADRATIC REGULATION FOR NON-LINEAR SYSTEMS USING FINITE DIFFERENCES,”[Online]. Available: https://studywolf.wordpress.com/2015/11/10/ linear-quadratic-regulation-for-non-linear-systems-using-finite-differences/ Accessed on: December, 2020.
  5. Y. Tassa, T. Erez and E. Todorov, “Synthesis and stabilization of complex behaviors through online trajectory optimization,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, Portugal, 2012, pp. 49064913., doi: 10.1109/IROS.2012.6386025.

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