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Robust Practical Point Stabilization of a Nonholonomic Mobile Robot Using Neural Networks

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Abstract

A control structure that makes possible the integration of a kinematiccontroller and a neural network (NN) computed-torque controller fornonholonomic mobile robots is presented. A combined kinematic/torque controllaw is developed and stability is guaranteed by Lyapunov theory. Thiscontrol algorithm is applied to the practical point stabilization problemi.e., stabilization to a small neighborhood of the origin. The NN controllercan deal with unmodeled bounded disturbances and/or unstructured unmodeleddynamics in the vehicle. On-line NN weight tuning algorithms that do notrequire off-line learning yet guarantee small tracking errors and boundedcontrol signals are utilized.

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Authors and Affiliations

  1. Automation and Robotics Research Institute, The University of Texas at Arlington, 7300 Jack Newell Blvd. South, Fort Worth, Texas, 76118-7115, U.S.A.

    Rafael Fierro & Frank L. Lewis

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  1. Rafael Fierro
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  2. Frank L. Lewis
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Fierro, R., Lewis, F.L. Robust Practical Point Stabilization of a Nonholonomic Mobile Robot Using Neural Networks. Journal of Intelligent and Robotic Systems 20, 295–317 (1997). https://doi.org/10.1023/A:1007916529436

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