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Updated: Jul 7, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Control of a nonholonomic mobile robot using neural networks
1Escuela Politécnica Nacional, Facultad de Ingeniería Elééctrica, Casilla Postal 17-01-2759, Quito, Ecuador.
Abstract:
A control structure that makes possible the integration of a kinematic controller and a neural network (NN) computed-torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping and stability is guaranteed by Lyapunov theory. This control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following, and stabilization about a desired posture. Moreover, the NN controller proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. On-line NN weight tuning algorithms do no require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.
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