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Updated: Jul 7, 2026
11:18
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.
IEEE Transactions on Neural Networks
|February 7, 2008
Summary
This study presents a novel control structure integrating kinematic control and neural network (NN) computed-torque control for nonholonomic mobile robots, ensuring stability and adaptability to disturbances.
Area of Science:
- Robotics
- Control Systems
- Artificial Intelligence
Background:
- Nonholonomic mobile robots present unique control challenges due to kinematic constraints.
- Existing control methods may struggle with unmodeled dynamics and external disturbances.
Purpose of the Study:
- To develop an integrated control structure for nonholonomic mobile robots.
- To incorporate a neural network (NN) computed-torque controller for enhanced robustness.
- To address trajectory tracking, path following, and stabilization tasks.
Main Methods:
- A combined kinematic/torque control law was developed using backstepping techniques.
- Lyapunov theory was employed to guarantee system stability.
- On-line neural network weight tuning algorithms were utilized without requiring offline learning.
Main Results:
- The proposed control algorithm effectively handles trajectory tracking, path following, and stabilization.
- The neural network controller successfully manages unmodeled bounded disturbances and unstructured dynamics.
- Small tracking errors and bounded control signals were achieved.
Conclusions:
- The integrated control structure offers a robust and adaptable solution for nonholonomic mobile robot navigation.
- The use of on-line NN tuning simplifies implementation while maintaining performance.
- This approach enhances the practical applicability of advanced control strategies for mobile robots.
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