Adaptive Formation Control of Electrically Driven Nonholonomic Mobile Robots With Limited Information.

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Electrically driven nonholonomic mobile robots often face challenges with limited sensory information, particularly velocity measurements.
• Achieving stable and collision-free formations in multi-robot systems requires robust control strategies that can adapt to uncertainties.

Purpose of the Study:

• To develop a leader-follower-based adaptive formation control method for mobile robots with limited information.
• To address the unavailability of velocity measurements and actuator saturation in the control system.

Main Methods:

• An adaptive observer was designed to estimate unmeasured velocities.
• A formation control strategy was developed incorporating the adaptive observer for desired formation and collision avoidance.
• A neural network was employed to compensate for actuator saturation.
• A projection algorithm was used for leader velocity estimation.
• Lyapunov theory was utilized to prove system stability.

Main Results:

• The proposed adaptive observer successfully estimated velocity information without direct measurement.
• The formation control strategy achieved the desired formation configuration while ensuring collision avoidance.
• The neural network effectively compensated for actuator saturation, improving control performance.
• Stability analysis confirmed that all closed-loop system errors are uniformly ultimately bounded.

Conclusions:

• The leader-follower adaptive formation control method is effective for nonholonomic mobile robots with limited information.
• The integration of adaptive observers, neural networks, and projection algorithms provides a robust solution for complex control challenges.
• The proposed system demonstrates reliable performance in achieving formations and maintaining safety through collision avoidance.