Adaptive Neural Cooperative Control of Multirobot Systems With Input Quantization

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Cooperative control of multi-robot systems is challenging due to limited sensing and input quantization.
• Existing methods often struggle to guarantee stability and performance under such constraints.

Purpose of the Study:

• To develop an adaptive neural cooperative control scheme for mobile robots with limited sensing range and input quantization.
• To ensure stability and performance of the multi-robot system using dynamic surface control techniques.

Main Methods:

• Transformation of the robotic system into a fully actuated system using a transverse function.
• Development of an adaptive neural cooperative controller utilizing radial basis function neural networks and connectivity preservation.
• Incorporation of dynamic surface control to handle input quantization effects, specifically hysteresis.

Main Results:

• The proposed control scheme guarantees semi-globally uniformly ultimately bounded closed-loop signals.
• Tracking errors are maintained within predefined domains, ensuring desired performance.
• Simulation results demonstrate the feasibility and efficiency of the developed control strategy.

Conclusions:

• The adaptive neural cooperative control scheme effectively addresses challenges of limited sensing and input quantization in multi-robot systems.
• The dynamic surface control technique combined with neural networks provides robust and stable robot coordination.
• The findings offer a viable solution for practical applications requiring precise mobile robot group control.