Terminal Sliding Mode-Based Consensus Tracking Control for Networked Uncertain Mechanical Systems on Digraphs.

Area of Science:

• Robotics and Control Systems
• Networked Systems Engineering
• Artificial Intelligence in Control

Background:

• Networked mechanical systems often face challenges with unknown dynamics and communication delays.
• Achieving consensus and tracking control in systems with directed communication topologies is complex.
• Existing terminal sliding-mode control methods have limitations in handling system uncertainties and distributed implementation.

Purpose of the Study:

• To develop a finite-time consensus tracking control strategy for networked uncertain mechanical systems.
• To address systems with unknown and potentially different dynamics across nodes.
• To enable fully distributed control implementation over directed graphs.

Main Methods:

• A novel terminal sliding-mode-based cooperative control scheme is proposed.
• Neural networks are employed at each node to approximate unknown local system dynamics.
• The control strategy is designed for fully distributed implementation on digraphs.

Main Results:

• The proposed control scheme guarantees finite-time convergence of tracking errors to an arbitrarily small bound around zero.
• The method effectively handles systems with unknown and varying dynamics.
• The approach extends existing consensus control analysis to directed graph scenarios.

Conclusions:

• The developed control method provides an effective solution for finite-time consensus tracking in networked uncertain mechanical systems.
• The use of neural networks and distributed implementation overcomes limitations of previous approaches.
• The findings are validated through simulations on networked robot manipulators.