Remote path-following control for a holonomic Mecanum-wheeled robot in a resource-efficient networked control system

Area of Science:

• Robotics and Control Systems
• Wireless Networked Control Systems (WNCS)
• Autonomous Vehicle Navigation

Background:

• Wireless networked control systems (WNCS) face challenges like resource limitations, time-varying delays, and packet loss.
• Efficient control strategies are crucial for autonomous vehicles to maintain path-following accuracy under WNCS constraints.

Purpose of the Study:

• To develop a resource-efficient control structure for remote path-following control of autonomous vehicles.
• To address challenges in WNCS, including delays, packet dropouts, and disorder.
• To maintain satisfactory path-following performance while minimizing resource consumption.

Main Methods:

• A novel control structure combining Kalman filtering, non-uniform dual-rate sampling, periodic event-triggered communication, and prediction-based control.
• Implementation of a non-uniform dual-rate extended Kalman filter (NUDREKF) with an h-step ahead prediction stage.
• Periodic event-triggered conditions for algorithmic implementation ensuring exponentially mean-square bounded prediction error.

Main Results:

• Achieved significant resource usage reduction (up to 93%) compared to time-triggered control solutions in simulations.
• Demonstrated robust path-following behavior for a holonomic Mecanum-wheeled robot.
• Validated the effectiveness of the proposed control structure through Simscape Multibody simulations and experimental testing.

Conclusions:

• The proposed resource-efficient control structure effectively balances resource management and path-following performance in WNCS for autonomous vehicles.
• The NUDREKF with prediction capabilities successfully handles WNCS drawbacks like delays and packet loss.
• The method offers a practical and validated solution for enhancing autonomous vehicle control in wireless environments.