Event-triggered MPC-PID based trajectory tracking control for differential drive mobile robots

Area of Science:

• Robotics
• Control Systems Engineering
• Automation

Background:

• Periodic Model Predictive Control (MPC) for differential-drive mobile robots demands high computational resources due to continuous online optimization.
• This computational burden is often unnecessary during periods of near-steady state tracking, leading to inefficiencies.

Purpose of the Study:

• To develop an event-triggered hierarchical MPC-PID framework (ET-MPC-PID) that reduces the computational load of the outer-loop optimization.
• To effectively manage actuator-side execution effects, including lag and saturation, within the control system.

Main Methods:

• Implemented an event-triggered outer MPC that updates velocity setpoints based on a normalized event condition or maximum holding interval.
• Utilized a fixed-period PID-form velocity servo with conditional integration to regulate the actuator channel.
• Formulated the system to differentiate between holding-induced discrepancies and actuator-side mismatches, providing practical boundedness analysis.

Main Results:

• The ET-MPC-PID framework reduced the Quadratic Programming (QP) call rate to 16.9% and 22.3% compared to periodic MPC.
• ET-MPC-PID demonstrated superior performance over a no-PID event-triggered variant in simulations.
• Analyses confirmed the accuracy-computation trade-off and empirical robustness under sampled execution uncertainty.

Conclusions:

• The ET-MPC-PID framework offers a computationally efficient alternative to traditional periodic MPC for mobile robot control.
• This approach effectively balances control accuracy with reduced computational demand, enhancing practical applicability.
• The event-triggered strategy proves robust and accurate, even with inherent sampled execution uncertainties.