Robust Trajectory Tracking Error Model-Based Predictive Control for Unmanned Ground Vehicles
This paper introduces a novel control approach specifically designed for unmanned ground vehicles (UGVs), emphasizing a robust trajectory tracking error model-based predictive control. The focus is on the integration of model predictive control (MPC) with feedforward and robust control actions to enhance trajectory tracking capabilities, particularly for tractor-trailer systems.
Overview
The primary challenge addressed by the paper is the control of a tractor-trailer system to follow both linear and curvilinear trajectories with minimal tracking error. Traditional methods like PID controllers have proven inadequate for the complex dynamics of multi-input multi-output (MIMO) systems, such as autonomous vehicles. The proposed approach utilizes a linear model predictive controller framework, integrating feedforward and robust control elements to maintain effective tracking even under adverse conditions like uneven terrain.
Methodology
The control architecture incorporates:
- Feedback Control via MPC: This component is responsible for minimizing the error between the reference trajectory and the actual position of the tractor and trailer, using an optimization framework formulated at each sampling instance. A convex optimization problem is solved within the MPC framework, ensuring real-time applicability.
- Feedforward Control: Essential for deriving control actions accounting for reference trajectories, this component compensates for known dynamic elements.
- Robust Control: Inspired by tube-based MPC approaches, it addresses model mismatches and disturbances through dynamic error correction, ensuring system stability around the nominal trajectory.
The error model and control strategies are evaluated using a kinematic tricycle model, accommodating the complex interaction between tractor and trailer dynamics.
Key Findings
Experimental results demonstrate the controller's capability in achieving low tracking errors for both straight and curved trajectories. Notably:
- The mean Euclidean distance errors for straight lines are approximately 23.49 cm for the tractor and 21.21 cm for the trailer.
- For curved lines, these errors increase to 39.82 cm and 36.21 cm, respectively.
- The computational efficiency is evident, with computation times around 1.1 ms, substantially lower than alternative NMPC implementations.
Implications and Future Developments
The implications of this research extend to the practical domain of agricultural robotics, where precision and robustness are critical. By minimizing computation times and maximizing accuracy, autonomous systems can increasingly operate under varied environmental conditions with reliability. The strategy of integrating MPC with feedforward and robust control actions highlights a pathway to enhance predictive control models for other applications.
Future developments could focus on scaling the approach to larger tractor-trailer systems or adapting the control architecture to other autonomous vehicle applications in non-agricultural settings. Enhancements in adaptive model components to accommodate dynamic environmental changes, like soil variability, could further refine tracking accuracy and system resilience.
In summary, this paper presents a robust, efficient framework for trajectory tracking in UGVs, providing insightful control mechanisms for maintaining path fidelity under diverse conditions.