Experimental Validation of Linear and Nonlinear MPC on an Articulated Unmanned Ground Vehicle

TL;DR

This study experimentally compares linear and nonlinear model predictive control strategies for an articulated unmanned ground vehicle, demonstrating that nonlinear MPC offers better accuracy with acceptable computation times, while linear MPC is more computationally efficient.

Contribution

The paper introduces a real-time implementation of NMHE-NMPC and benchmarks it against ISL-LMPC on an articulated UGV, highlighting performance and computational trade-offs.

Findings

NMHE-NMPC outperforms ISL-LMPC in trajectory tracking accuracy.

Real-time iteration scheme enables feasible nonlinear MPC implementation.

Linear MPC achieves comparable results with lower computational cost.

Abstract

This paper focuses on the trajectory tracking control problem for an articulated unmanned ground vehicle. We propose and compare two approaches in terms of performance and computational complexity. The first uses a nonlinear mathematical model derived from first principles and combines a nonlinear model predictive controller (NMPC) with a nonlinear moving horizon estimator (NMHE) to produce a control strategy. The second is based on an input-state linearization (ISL) of the original model followed by linear model predictive control (LMPC). A fast real-time iteration scheme is proposed, implemented for the NMHE-NMPC framework and benchmarked against the ISL-LMPC framework, which is a traditional and cheap method. The experimental results for a time-based trajectory show that the NMHE-NMPC framework with the proposed real-time iteration scheme gives better trajectory tracking performance than the ISL-LMPC framework and the required computation time is feasible for real-time applications. Moreover, the ISL-LMPC produces results of a quality comparable to the NMHE-NMPC framework at a significantly reduced computational cost.