Experimental Validation of Linear and Nonlinear MPC on an Articulated Unmanned Ground Vehicle
This work addresses control challenges for unmanned ground vehicles, offering practical insights for robotics applications, but it is incremental as it builds on existing MPC methods.
The paper tackled trajectory tracking control for an articulated unmanned ground vehicle by comparing nonlinear and linear MPC approaches, finding that the nonlinear method with a real-time iteration scheme provided better tracking performance while the linear method offered comparable quality at significantly lower computational cost.
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.