Real-Time Feasible Nonlinear Model Predictive Control for Waypoint Following of a Chain-Based Mobile Robot

다국어 초록 (Multilingual Abstract)

In this paper, a real-time nonlinear model predictive control (NMPC) approach is proposed for high-precision waypoint following in mobile robotswith chain-based drive systems. The NMPC-based control setup profits from predictive capabilities and adeptness at handling constraints to deal with the challenges of multi-constrained control problems. An extended Kalman filter (EKF) is integrated into the system framework to achieve real-time estimation for unobservable system states and noisy velocity measurements, thereby enhancing the robustness of the feedback loop. The proposed approach is compared with heuristic methods such as pure pursuit (PP), multi-goal stabilization (MGS), classical sliding mode control (SMC), as well as a standard NMPC formulation with terminal cost (T-NMPC) to showcase improvements in waypoint following performance, disturbance rejection, and time-varying constraints. Based on hardware tests, an extensive real-time feasibility analysis is presented, which is essential for assessing the suitability of the approach for execution on the target hardware platform. The NMPC framework demonstrates improved waypoint following, robustness, and error minimization through MATLAB/Simulink simulations with adaptive variation to operating conditions. The robustness ofNMPCis further confirmed through extensive experiments on a real chain-based mobile robot platform developed under ROS2 as a software framework, where R2 values reached up to 68.18% in simulations and 62.01% in real-world experiments, compared to 51.42% and 57.54% obtained with PP. Stress-testing in the real-time feasibility analysis showed a runtime of approximately 50 ms on target hardware, while the nested-NMPC (N-NMPC) variant achieved runtimes of around 20 ms in real-world experiments due to warm-starting of the optimizer. These findings confirm NMPC as a benchmark in model-based approaches for achieving higher accuracy and control in waypoint following of chain-based robots, thereby contributing to future directions of research in this area.