Trajectory Tracking of a Pioneer P3DX Robot Using Model Predictive Control
DOI:
https://doi.org/10.29207/resti.v10i3.7383Keywords:
CoppeliaSim, model predictive control, NMPC, pioneer P3DX, SLSQP, trajectory trackingAbstract
Trajectory tracking of nonholonomic mobile robots using Model Predictive Control (MPC) has attracted significant attention due to its capability to explicitly handle system dynamics and actuator constraints within an optimization framework. However, limited studies specifically investigate the computational consistency of constrained Nonlinear Model Predictive Control (NMPC) under fixed-sampling closed-loop simulation environments. This study presents the implementation of a constrained NMPC framework for trajectory tracking of a Pioneer P3DX differential-drive robot using a discrete-time kinematic model and Sequential Least Squares Quadratic Programming (SLSQP) optimization. The controller is integrated with the CoppeliaSim environment through a ZeroMQ based communication interface operating at a fixed sampling time of 0.1 s. Controller performance is evaluated using circular, lemniscate, and square reference trajectories to analyze predictive behavior under varying curvature conditions. The simulation results demonstrate cumulative Root Mean Square Error (RMSE) values of 0.3868 m for the circular trajectory, 0.0942 m for the lemniscate trajectory, and 0.4911 m for the square trajectory. In the square trajectory case, the controller autonomously reduces linear velocity before sharp corners and generates smooth feasible turning maneuvers while satisfying actuator constraints. These behaviors indicate that the implemented NMPC framework is capable of maintaining stable and constraint-aware trajectory tracking performance within a structured closed-loop simulation environment. The study, therefore, provides a preliminary validation of computational feasibility prior to hardware level implementation.
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