A Reinforcement Learning-Based Robust Path Tracking Controller for Nonlinear Mobile Robot Dynamics under Uncertainty

Safety-Aware Optimal Tracking Control of Stochastic Systems With Obstacle Avoidance via Adaptive Dynamic Programming.

Safe trajectory tracking for nonlinear stochastic systems operating in obstacle-cluttered environments remains a significant challenge, as random disturbances and obstacle-induced constraints can simultaneously degrade tracking accuracy and threaten system safety. To overcome this issue, this article develops a safety-...

Adaptive MPC Path Tracking Control for Autonomous Vehicles

This work introduces a Model Predictive Control (MPC) path tracking controller, which is developed to boost robustness, tracking precision, and vehicle stability when navigating high-speed and high-curvature driving scenarios. First, a 3-degree-of-freedom (3-DOF) dynamic model of the vehicle is established to serve as...

Dual-Loop Adaptive Sliding Mode and RBF Neural Network Control for Robust Trajectory Tracking of Wheeled Mobile Robots

Trajectory tracking of wheeled mobile robots (WMRs) is significantly affected by kinematic parameter uncertainties, dynamic model uncertainties, and external disturbances, particularly under high-speed operating conditions, where conventional controllers often suffer from degraded tracking accuracy or even loss of clos...

Hierarchical Control Architecture for Trajectory Tracking of Differential-Drive Nonholonomic Robots Using LQG-Based Velocity Control

The results demonstrate accurate wheel velocity regulation, effective attenuation of encoder measurement noise, consistent tracking of the prescribed trajectories, and experimentally measurable trade-offs between spatial tracking accuracy, wheel velocity regulation, and control effort under different trajectory geometr...

Reinforcement Learning-Based Predefined-Time Consensus Control for Uncertain Multirobot Systems with Improved Prescribed Performance Function

Lifting-based nonlinear model predictive control for high-performance mobile robot tracking

Nonlinear model predictive control (NMPC) is widely used in robotics due to its ability to handle nonlinear dynamics and explicitly enforce state and input constraints by solving an optimal control problem online. However, standard NMPC implementations typically rely on direct time discretization and evaluate costs and...