Obstacle Avoidance Path Planning and Robust Tracking Control of Quadrotor UAVs under Uncertain Disturbances

Abstract

In this paper, obstacle-avoidance path planning and robust trajectory tracking are addressed for quadrotor UAVs in the presence of model uncertainties and pulse disturbances. By using the Newton–Euler formulation, a 6-DOF nonlinear dynamic model is derived to construct a constrained 3D path-planning problem. To overcome the shortcomings of conventional PSO in terms of premature convergence and poor path smoothness, a hybrid adaptive multi-topology PSO with intelligent strategy learning and cubic B-spline smoothing, termed as HAMT-AISL-PSO, is proposed, which jointly enhances the global search ability and path executability. In the aspect of tracking control, a linear active disturbance rejection control (LADRC) scheme with a linear extended state observer is designed to estimate and compensate the total disturbances in real time. Simulation results on a cluttered 3D environment demonstrate that, compared with the standard PSO, the average cost function and average path length are reduced by 25.20% and 20.45%, respectively. Under a 3D spiral reference and pulse torque disturbances, LADRC achieves faster transient response and near-zero steady-state error with much smaller tracking deviations than PID, hence verifying the effectiveness of the proposed planning-and-control framework.