A Neural Network-Based State-Constrained Control Strategy for Underactuated Aerial Transportation Systems Within Narrow Workspace

The aerial transportation system belongs to a symmetrical system and has recently garnered increasing attention from researchers due to its broad application range and convenient operation. The control difficulty of the aerial transportation system lies in the fact that the load is not directly actuated, posing a significant challenge for state-constrained control. Taking the motion of an unmanned aerial vehicle (UAV) suspension transportation system within complex pipelines as an example, this paper employs the the swept volume signed distance field (SVSDF) method to search for state boundaries, which is an aspect not considered or elaborated in many state-constrained control approaches. Furthermore, adaptive state-constrained control based on the radial basis function (RBF) neural network is utilized for the case of experiencing unknown air resistance. The convergence of the proposed method for underactuated and actuated state variables is theoretically demonstrated based on the Lyapunov technique. Compared with existing methods, the error integral index demonstrates that the proposed method displays better convergence capability in the simulation section when considering state constraints under disturbance and air resistance.