Particle Swarm-Optimized Neural Network Hierarchical Sliding Mode Control for Variable-Length Double-Pendulum Cranes

In the anti-sway control of variable-length double-pendulum gantry cranes, traditional sliding mode control relies on high switching gains, which can cause chattering. Additionally, the introduction of neural networks presents challenges in tuning high-dimensional parameters. To address these issues, this study proposes an adaptive hierarchical sliding mode control strategy based on an RBF neural network and particle swarm optimization. First, a low-energy-dissipation dynamic model is established without the small-angle assumption. Second, a composite hierarchical sliding surface is designed to achieve multi-objective decoupling, and an RBF neural network is utilized to approximate the system’s unknown dynamics online, thereby reducing switching gains and suppressing chattering. The asymptotic stability of the closed-loop system is proven based on Lyapunov theory. Finally, a particle swarm optimization algorithm is introduced to achieve automated, high-precision matching of high-dimensional controller parameters. Simulation results indicate that the control method designed in this paper can achieve automatic matching of high-dimensional parameters, effectively resolving the chattering issue in sliding mode control. Furthermore, under wide-range parameter perturbations and external multi-source disturbances, the controller exhibits strong robustness and demonstrates excellent positioning and anti-chattering performance.