Designing an Adaptive Controller for 3D Overhead Cranes Using Hierarchical Sliding Mode and Neural Network

This paper proposes an adaptive control system for uncertain overhead cranes on the basis of hierarchical sliding mode approach combined with radial basis function (RBF) neural network. A sliding surface is defined by linearly combining two sub-manifolds. A RBF neural network is adopted to approximate the unknown dynamic model. The control law is designed to ensure the stability of sliding surfaces while an adaptation mechanism for updating weight matrices of neural network is derived from a candidate of Lyapunov function. Simulation results show the effectiveness of the proposed control scheme.