Adaptive tracking control of a rigid arm robot based on extreme learning machine
Li Ju · Dianji yu kongzhi xuebao · 2015
Based on extreme learning machine( ELM),two adaptive neural control algorithms for rigid arm robot system were presented. ELM for signle-hidden layer feedforward neural networks( SLFNs),which randomly chooses hidden node parameters and analytically determines the output weights of SLFNs,tends to provide good generalized performance at extremely fast learning speed. Within these adaptive control algorithms,ELM was employed to approximation the plant's unknown nonlinear function and robust control term was used to compensate for approximation error. Parameter adaptive laws and robust control term of ELM controllers were derived based on Lyapunov stability analysis so that global stability and asymptotic convergence to zero of tracking errors can be guaranteed. Futhermore,two adaptive controllers do not depend on any parameter initialization conditions and relax the requirement of bounding parameter values. The proposed adaptive ELM control algorithms were then applied to a tracking control instance for two-link rigid arm robot and compared with existing radial basis function( RBF) neural control algorithms. Simulation results show that ELM controllers have good tracking performance and demonstrate the effectiveness of the proposed control algorithms.