IEEE Trans Cybern. 2016 May;46(5):1106-17. doi: 10.1109/TCYB.2015.2423635. Epub 2015 Apr 30.
In this paper, an extreme learning control (ELC) framework using the single-hidden-layer feedforward network (SLFN) with random hidden nodes for tracking an unmanned surface vehicle suffering from unknown dynamics and external disturbances is proposed. By combining tracking errors with derivatives, an error surface and transformed states are defined to encapsulate unknown dynamics and disturbances into a lumped vector field of transformed states. The lumped nonlinearity is further identified accurately by an extreme-learning-machine-based SLFN approximator which does not require a priori system knowledge nor tuning input weights. Only output weights of the SLFN need to be updated by adaptive projection-based laws derived from the Lyapunov approach. Moreover, an error compensator is incorporated to suppress approximation residuals, and thereby contributing to the robustness and global asymptotic stability of the closed-loop ELC system. Simulation studies and comprehensive comparisons demonstrate that the ELC framework achieves high accuracy in both tracking and approximation.
本文提出了一种基于单隐层前馈神经网络（SLFN）的极限学习控制（ELC）框架，该框架使用随机隐节点来跟踪遭受未知动力学和外部干扰的无人水面车辆。通过将跟踪误差与导数结合起来，定义了一个误差曲面和变换状态，将未知动力学和干扰封装到变换状态的集中矢量场中。通过基于极限学习机的 SLFN 逼近器，可以准确地识别集中非线性，该逼近器不需要先验系统知识，也不需要调整输入权重。仅需要通过自适应投影律更新 SLFN 的输出权重，该律源自 Lyapunov 方法。此外，还结合了误差补偿器以抑制逼近残差，从而提高了闭环 ELC 系统的鲁棒性和全局渐近稳定性。仿真研究和综合比较表明，ELC 框架在跟踪和逼近方面都具有很高的精度。
