Ren Beibei, Ge Shuzhi Sam, Tee Keng Peng, Lee Tong Heng
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576.
IEEE Trans Neural Netw. 2010 Aug;21(8):1339-45. doi: 10.1109/TNN.2010.2047115. Epub 2010 Jul 1.
In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: 1) for any initial compact set, how to determine a priori the compact superset, on which NN approximation is valid; and 2) how to ensure that the arguments of the unknown functions remain within the specified compact superset. By ensuring boundedness of the BLF, we actively constrain the argument of the unknown functions to remain within a compact superset such that the NN approximation conditions hold. The semiglobal boundedness of all closed-loop signals is ensured, and the tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.
在本简报中，针对一类存在未知函数的输出反馈非线性系统，提出了自适应神经控制方法。未知函数通过仅使用输出测量值的在线神经网络（NN）控制来处理。引入了障碍李雅普诺夫函数（BLF）来解决神经控制领域中两个开放且具有挑战性的问题：1）对于任何初始紧致集，如何先验地确定紧致超集，使得NN逼近在该超集上有效；2）如何确保未知函数的自变量保持在指定的紧致超集内。通过确保BLF的有界性，我们积极地将未知函数的自变量约束在一个紧致超集内，使得NN逼近条件成立。确保了所有闭环信号的半全局有界性，并且跟踪误差收敛到零的邻域。仿真结果证明了所提方法的有效性。
