Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function

作者：Beibei Ren, Shuzhi Sam Ge, Keng Peng Tee, Tong Heng Lee · 发表于：IEEE Transactions on Neural Networks · 年份：2010 · DOI：10.1109/tnn.2010.2047115 · 被引用次数：1075 · 研究领域：Adaptive Control of Nonlinear Systems、Adaptive Dynamic Programming Control、Neural Networks and Applications

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.