Robust Asymptotic Stabilization of Uncertain Nonlinear Systems Using Artificial Neural Networks with Application to Power Systems
The problem of robust stabilization for uncertain nonlinear systems is considered in this paper. The controlled systems are not restricted to the strict feedback form any more. The robust stabilization controller is designed based on Backstepping approach with using Artificial Neural Networks (ANN) to account for the uncertain terms. A new adaptive algorithm is proposed to update the weights of ANN such that all signals in the closed-loop systems are bounded and the states are convergent asymptotically to the equilibrium through the proposed controller. A speed governor is designed for single-machine infinite-bus system based on the design scheme proposed in this paper and the simulation results illustrate the utility of the proposed scheme.
Uncertain Nonlinear Systems Artificial Neural Networks Backstepping Power Systems
Ying Zhou Qiang Zang
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China School of Automation, Southeast University, Nanjing 210096, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
814-818
2009-06-17(万方平台首次上网日期,不代表论文的发表时间)