Learning from Adaptive Neural Control with Predefined Performance for a Class of Nonlinear Systems

摘要：

　　This paper presents a neural learning scheme for a class of single-input-single-output(SISO)uncertain nonlinear systems.The proposed scheme achieves knowledge acquisition,storage and reuse of the unknown system dynamics as well as the predefined tracking error behavior bound.Using the novel transformed function,the constrained tracking control problem of the original nonlinear system is transformed into the stabilization problem of an augmented system.By combining a filter tracking error with radial basis function(RBF)neural networks(NNs),a stable adaptive neural control(ANC)scheme is proposed to guarantee the ultimate boundedness of all the signals in the closed-loop system and the prescribed tracking performance.In the steady-state control process,partial persistent excitation(PE)condition of RBF NNs is satisfied during tracking control to recurrent reference orbits.As a result,it is shown that the proposed ANC scheme can acquire and store knowledge of the unknown system dynamics.The stored knowledge is reused to develop neural learning control,so that the improved control performance with the faster tracking convergence rate and the less computational burden is achieved.Specially,the develop neural learning control can also guarantee the prescribed transient and steady tracking performance when the initial condition satisfies the prescribed performance bound.Simulation studies are performed to demonstrate the effectiveness of the proposed scheme.

关键词： Adaptive Neural Control Deterministic Learning Predefined Performance PE Condition Uncertain Dynamics

作者: WANG Min WANG Cong LIU Xiaoping

作者单位: College of Automation,South China University of Technology,Guangzhou 510641,P.R.China Faculty of Engineering,Lakehead University,Thunder Bay,ON P7B 5E1,Canada

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 8871-8876

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）