摘要
提出了一种新的动态迟滞非线性模型.将一定数量不同死区宽度的back lash模型并行相加,作为一个动态系统以仿真执行器中的迟滞特性.利用该模型,采用伪控制方法设计了一套具有未知迟滞特性非线性系统的神经网络自适应控制方案,通过自适应算法来调整干扰项的上限.采用Lyapunov稳定性理论进行了严格证明,仿真试验验证了所提方案的有效性.
A novel class of hysteresis models was proposed. A certain number of different deadband width backlash models are superposed, which represents a dynamics to mimic hysteresis in the actuator. With the model proposed, an radial basis function neural network (RBFN)-based adaptive control scheme for nonlinear systems with unknown hysteresis nonlinearity was developed. The control scheme adopts the design method of pseudo-control. Without the assumption of boundedness of disturbance term, it is tuned through adaptive algorithm. The stability is rigidly proved via Lyapunov theory and the effectiveness of the proposed control scheme is illustrated through simulation.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2005年第12期2002-2005,2010,共5页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金(50265001)
青岛科技大学科研启动基金资助项目
关键词
神经网络
迟滞
非线性系统
自适应控制
neural network
hysteresis
nonlinear system
adaptive control