基于径向基函数神经网络迟滞非线性自适应控制

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 backlash 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 nov el class of hysteresis mo dels w as proposed.　A cer tain num ber o f different deadband w idth

backlash models are superposed,　w hich represents a dynamics to m im ic hysteresis in the actuator.　With

the　mo del proposed,　an radial basis function neural netw ork ( RBFN )-based adaptive control scheme for

nonlinear sy stems w ith unknow n hysteresis nonlinearity w as dev elo ped.　The control scheme adopts the de-

sign method of　pseudo-co ntro l.　Witho ut　the assumption of　boundedness of　disturbance term ,　it is tuned

thr oug h adaptive algo rithm .　The stability is rigidly pr oved v ia Lyapunov　theory　and the effectiveness of

the pro posed contr ol scheme is illustrated through simulatio n.