一类输入受限的不确定非线性系统自适应Backstepping变结构控制

Adaptive Backstepping sliding mode control for a class of uncertain nonlinear systems with input constraints

针对一类输入受限的不确定非线性系统,提出了一种自适应Backstepping变结构控制器设计方法。建立了受未知非线性特征约束的执行器故障模型,可以描述系统存在死区、齿隙、饱和、滞回等输入受限情形以及可能发生的执行器失效、卡死等故障情形。设计径向基函数神经网络补偿未建模动态项,引入一阶低通滤波器避免了Backstepping控制中的计算复杂性问题。自适应近似变结构控制能够有效削弱控制信号抖振。理论分析和仿真实验结果证明,提出的自适应鲁棒控制律能够在输入受限的情况下自适应地调节控制输入,使得闭环系统稳定且满足控制性能要求。

An adaptive Backstepping sliding mode control method is proposed for a class of uncertain nonlin-ear systems with input constraints. A model for the nonlinear actuator is developed,which includes input con-strained situations such as dead zone, backlash, saturation, hysteresis, and unknown faults such as partial loss of effectiveness fault and actuator stuck fault. Radial basis function neural network is employed to approximate the unknown nonlinear functions. The explosion of complexity is avoided in the traditional Backstepping design method by introducing a first order filter. Adaptive approximate variable structure control is effective to reduce the chatting of the control signal. Theoretical analysis and simulation results are presented to demonstrate the effectiveness of this method by adaptively adjusting control input.