| 引用本文: | 李飞,胡剑波,王坚浩,郑磊.一类含有滞回故障非线性系统Backstepping变结构控制[J].哈尔滨工业大学学报,2017,49(4):108.DOI:10.11918/j.issn.0367-6234.201503083 |
| LI Fei,HU Jianbo,WANG Jianhao,ZHENG Lei.Backstepping sliding mode control for a class of nonlinear systems with hysteresis failures[J].Journal of Harbin Institute of Technology,2017,49(4):108.DOI:10.11918/j.issn.0367-6234.201503083 |
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| 摘要: |
| 为解决执行器发生未知故障情况下不确定非线性系统的控制问题，采用一种自适应Backstepping变结构控制方法，建立了包括滞回非线性和失效、卡死等故障类型的非线性执行器模型.通过径向基函数 (radial basis function, RBF) 神经网络逼近系统中的未知非线性函数项，神经网络参数根据自适应律实时调整，保证了逼近效果.结合动态面控制，避免了Backstepping控制中的计算复杂性问题.引入的自适应补偿项消除了系统建模误差和不确定干扰的影响，理论分析证明了闭环系统半全局一致最终有界，仿真结果验证了该方法的有效性.
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| 关键词: 滞回非线性 未知故障 不确定性 自适应Backstepping控制 RBF神经网络 |
| DOI：10.11918/j.issn.0367-6234.201503083 |
| 分类号:TP273 |
| 文献标识码:A |
| 基金项目:工业控制技术国家重点实验室资助 (ICT1401)；上海市重点学科建设资助(No.J50103) |
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| Backstepping sliding mode control for a class of nonlinear systems with hysteresis failures |
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LI Fei1,HU Jianbo2,WANG Jianhao2,ZHENG Lei2
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(1.Science College, Air Force Engineering University, Xi’an 710051, China; 2.Materiel Management and Safety Engineering College, Air Force Engineering University, Xi’an 710051, China)
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| Abstract: |
| An adaptive backstepping sliding mode control method is adopted to solve the control problem of uncertain nonlinear systems with unknown actuator failures. A model for the nonlinear actuator is developed which includes hysteresis nonlinearity, partial loss of effectiveness and total loss of effectiveness. Radial basis function neural network is employed to approximate the unknown nonlinear functions, and the parameters of neural network are tuned on-line by adaptation laws to improve the effect of approximation. The dynamic surface control is combined with backstepping control to avoid the explosion of complexity in the traditional backstepping design method. The influence of modeling error and uncertain disturbances is suppressed by introducing the adaptive compensation term. The closed loop system is proved to be semi-globally uniformly ultimately bounded by theoretical analysis. Simulation results are presented to demonstrate the effectiveness of this method.
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| Key words: hysteresis nonlinearity unknown faults uncertainties adaptive backstepping control RBF neural network |
