| 引用本文: | 耿宝亮,胡云安.控制方向未知的不确定系统预设性能自适应神经网络反演控制[J].控制理论与应用,2014,31(3):397~403.[点击复制] |
| GENG Bao-liang,HU Yun-an.Prescribed performance adaptive neural backstepping control for nonlinear system with uncertainties and unknown control directions[J].Control Theory and Technology,2014,31(3):397~403.[点击复制] |
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| 控制方向未知的不确定系统预设性能自适应神经网络反演控制 |
| Prescribed performance adaptive neural backstepping control for nonlinear system with uncertainties and unknown control directions |
| 摘要点击 3165 全文点击 2903 投稿时间：2013-05-05 修订日期：2013-10-07 |
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| DOI编号 10.7641/CTA.2014.30436 |
| 2014,31(3):397-403 |
| 中文关键词 预设性能 神经网络 Nussbaum函数 反演 |
| 英文关键词 prescribed performance neural networks Nussbaum function backstepping |
| 基金项目 国家自然科学基金资助项目(61174031). |
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| 中文摘要 |
| 对一类控制方向未知的不确定严格反馈非线性系统的预设性能自适应神经网络反演控制问题进行了研究.
系统中含有时变非匹配不确定项且控制方向未知. 首先, 提出了一种新的误差转化方法, 放宽了对初始误差已知的
限制; 随后, 利用径向基函数(radial basis function, RBF)神经网络及跟踪微分器分别实现了对未知函数和虚拟控制
量导数的逼近, 并综合运用Nussbaum函数和反演控制技术设计了控制器. 所设计的控制器能保证系统内所有信号
有界且输出误差满足预设的瞬态和稳态性能要求. 最后的仿真研究验证了控制器设计方法的有效性. |
| 英文摘要 |
| We investigate prescribed performance adaptive neural backstepping control for a class of strict-feedback
nonlinear systems with time-varying uncertainties and unknown control directions. Firstly, a novel error transformation
is proposed to eliminate the limitation that initial error must be known. Subsequently, radial basis function (RBF) neural
networks and track differentiators are proposed to approximate unknown functions and derivatives of virtual controls respectively.
At the same time, Nussbaum function and backstepping technique are combined to design the controller. The
controller guarantees that all state variables are bounded and the prescribed transient and steady state error bounds are
satisfied. Finally, the effectiveness of proposed scheme is validated by simulation research. |