NN-based asymptotic tracking control for a class of strict-feedback uncertain nonlinear systems with output constraints
NN-based asymptotic tracking control for a class of strict-feedback uncertain nonlinear systems with output constraints
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DOI：
10.1109/cdc.2012.6426120
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Wenchao Meng;Qinmin Yang;Donghao Pan;Huiqin Zheng;Guizi Wang;Youxian Sun
中科院分区：
文献类型：
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作者：
Wenchao Meng;Qinmin Yang;Donghao Pan;Huiqin Zheng;Guizi Wang;Youxian Sun
针对一类具有未知非线性的严格反馈非线性系统，提出了一种渐近跟踪控制律。提出了一种结合Barrier Lyapunov函数和反推的方法来保证输出轨迹包含在预定义的集合中。该设计采用权值在线调整的单神经网络逼近系统动力学中的未知函数，避免了控制增益函数的奇异性问题。同时，为了补偿神经网络的残差重构误差和系统不确定性，引入了鲁棒项，实现了系统的渐近跟踪稳定性。通过李亚普诺夫综合，证明了闭环系统中的所有信号都是有界的，并且输出在不超过给定上界的情况下渐近收敛到期望轨迹。最后在仿真环境中验证了所提出的控制器的优点。
An asymptotic tracking control law is proposed for a class of strict-feedback nonlinear systems with unknown nonlinearities. A Barrier Lyapunov function in combination with backstepping is proposed to guarantee that the output trajectory is contained in a predefined set. A single neural network (NN), whose weights are tuned online, is utilized in our design to approximate the unknown functions in the system dynamics, while the singularity problem of the control gain function is avoided. Meanwhile, in order to compensate for the NN residual reconstruction error and system uncertainties, a robust term is introduced and asymptotic tracking stability is achieved. All the signals in the closed-loop system are proved to be bounded via Lyapunov synthesis and the output converges to the desired trajectory asymptotically without transgressing a given bound. Finally, the merits of the proposed controller are verified in the simulation environment.