控制理论(社会学)
计算机科学
约束(计算机辅助设计)
分段
非线性系统
趋同(经济学)
可微函数
自适应控制
边界(拓扑)
弹道
径向基函数
控制(管理)
跟踪误差
人工神经网络
观察员(物理)
李雅普诺夫函数
数学
人工智能
数学分析
经济
物理
量子力学
经济增长
几何学
天文
作者
Mou Chen,Haoxiang Ma,Yu Kang,Qingxian Wu
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2021-06-24
卷期号:52 (11): 12571-12582
被引量:39
标识
DOI:10.1109/tcyb.2021.3074566
摘要
In this article, an adaptive neural safe tracking control scheme is studied for a class of uncertain nonlinear systems with output constraints and unknown external disturbances. To allow the output to stay in the desired output constraints, a boundary protection approach is developed and utilized in the output constrained problem. Since the generated output constraint trajectory is piecewise differentiable, a dynamic surface method is utilized to handle it. For the purpose of approximating the system uncertainties, a radial basis function neural network (RBFNN) is adopted. Under the output of the RBFNN, the disturbance observer technology is employed to estimate the unknown compound disturbances of the system. Finally, the Lyapunov function method is utilized to analyze the convergence of the tracking error. Taking a two-link manipulator system, as an example, the simulation results are presented to illustrate the feasibility of the proposed control scheme.
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