期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 28, 期 6, 页码 1318-1330出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2016.2538779
关键词
Barrier Lyapunov function (BLF); disturbance observer; neural networks (NNs); state/input saturation constraints
类别
资金
- National Natural Science Foundation of China [61573147, 91520201, 61572540]
- Guangzhou Research Collaborative Innovation Projects [2014Y2-00507]
- Guangdong Science and Technology Research Collaborative Innovation Projects [2014B090901056]
- Guangdong Science and Technology Plan Project through the Application Technology Research Foundation [2015B020233006]
An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.
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