期刊
NONLINEAR DYNAMICS
卷 72, 期 1-2, 页码 175-184出版社
SPRINGER
DOI: 10.1007/s11071-012-0701-y
关键词
Strict-feedback nonlinear systems; Complexity growing problem; Single neural network; Adaptive control
资金
- National Natural Science Foundation of China [61273137, 51209026, 61074017, 60874056]
- Program for Liaoning Excellent Talents in Universities [2009R06]
- Fundamental Research Funds for the Central Universities [017004]
A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach.
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