期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 49, 期 12, 页码 2511-2518出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2017.2755377
关键词
Adaptive control; barrier Lyapunov function (BLF); neural networks (NNs); robotic systems; state constraints
资金
- National Natural Science Foundation of China [61473139, 61622303, 61603164]
- Project for Distinguished Professor of Liaoning Province
In this paper, we design an adaptive neural network (NN) controller of uncertain n-joint robotic systems with time-varying state constraints. By proposing a nonlinear mapping, the robotic systems are transformed into the multiple-input, multiple-output systems. Compared with constant constraints, the time-varying state constraints are more general in the real systems. To overcome the design challenge, the time-varying barrier Lyapunov function is introduced to ensure that the states of the robotic systems are bounded within the predetermined time-varying range. The NN approximations are employed to approximate the uncertain parametric and unknown functions in the robotic systems. Based on the Lyapunov analysis, it can be proved that all signals of robotic systems are bounded; the tracking errors of system output converge on a small neighborhood of zero and the time-varying state constraints are never violated. Finally, a simulation example is performed to demonstrate the feasibility of the proposed approach.
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