Abstract
In this paper, the problem of adaptive neural networks (NNs) control for a class of uncertain flexible manipulator systems with variable loads and state constraints is discussed. The system is expressed by partial differential equation (PDE) model. The barrier Lyapunov function (BLF) is selected in the design process to ensure that the states of the system do not violate the constraints. The uncertainties caused by the unknown hub inertia of the system and varying payload are also considered, then a vibration inhibition control method on the basis of NNs is proposed in the framework of adaptive backstepping control design, which ensure that the control input forces φ(t) and U(t) satisfy the corresponding constraint condition, the closed-loop system is stable. Finally, the simulation results verify the effectiveness of the control strategy.
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