Neural Network Control of a Flexible Robotic Manipulator Using the Lumped Spring-Mass Model
采用自适应神经网络控制方法抑制柔性机械臂的振动，通过集中弹簧-质量模型提高弹性变形描述精度，并分别设计了全状态反馈和输出反馈控制器，仿真与实验验证了有效性。
Adaptive neural networks (NNs) are employed for control design to suppress vibrations of a flexible robotic manipulator. To improve the accuracy in describing the elastic deflection of the flexible manipulator, the system is modeled via the lumped spring-mass approach. Full-state feedback control as well as output feedback control are proposed separately. Aiming at achieving the control objective, uniform ultimate boundedness of the closed-loop system is ensured. Numerical simulations for the lumped model of the flexible robotic system are carried out to verify the performance of the NN control. Finally, the experiments are given to further validate the feasibility of the proposed NN controllers on the Quanser platform.