Adaptive Neural Zeta-Backstepping With Predefined Damping Ratio. Application to DC Motors
提出一种自适应神经Zeta反步控制策略，使不确定非线性系统能以预定义阻尼比实现实际稳定，并通过直流电机实验验证了方法的有效性。
This brief presents an adaptive neural zeta-backstepping control strategy for a class of uncertain nonlinear systems, which allows these systems to be practically stabilized with predefined damping ratios. By introducing the zeta-backstepping technique, system damping ratios can be predetermined based on specific parameter selection rules. To reduce the impact of unknown nonlinearities, neural networks (NNs) with gradient descent training are applied to compensate such nonlinearities online. A new filter, called dynamic command filter, is used to construct the gradient of the NNs. By resorting to second-order Lyapunov stability criteria, it is proved that the closed-loop system is practically stable and has predefined damping ratio. Finally, experiments on a perturbed direct current (DC) motor system demonstrate the advantages of the proposed method.