We aim at the optimization of the tracking control of a robot to improve the robustness, under the effect of unknown nonlinear perturbations. First, an auxiliary system is introduced, and optimal control of the auxiliary system can be seen as an approximate optimal control of the robot. Then, neural networks (NNs) are employed to approximate the solution of the Hamilton-Jacobi-Isaacs equation under the frame of adaptive dynamic programming. Next, based on the standard gradient attenuation algorithm and adaptive critic design, NNs are trained depending on the designed updating law with relaxing the requirement of initial stabilizing control. In light of the Lyapunov stability theory, all the error signals can be proved to be uniformly ultimately bounded. A series of simulation studies are carried out to show the effectiveness of the proposed control.

中文翻译：

---

通过自适应动态规划对机器人进行鲁棒神经优化控制。

我们的目标是在未知非线性扰动的影响下优化机器人的跟踪控制以提高鲁棒性。首先，引入辅助系统，对辅助系统的优化控制可以看作是对机器人的近似优化控制。然后，在自适应动态规划框架下，采用神经网络 (NN) 来逼近 Hamilton-Jacobi-Isaacs 方程的解。接下来，基于标准的梯度衰减算法和自适应批评家设计，根据设计的更新律训练神经网络，并放宽初始稳定控制的要求。根据李雅普诺夫稳定性理论，可以证明所有的误差信号都是一致最终有界的。