Optimal tracking of continuous time nonlinear systems has been extensively studied in literature. However, in several applications, absence of knowledge about system dynamics poses a severe challenge to solving the optimal tracking problem. This has found growing attention among researchers recently, and integral reinforcement learning (IRL)-based method augmented with actor neural network (NN) have been deployed to this end. However, very few studies have been directed to model-free $H_{\infty}$ optimal tracking control that helps in attenuating the effect of disturbances on the system performance without any prior knowledge about system dynamics. To this end a recursive least square-based parameter update was recently proposed. However, gradient descent-based parameter update scheme is more sensitive to real-time variation in plant dynamics. And experience replay (ER) technique has been shown to improve the convergence of NN weights by utilizing past observations iteratively. Motivated by these, this paper presents a novel parameter update law based on variable gain gradient descent and experience replay technique for tuning the weights of critic, actor and disturbance NNs.

中文翻译：

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$\text{H}_{\infty}$ 未知连续时间非线性系统的基于可变增益梯度下降积分强化学习的跟踪控制

连续时间非线性系统的最优跟踪已在文献中得到广泛研究。然而，在一些应用中，缺乏系统动力学知识对解决最优跟踪问题构成了严峻挑战。最近，这在研究人员中引起了越来越多的关注，并且已经为此部署了基于积分强化学习 (IRL) 的方法，并使用了演员神经网络 (NN)。然而，很少有研究针对无模型 $H_{\infty}$ 最优跟踪控制，该控制有助于在没有任何关于系统动力学的先验知识的情况下减弱扰动对系统性能的影响。为此，最近提出了基于递归最小二乘的参数更新。然而，基于梯度下降的参数更新方案对植物动力学的实时变化更为敏感。经验重放 (ER) 技术已被证明可以通过迭代地利用过去的观察来提高 NN 权重的收敛性。受这些启发，本文提出了一种基于可变增益梯度下降和经验重放技术的新参数更新法则，用于调整评论家、演员和干扰神经网络的权重。