In this article, we present an echo state network (ESN)-based tracking control approach for a class of affine nonlinear systems. Different from the most existing neural-network (NN)-based control methods that are focused on the feedforward NN, the proposed method adopts a bioinspired recurrent NN fusing with multiple cluster and intrinsic plasticity (IP) to deal with modeling uncertainties and coupling nonlinearities in the systems. The key features of this work can be summarized as follows: 1) the proposed control is built upon the ESN embedded with multiclustered reservoir inspired from the hierarchically clustered organizations of cortical connections in mammalian brains; 2) the developed neuroadaptive control scheme utilizes unsupervised learning rules inspired from the neural plasticity mechanism of the individual neuron in nervous systems, called IP; 3) a multiclustered reservoir with IP is integrated into the algorithm to enhance the approximation performance of NN; and 4) the multiclustered reservoir is constructed offline and is task-independent, rendering the proposed method less expensive in computation. The effectiveness of the method is also confirmed by comparison with the existing neuroadaptive methods via numerical simulations, demonstrating that better tracking precision is achieved by the proposed method.

中文翻译：

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使用嵌入多簇结构和内在可塑性的回声状态网络对仿射非线性系统进行神经自适应跟踪控制。

在本文中，我们为一类仿射非线性系统提出了一种基于回声状态网络 (ESN) 的跟踪控制方法。与大多数现有的基于神经网络 (NN) 的控制方法侧重于前馈 NN 不同，所提出的方法采用生物启发的递归 NN 融合多簇和内在可塑性 (IP) 来处理建模不确定性和耦合非线性系统。这项工作的主要特点可以总结如下：1）所提出的控制是建立在嵌入多簇水库的ESN之上的​​，灵感来自哺乳动物大脑中皮质连接的层次聚类组织；2) 开发的神经自适应控制方案利用受神经系统中单个神经元的神经可塑性机制启发的无监督学习规则，称为IP；3) 算法中集成了一个带IP的多聚类水库，以提高NN的逼近性能；4）多集群水库是离线构建的，并且与任务无关，使得所提出的方法的计算成本更低。通过数值模拟与现有的神经自适应方法进行比较，也证实了该方法的有效性，表明该方法实现了更好的跟踪精度。