Neuroadaptive Tracking Control of Affine Nonlinear Systems Using Echo State Networks Embedded With Multiclustered Structure and Intrinsic Plasticity
提出一种基于回声状态网络的跟踪控制方法，利用多簇结构和内在可塑性处理仿射非线性系统中的建模不确定性和耦合非线性，仿真表明跟踪精度优于现有方法。
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.