While neural adaptive control is widely used for dealing with continuous- or discrete-time dynamical systems, less is known about its mechanism and performance in hybrid dynamical systems. This article develops analytical tools to investigate the neural adaptive tracking control of the hybrid Markovian switching networks with heterogeneous nonlinear dynamics and randomly switched topologies. A gradient-descent adaptation law built on neural networks (NNs) is presented for efficient distributed adaptive control. It is shown that the proposed control scheme can guarantee a stable closed-loop error system for any positive control gain and tuning gain. The tracking error is demonstrated to be practically uniformly exponentially stable with a threshold in the mean-square sense. This study further reveals how the topological structure affects the NN function, by measuring the influence of the switched topologies on the learning performance.

中文翻译：

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用于马尔可夫交换网络实际跟踪的混合神经自适应控制。

虽然神经自适应控制广泛用于处理连续或离散时间动态系统，但对其在混合动力系统中的机制和性能知之甚少。本文开发了分析工具来研究具有异构非线性动力学和随机切换拓扑的混合马尔可夫切换网络的神经自适应跟踪控制。提出了一种建立在神经网络 (NN) 上的梯度下降自适应律，以实现高效的分布式自适应控制。结果表明，所提出的控制方案可以保证任何正控制增益和调谐增益的稳定闭环误差系统。跟踪误差被证明在均方意义上的阈值实际上是均匀指数稳定的。