Abstract
This paper studies the problem of learning-based adaptive state feedback control for networks of heterogeneous agents with input and matched uncertainties. We propose a control strategy that guarantees that the closed-loop synchronization error of all agents in the network is bounded. Adaptive control laws are designed via matching conditions to approximate the dynamics of each agent, which are partially unknown. Uncertainty parameters in the model are suppressed using adaptive optimal modification theory. Moreover, a momentum-based technique is also introduced in our framework for improving the convergence behavior of the adaptive control law parameters computation. Finally, a simulation example for Cooperative Adaptive Cruise Control (CCAC) is presented to validate the effectiveness of the control scheme. Numerical examples show that momentum-based techniques reach the bounded error set faster than gradient-based techniques.
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