IJCAS

In this paper, model free adaptive control algorithms are proposed based on ten improved gradient descent methods which are commonly used as optimization algorithms in deep learning. For the designed control scheme, the modelling, control and optimization can be integrated in a unified framework. The effects of ten algorithms on the consensus tracking performance in multi-agent systems are studied and compared. In order to get a more universal conclusion, systems with fixed and switching topology are considered respectively. Simulation results show that the model free adaptive control algorithm based on adaptive momentum estimation method with decoupled weight decay (AdamW) has optimal performance.

Keywords: Consensus tracking, gradient descent methods, model free adaptive control, multi-agent systems.