Adaptive H∞ Consensus Control of Multi-Agent Systems on Directed Graph by Utilizing Neural Network Approximators

Abstract Design methods of adaptive H∞ consensus control of multi-agent systems composed of the first-order and the second-order regression models on directed network graphs and with nonlinear terms by utilizing neural network approximators, are presented in this paper. The proposed control schemes are derived as solutions of certain H∞ control problems, where estimation errors of tuning parameters and approximate and algorithmic errors in neural network estimation schemes are regarded as external disturbances to the process. It is shown that the resulting control systems are robust to uncertain system parameters and that the desirable consensus tracking is achieved approximately via adaptation schemes and L2-gain design parameters.