Adaptive H∞ Consensus Control of Multi-Agent Systems 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 and 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, imperfect knowledge of the leader, and approximate and algorithmic errors in the neural network estimation schemes are regarded as external disturbances to the process.