Decentralised adaptive neural connectively finite-time control for a class of p-normal form large-scale nonlinear systems

This paper focuses on the decentralised adaptive finite-time connective stabilisation problem for a class of p-normal form large-scale nonlinear systems at the first. By combining the adding a power integrator technique, the neural network technique and the finite-time Lyapunov stability theory, the decentralised adaptive neural finite-time controllers are designed, which can guarantee the large-scale system is finite-time connectively stable. In order to avoid the effect of neural network estimation error on satisfying the finite-time criteria, the combination vectors are composed by the weights and the estimation errors of the neural networks. The maximal upper bounds of the combination vector norms are taken as the adaptive parameters. Because of employing neural networks, the restriction of the unknown nonlinear terms in some literature about finite-time control is relaxed. Two simulation examples are provided to prove the effectiveness and advantage of the proposed control method.