Adaptive neural network based leader-following consensus control for a class of second-order nonlinear multi-agent systems

The leader-following consensus problem for a type of second-order nonlinear multi-agent systems (MASs) with input saturation, actuator faults, and sensor faults is examined in this study. An adaptive control strategy based on neural networks (NNs) is suggested to overcome the difficulties brought on by unknown nonlinear dynamics and real-world limitations. To handle the unknown nonlinear factors more precisely and flexibly than traditional approaches that depend on global Lipschitz requirements, we use the differential mean value theorem. This improvement enhances the system's capacity to deal with nonlinear behaviors that change quickly. Additionally, sensor faults that affect location and velocity readings and actuator faults that may decrease or deform the input signal are taken into consideration in the control design. The suggested design also clearly addresses input saturation, which may restrict the control authority. Complete state measurements are not required because a distributed adaptive NN-based controller is created only on relative location and velocity data. Using stability theory and appropriate Lyapunov function construction, we rigorously demonstrate that the suggested approach leads to leader-following consensus. The efficiency and resilience of the suggested control strategy against nonlinear uncertainty, actuator and sensor failures, and saturation effects in complicated multi-agent networks are confirmed by simulation results.