Adaptive Neural Consensus Tracking for Second-Order Nonlinear Multi-agent Systems with Full-State Constraints

Abstract

In this paper, a scheme to handle the consensus tracking problem of multi-agent systems that feature second-order and nonlinearity under the conditions of full-state constraints is further discussed. In order to make the output of each agent track the output of leader accurately without explosion of complexity problem in traditional backstepping, the backstepping method using the design of command filter is adopted. The filtering process will produce errors, so the compensation signal is adopted to further guarantee the tracking precision. Moreover, the innovative adaptive control law that need only one parameter is proposed and the output signal do not exceed the constrained region in the tracking process is proved. The neural network technology is introduced to approximate the dynamics that feature unknown nonlinearities. An example of mathematical simulation verifies the validity of involved method.