Adaptive neural control of nonlinear periodic time-varying parameterized mixed-order multi-agent systems with unknown control coefficients

In this paper, we first consider the adaptive leader-following consensus problem for a class of nonlinear parameterized mixed-order multi-agent systems with unknown control coefficients and time-varying disturbance parameters of the same period. Neural networks and Fourier series expansions are used to describe the unknown nonlinear periodic time-varying parameterized function. A distributed control protocol is designed based on adaptive control, matrix theory, and Nussbaum function. The robustness of the distributed control protocol is analyzed by combining the stability analysis theory of closed-loop systems. On this basis, this paper discusses the case of time-varying disturbance parameters with non-identical periods, expanding the application scope of this control protocol. Finally, the effectiveness of the algorithm is verified by a simulation example.