Distributed cooperative adaptive identification for time-varying static parametric model systems with a cooperative PE condition by distributed event-triggered strategy

This paper addresses the problem of distributed cooperative adaptive identification for time-varying static parametric model systems with a new distributed event-triggered schedule. In the present framework, the cooperative persistent exciting (PE) condition is introduced to remove the existing assumption that the feasible solution of the controllers depended on the solving of line matrix inequality. By considering the communication resources constraints, a new event-triggered strategy is proposed to relax the requirement of continuous monitoring for each system. It is shown that, if the proposed assumpations are statisfied, the closed-loop adaptive systems are uniformly globally exponentially stable (UGES), the parameter estimation error converge to zero uniformly exponentially under a cooperative persistent excitation (PE) condition, and the Zeno behaviour is avoided by intoruding appropriate assumption that removes the state dependence requirement for each system.