Distributed Optimization for a Class of High-order Nonlinear Multi-agent Systems with Unknown Dynamics
It extends distributed optimization to a more general class of nonlinear systems, but the results are incremental over existing work on integrators and linear agents.
This paper addresses distributed optimization for high-order nonlinear multi-agent systems with unknown dynamics, converting the problem into output stabilization and providing adaptive controllers that drive outputs to the global optimum. Simulation results verify the approach.
In this paper, we study a distributed optimization problem for a class of high-order multi-agent systems with unknown dynamics. In comparison with existing results for integrators or linear agents, we need to overcome the difficulties brought by the unknown nonlinearities and also the optimization requirement. For this purpose, we employ an embedded control based design and first convert this problem into an output stabilization problem. Then, two kinds of adaptive controllers are given for these agents to drive their outputs to the global optimal solution under some mild conditions. Finally, we show that the estimated parameter vector converges to the true parameter vector under some well-known persistence of excitation condition. The efficacy of these algorithms was verified by a simulation example.