Distributed optimal control law design for a class of higher order linear multi-agent systems and its application to Euler-Lagrangian systems

摘要

The paper is concerned with the distributed optimization problem for a class of higher order linear multi-agent systems. The target is to cooperatively optimize a performance function formed by a sum of convex local cost functions. To deal with the higher order dynamic, the integrator backstepping idea is introduced to break the integrator chains. Moreover, the adaptive gain technique is proposed such that the control law design can be performed in a fully distributed way, i.e., without utilizing the information of Laplacian matrix. Furthermore, the proposed scheme is applied to the control law design for the consensus of networked uncertain Euler-Lagrangian systems under optimization constraint. The effectiveness of the proposed methods is validated through some numerical simulations of a group of networked uncertain robot manipulators.