Finite-time distributed optimal tracking for multiple heterogeneous linear systems

This brief investigates the finite-time distributed optimization problem for heterogeneous multi-agent systems subject to bounded inputs. The gradient of the local objective function is Lipschitz and the local convex constants cannot be obtained. By utilizing non-smooth control approaches to eliminate the effects of the nonlinear item, a class of finite-time distributed optimization control algorithms are developed to guarantee the outputs of all agents to track the optimum value, which makes a global objective function achieve minimum. Finally, a simulation example is presented to illustrate the effectiveness of the obtained results.