work function

consensus

nanoparticles_97913

consensus tracking

host

consensus control

nonlinear multiagent

boundary conditions

leader-following

multi-agent_74367

wind_75836

la social

nonlinear multi-agent

multiagent_100426

nonlinear multi-agent systems

genetic_86175

epoxy coatings

multi-agent systems_39367

vacnt

cucl

IEEE Transactions on Neural Networks and Learning Systems

This work investigates a reduced-complexity adaptive methodology to consensus tracking for a team of uncertain high-order nonlinear systems with switched (possibly asynchronous) dynamics. It is well known that high-order nonlinear systems are intrinsically challenging as feedback linearization and backstepping methods successfully developed for low-order systems fail to work. Even the adding-one-power-integrator methodology, well explored for the single-agent high-order case, presents some complexity issues and is unsuited for distributed control. At the core of the proposed distributed methodology is a newly proposed definition for separable functions: this definition allows the formulation of a separation-based lemma to handle the high-order terms with reduced complexity in the control design. Complexity is reduced in a twofold sense: the control gain of each virtual control law does not have to be incorporated in the next virtual control law iteratively, thus leading to a simpler expression of the control laws; the power of the virtual and actual control laws increases only proportionally (rather than exponentially) with the order of the systems, dramatically reducing high-gain issues.

A Separation-Based Methodology to Consensus Tracking of Switched High-Order Nonlinear Multiagent Systems