Adaptive cooperative output regulation of nonlinear multiagent systems with arbitrarily large parametric uncertainties and an uncertain leader

Abstract

Summary This paper extends the result for cooperative output regulation problem for uncertain nonlinear multiagent systems in output feedback form in the sense that the exosystem generating leader's signal and disturbance is allowed to contain unknown parameter, and all parameters in the whole multiagent system can be arbitrarily large. Since only the information of itself and its neighbors is available, constructing a distributed control law is necessary for the asymptotic tracking of the uncertain leader's signal and the rejection of unknown external disturbances, which is also the main challenge here. A series of simulations are conducted to illustrate the efficiency and advantage of our designs together with the comparison of the design in the existing work.

Distributed output‐feedback finite‐time tracking control of nonaffine nonlinear leader‐follower multiagent systems

“…Based on the finite‐time observer, the fraction dynamic surface control approach, and the NN approximation property, the output‐feedback control protocols are derived. Different from the asymptotical convergence results in References , , and the finite‐time convergence property is achieved in this work. Since the finite‐time convergence enjoys strong robustness and fast convergence properties, it is attractive specially for the networked large‐scale systems.…”

“…In Reference , the output‐feedback consensus control for high‐order linear MASs was investigated. After that, many results have been developed to address the output‐feedback control for high‐order linear or nonlinear MASs by considering different situations, such as external disturbance, input saturation, unmodelled dynamics, and so on (eg, References ). In addition, it is more desirable to design finite‐time consensus protocol so that consensus is achieved in a fast way.…”

Stochastic fixed-time synchronization for multilayer derivative dynamic Cohen-Grossberg networks via quantitative control

“…Fig. 1 shows errors e ij (t)(i = 1, 2, 3, 4; j = 1, 2) of multilayer derivative dynamic heterogeneous Cohen-Grossberg random neural networks (35) tends to 0 before T m =11.0027, and the j−th neuron in all subnets of neural networks (35) can be synchronized to the equilibrium node under fixed-timely quantized controller (8), which means the theoretical framework is effective in this paper.…”

“…Synchronization is an important dynamic behavior of networked dynamical systems [8][9][10]. The research of networked dynamical systems has complex connections with control theory [1,[11][12]32].…”