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This paper considers the global stabilization for a class of uncertain nonlinear systems with unknown powers, unknown control directions, and unknown nonlinearities. Mainly due to the presence of the unknown powers which have not known upper bound, no existing methods are applicable to the control problem to be solved. In this paper, to compensate the serious system uncertainties and particularly to overcome the major obstruction from unknown powers, we appeal to the mechanism of switching adaptive feedback. By flexibly combining the domination and the method of adding a power integrator, we propose a new adaptive controller design, whose design parameters are tuned online based on a switching logic. The designed controller will become operative as long as finite switchings happen and will not lead to Zeno phenomenon, and can guarantee the global boundedness as well as ultimate convergence of the resulting closed-loop system. Two numerical examples are given to demonstrate the effectiveness of the developed design scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed output tracking problem via linear sampled-data control for a class of uncertain nonlinear multi-agent systems. Specifically, for the case where the graph topology is directed and the leader is the neighbor of only a small portion of followers, by combining the backstepping method with sampled-data method, a series of linear sampled-data controllers are obtained. Stability analysis is performed by using calculus, comparison principle and so on. The main results of this paper are concluded that all the states of the closed-loop system are globally bounded and the output tracking errors can be tuned arbitrarily small. Finally, a numerical example is given to verify the validity of the conclusion. [ABSTRACT FROM AUTHOR]

We study the distributed output‐feedback tracking control problem by linear sampled‐data control for nonlinear multi‐agent systems with unmeasurable states in this paper. Noting that sampled‐data control can effectively reduce implementation cost and time, we develop a novel linear sampled‐data high‐gain homogeneous domination control strategy by integrating backstepping method and high‐gain homogeneous domination method into sampled‐data control. A series of linear sampled‐data output‐feedback controllers and reduced‐order observers are designed to ensure that the output tracking errors can be tuned arbitrarily small while all states of the closed‐loop system are globally bounded. A numerical example is given to verify the effectiveness of the sampled‐data strategy. [ABSTRACT FROM AUTHOR]