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The distributed finite-time control problem through adaptive neural identifier for a group of nonlinear agents with strict-feedback structure is assessed here. The main considered and assessed technical problems in this proposed control strategy are that followers’ states are not fully available for control design, and at the same time, each follower is represented by uncertain nonlinear switched dynamics. Due to the structure of the proposed followers here, a switched linear state observer is constructed to estimate the unavailable states. To finite convergence time guarantee, the backstepping design and finite-time approach are adopted in the distributed control design in a simultaneous manner. The control architecture is chosen to guarantee graph stability through multiple Lyapunov functions. It is revealed that the followers’ outputs are forced to follow the leader output in the finite time, and all signals in the overall closed-loop network system are assured to be bounded. Two simulation examples are presented to further demonstrate the validity of this proposed method.

In order to synchronize a network of Euler–Lagrangian (EL) systems, a distributed output-feedback control is proposed in this paper. The adopted mathematical model of EL systems possesses nonlinear uncertainties, unmeasured states, and stochastic disturbances. By introducing prescribed performance control (PPC) in a dynamic surface control approach, a novel distributed neural adaptive PPC design is presented. Simultaneously, one outstanding characteristic is that the minimal learning parameter approach is employed in the dynamic surface control methodology. It is proved that the norm of distributed tracking errors is confined to compact set with predefined convergence rates and maximum overshoots by using stochastic Lyapunov analysis and graph theory. Finally, simulation examples validate the effectiveness of the proposed approach.

In this paper, an adaptive neural output-feedback control approach is considered for a class of uncertain multi-input and multi-output MIMO stochastic nonlinear systems with unknown control directions. Neural networks NNs are applied to approximate unknown nonlinearities, and K-filter observer is designed to estimate unavailable system's states. Due to utilization of Nussbaum gain function technique in the proposed approach, the singularity problem and requirement to prior knowledge about signs of high-frequency gains are removed, simultaneously. Razumikhin functional method is employed to deal with unknown state time-varying delays, so that the offered control approach is free of common assumptions on derivative of time-varying delays. Also, an adaptive neural dynamic surface control is developed; hence, explosion of complexity in conventional backstepping method is eliminated, effectively. The boundedness of all the resulting closed-loop signals is guaranteed in probability; meanwhile, convergence of the tracking errors to adjustable compact set in the sense of mean quartic value is also proved. Finally, simulation results are shown to verify and clarify efficiency of the offered approach. Copyright © 2016 John Wiley & Sons, Ltd.

This paper considers the problem of partial tracking errors constrained for high-order nonlinear multi-agent systems in strict-feedback form. In the control design, radial-based function neural networks are utilized to identify uncertain nonlinear functions, and a cooperative adaptive dynamic surface control is proposed to avoid the explosion of complexity in the backstepping technique. Based on the minimal learning parameter technique and the predefined performance approach, a novel cooperative adaptive neural network control method is developed. The proposed controller is able to guarantee that all the closed-loop network signals are cooperative semi-globally uniformly ultimately bounded, and partial tracking errors confine all times within the predefined bounds. Finally, simulation example and comparative example with previous methods are given to verify and clarify the effectiveness of the new design procedure. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, the problem of distributed adaptive neural control is addressed for a class of uncertain non-affine nonlinear multi-agent systems with unknown control directions under switching directed topologies. Via mean-value theorem, non-affine follower agents’ dynamics are transformed to the structures so that control design becomes feasible. Then, radial basis function neural networks are used to approximate the unknown nonlinear functions. Due to the utilization of a Nussbaum gain function technique, the singularity problem and requirement to prior knowledge about signs of derivative of control gains are removed. On the base of dynamic surface control design and minimal learning parameter approach, a simplified approach to design distributed controller for uncertain nonlinear multi-agent systems is developed. As a result, the problems of explosion of complexity and dimensionality curse are counteracted, simultaneously. By the theoretical analysis, it is proved that the closed-loop network system is cooperatively semi-globally uniformly ultimately bounded. Meanwhile, convergence of distributed tracking errors to adjustable neighborhood of the origin is also proved. Finally, simulation examples and a comparative example are shown to verify and clarify efficiency of the proposed control approach.