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In this article, a decentralized adaptive finite-time tracking control scheme is proposed for a class of nonstrict feedback large-scale nonlinear interconnected systems with disturbances. First, a practical almost fast finite-time stability framework is established for a general nonlinear system, which is then applied to the design of the large-scale system under consideration. By fusing command filter technique and adaptive neural control and introducing two smooth functions, the “singular” and “explosion of complex” problems in the backstepping procedure are circumvented, while the obstacles caused by unknown interconnections are overcome. Moreover, according to the framework of practical almost fast finite-time stability, it is shown that all the closed-loop signals of the large-scale system are almost fast finite-time bounded, and the tracking errors can converge to arbitrarily small residual sets predefined in an almost fast finite time. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed finite-time decentralized control scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time adaptive neural control and almost disturbance decoupling problems for multi-input/multi-output (MIMO) nonlinear systems with disturbances and non-strict-feedback structure. In the design procedure of the adaptive controller, neural networks are employed to estimate the unknown nonlinearities and Young's inequality is utilized to cope with the disturbance terms derived from all subsystems. In order to characterize the disturbance attenuation performance of finite-time adaptive control, a criterion named finite-time almost disturbance decoupling is first developed. Under this criterion, an adaptive neural controller is designed via the backstepping method and the appropriate selection of Lyapunov function. It is revealed that the proposed controller can guarantee all variables of the closed-loop system are bounded, and the performance of finite-time almost disturbance decoupling is realized. Finally, a practical example is employed to validate the effectiveness of the designed controller. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the multiple model strategy is applied to the adaptive control of switched linear systems to improve the transient performance. The solvability of the adaptive stabilization problem of each subsystem is not required. Firstly, the two-layer switching mechanism is designed. The state-dependent switching law with dwell time constraint is designed in the outer-layer switching to guarantee the stability of the switched systems. During the interval of dwell time constraint, the parameter resetting adaptive laws are designed in the inner-layer switching to improve the transient performance. Secondly, the minimum dwell time constraint providing enough time for multiple model adaptive control strategy to work fully and maintaining the stability of the switched systems is found. Finally, the proposed switched multiple model adaptive control strategy guarantees that all the closed-loop system signals remain bounded and the state tracking error converges to zero. [ABSTRACT FROM AUTHOR]