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This paper investigates dissipativity and related properties for switched linear parameter-varying (LPV) systems by using parameter and dwell time-dependent multiple storage functions, which are different from traditional multiple storage functions because of the introduction of the external parameters into the storage functions construction. First, a novel concept relating to external parameters is proposed to describe the overall dissipativity property of switched parameter-varying systems in the absence of the conventional dissipativity property of the subsystems. Second, a state and parameter-triggered and time-driven switching laws is proposed, which avoids Zeno behavior by guaranteeing a dwell time among the adjacent switching. Third, several classic forms of dissipativity are addressed. For passivity and $ {L_{2}}$ -gain, under certain negative output feedback asymptotic stability is guaranteed with the aid of asymptotic zero state detectability. Finally, the effectiveness of the proposed result is illustrated by its application to a speed adjustment problem of an aero-engine. [ABSTRACT FROM AUTHOR]

This article addresses the problem of adaptive finite-time neural tracking control for nonstrict-feedback nonlinear systems via dynamic surface (DS) technique. First, a new quasi-fast finite-time practical stability (QFPS) criterion is proposed for a class of general nonlinear systems. Then, the presented QFPS criterion is applied to design the desired adaptive finite-time neural tracking controller for a class of nonstrict-feedback nonlinear systems. The presented design scheme for the nonstrict-feedback nonlinear system has the following two features: 1) the “explosion of complexity” issue of the backstepping design is addressed by utilizing the DS technique and 2) the structural feature of Gaussian functions is applied to solve the design difficulties caused by the nonstrict-feedback form. It is proved that the designed controller for the nonstrict-feedback nonlinear system can make the resulting closed-loop system stabilizable in a quasi-fast finite time and the tracking error converges to a sufficiently small neighborhood of the origin. Finally, the simulation results are given to show the validity and practicability of the proposed design scheme. [ABSTRACT FROM AUTHOR]

In this article, an adaptive approximation-based output-feedback tracking control scheme is presented for a class of stochastic switched lower-triangular nonlinear systems with input saturation and unmeasurable state variables. First, to overcome the design obstacle caused by the nondifferential saturation nonlinearity, a carefully selected nonlinear function of the control input signal is applied to estimate the saturation function. Then, a reduced-order state observer is designed to model the unmeasured system states, which also means the error system can be established. Furthermore, the fuzzy-logic systems are utilized to approximate the unknown system nonlinearities in the adaptive backstepping-based controller design procedure. It is ensured that all the closed-loop system variables are bounded in probability and the error signal belongs to a compact set in the mean square sense. Finally, the effectiveness and the practicability of the proposed control scheme are shown by two examples. [ABSTRACT FROM AUTHOR]