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This paper is concerned with the stabilization of a geometric nonlinear beam with a nonlinear delay term in boundary control. The well-posedness of the closed-loop system where a nonlinear damping and a nonlinear delay damping are applied at the boundary is examined using the Faedo–Galerkin approximation method. Constructing a novel energy-like function to handle the nonlinear delay, the explicit exponential decay rate of the closed-loop system is established with a generalized Gronwall-type integral inequality and the integral-type multiplier method. [ABSTRACT FROM AUTHOR]

This article considers the problem of finite-time command-filtered control for switched nonlinear systems with input quantization and output constraints. The unmeasurable state is estimated by designing a switched state observer. During the design process, to overcome the chattering problem effectively, the hysteresis quantization is designed as two bounded nonlinear functions. Furthermore, in order to restrict the output to an expected range, the barrier Lyapunov function approach is introduced. The "explosion of complexity" and the error compensation problems in the backstepping design are solved by using a finite-time command-filtered approach. A first-order Levant differentiator is used instead of the general command filter in this paper, which can not only filter the intermediate signals accurately to get the differential signals, but also ensure the finite-time stability of the filter. Stability of the closed-loop system in the sense of semi-global practical finite-time stability (SGPFS) is proved by exploring a multiple Lyapunov functions approach. Finally, a simulation example is provided to verify the validity of the presented control method. [ABSTRACT FROM AUTHOR]

This article focuses on an adaptive neural network (NN) finite-time prescribed performance control problem for nonstrict-feedback nonlinear systems subject to full-state constraints. Specifically, a finite-time performance function is employed, which can guarantee that the tracking error converges to a prescribed region within a finite-time. Neural networks (NNs) are used to approximate the unknown nonlinear function. The unmeasurable states are estimated via constructing a state observer. By using the dynamic surface control (DSC) technique, the complexity problem has been avoided in traditional backstepping control. In order to satisfy the state constraint condition, the barrier Lyapunov function (BLF) is incorporated in the process of backstepping. The developed adaptive finite-time NN backstepping control strategy can make that the closed-loop system is semiglobally practical finite-time stability (SGPFS). Meanwhile, all states can be guaranteed to remain in the constrained space. Simulation results demonstrate the validity of the control method. [ABSTRACT FROM AUTHOR]

This paper focuses on the passive fault-tolerant control for an uncertain switched nonlinear random system with multiple interval time-varying delays and intermittent faults in sensors and actuators. There are also nonlinear functions, exogenous disturbances and measurement noise in the system. There are few tries to realize noise-to-state exponentially mean-square stability for switched random nonlinear systems subject to multiple interval time-varying delays allowing various time delay cases. Random differential equations are more common than stochastic differential equations in practice. Thus, the suggested method is more practicable. First, a nonlinear dynamic output feedback fault-tolerant controller is constructed. Next, an augmented closed-loop system can be obtained in the framework of multiple interval time-varying delays. Then piecewise Lyapunov function is utilized to realize stability analysis for the closed-loop system. At last, this feasibility of this approach presented in this study is verified through two examples. [ABSTRACT FROM AUTHOR]

This article discusses the problem of event-triggered fixed-time control for a class of stochastic non-triangular structure nonlinear systems. The Mean Value Theorem is employed to transform the stochastic non-triangular structure nonlinear systems into the equivalent systems with affine structure. The unknown nonlinear functions are approximated by the radial basis function neural networks. Combining with the fixed-time stable theory, backstepping technique and event-triggered control technique, a novel event-triggered fixed-time adaptive controller is designed. The results manifest that the whole signals in the closed-loop system are bounded in probability. Meanwhile, the tracking error converges to a small residual set within a fixed-time interval. Two simulations are included to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This study is concerned with the problem of finite-time fault-tolerant control for uncertain semi-Markovian jump systems with input constraint and multiple time delays. There exist nonlinear functions, exogenous disturbances and measurement noise in these nonlinear systems. First, a dynamic output feedback controller is constructed for the uncertain semi-Markovian jump systems. A closed-loop system is earned. Next, sufficient stability conditions of the closed-loop system are obtained via stochastic Lyapunov function. Not only finite-time boundedness but also input-output finite-time mean square stabilization is achieved. This study is the first time to study. Finally, an example is provided to prove the novelty of the approach proposed in this paper. [ABSTRACT FROM AUTHOR]

In this article, an adaptive fuzzy output-feedback tracking control scheme is proposed for a class of single-input and single-output uncertain switched nonlinear systems in nonstrict-feedback form with prescribed performance and arbitrary switching. By using the concept of prescribed performance, we mean that the tracking error converges to the predefined performance bounds. The unknown nonlinear functions are approximated by utilizing the fuzzy logic systems. For the systems under study, unavailable states are taken into account, which are estimated by constructing a fuzzy state observer. In order to overcome the difficulty arising from the nonstrict-feedback form, an effective adaptive parameter is introduced. By using the common Lyapunov function method and backstepping design approach, an adaptive control algorithm is presented for the considered system. The designed controller can guarantee that all the signals within closed-loop systems are semi-globally uniformly ultimately bounded. The effectiveness of the control strategy put forward in this paper is demonstrated by the simulation results. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of tracking control aiming at a class of uncertain nonlinear systems in the presence of input saturation, unknown external disturbances and unmeasured states on the basis of adaptive neural dynamic surface control (DSC) scheme in combination with state and disturbance observers. The unknown nonlinear system functions are approximated by Radial Basis Function Neural Networks, the unmeasured states are estimated by a developed state observer and the unknown compounded disturbances are estimated by nonlinear disturbance observers. In addition, by introducing DSC technique, the problem of "explosion of complexity" inherent in the conventional backstepping method is eliminated. The designed controller ensures the semi-global stabilization of the whole closed-loop system by means of Lyapunov analysis method. The effectiveness of the proposed approach is demonstrated through a numerical example. [ABSTRACT FROM AUTHOR]

This paper studies the finite-time stabilization of a class of time-delay nonlinear systems in the presence of uncertainties and external disturbances under the input amplitude constraints. The external disturbances are unknown and energy-bounded and the nonlinear vector function of system satisfies the one-sided Lipschitz condition which is less conservative than the well-known Lipschitz condition. To have a robust finite-time stabilization in the considered system, a robust H∞ controller is designed with respect to a finite-time interval. In this regard, two theorems are presented based on Lyapunov–Krasovskii approach and the sufficient conditions are derived as linear matrix inequalities which guarantee the finite-time boundedness of the resulting uncertain closed-loop system. The effectiveness of the proposed method is illustrated by two examples, one numerical and one practical (time-delay Chua's circuit) with simulation results. [ABSTRACT FROM AUTHOR]

Adaptive Front-Lighting System (AFLS) is a system which assists driver's field of vision by automatically controlling its brightness and illumination angle to adapt various driving conditions such as climate, traffic, road changes and so forth. This paper aims to propose novel model-based PID gain design method to improve the performances of Dynamic Bending Light (DBL) module that change horizontal angle of a system by applying Brent-Dekker algorithm that finds the root of nonlinear function and implementing Nelder-Mead simplex algorithm to the system reduction process. Along with the linear system model-based control theory, motor dynamics were modeled with frequency response. Validation of the prototype resulted in having less than 3 % error from the simulation, where position initialization and the real-time status monitoring function is available due to the closed loop control which enables over 3 times faster response than the conventional open-loop system. [ABSTRACT FROM AUTHOR]