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In this article, a prescribed time output feedback 3-D stabilization control scheme is proposed for unmanned underwater vehicles. Considering the problem of unmeasurable velocities, unknown hydrodynamic parameters, and external underwater perturbations, we construct a prescribed time extended state observer (PTESO) for the unmanned underwater vehicles based on a time-varying scaling function that can estimate the linear and angular velocities as well as unknown nonlinearities within a preset time. Then, a novel prescribed time output feedback controller is developed for the unmanned underwater vehicles with the aid of PTESO, which exhibits a simplified structure and allows for advance specification of the prescribed time. By using Lyapunov stability theory, it is strictly proven that the stabilization error converges to zero within a prescribed time. Finally, an experimental test is carried out on an open-frame unmanned underwater vehicle to verify the effectiveness of the proposed scheme.

This article focuses on the problem of adaptive event-triggered output feedback control for a class of uncertain nonlinear systems under the output constraint. Different from the existing works, the time-varying parameters and the global output constraint are taken into account. First, by means of multifilters, the unmeasurable state variables are reconstructed, under which the unknown time-varying parameters and sensor sensitivity are transformed into the estimation problem of unknown parameters. Second, based on a barrier function, a novel constraint algorithm is established to make the output enter into asymmetric time-varying constraint boundaries, which is independent of the initial value of the output. To avoid continuous sampling of the controller, an event-triggered mechanism is proposed without the Zeno phenomenon. By means of the Lyapunov stability theory, it is strictly proved that the output enters into the pregiven asymmetric constraint boundaries, and never exceeds. Finally, the validity of our proposed control algorithm is illustrated by a numerical simulation.

This article studies the adaptive prescribed-time control problem for a class of nonlinear systems with unknown time-varying control coefficients. Existing methods for unknown control direction problems can only achieve asymptotic stability based on Barbalat's lemma. Different from these results, we present a new theorem in conjunction with Nussbaum functions to achieve the prescribed-time stability. Meanwhile, the conservative condition of Nussbaum parameters is relaxed under time-varying control coefficients, which increases the applicability of the control algorithm. Based on this theorem, an adaptive prescribed-time control method for high-order nonlinear systems is proposed, which guarantees that the state variables of the system converge to zero in a prescribed time rather than infinity. Finally, theoretical analysis and numerical simulations are provided to validate the effectiveness of the proposed method.

This brief investigates the problem of adaptive tracking control for uncertain nonlinear systems with output constraint in predefined time interval. The considered constraint brings the problem of discontinuity in the transition between constrained and unconstrained phases. Firstly, a novel shifting function is proposed to solve this problem. Then, this shifting function is combined with the barrier Lyapunov function (BLF) to solve the considered output constraint. By means of variant Sigmoid functions, the requirement of the reference signal is relaxed, which only needs to be piecewise differentiable. The proposed control method is also able to deal with the constraint-free case, the infinite-time constrained one and deferred output constraint with unchanged control structure. Finally, the effectiveness of the proposed control strategy is assessed through the simulation.

This brief is concerned with the prescribed-time tracking control problem for mobile robot systems subject to performance constraints. In contrast to existing results on mobile robotic systems, which fail to achieve precise tracking, this brief focuses on achieving error-free tracking control within a specific time interval, which is independent of system initial conditions as well as control parameters. Then, a novel state transformation is proposed to achieve global performance constrains control. Additionally, based on the Lyapunov’s theory, it is proved that the designed controller can make system states accurately track the given signal within a prescribed-time interval, while guaranteeing the boundedness of close-loop systems. Finally, the effectiveness of proposed control algorithm is verified by a simulation example.

This article studies the global prescribed-time stabilization problem for a class of time-delay nonlinear systems with uncertain parameters. First, we design two time-varying gains with special properties, in which one is introduced into virtual controllers to achieve prescribed-time convergence and the other one is used to construct the Lyapunov–Krasovskii (L-K) functional and Lyapunov function to handle the nonlinear time-delay term and unknown parameters, respectively. Then, by utilizing double time-varying gains and the scaling-free backstepping design approach, a dynamic state feedback controller is constructed, which guarantees that all state variables reach zero within a prescribed time, and the prescribed time can be specified in advance. Then, based on new functionals and regular differential inequality, we figure out the explicit expression for the upper bound of all variables, which plays an important role in proving the boundedness of all system variables. Final, a simulation example is given to demonstrate the effectiveness of the proposed method.

This article concerns with the adaptive fixed-time control problem for classes of nonlinear cascade systems with parametric uncertainty. The majority of existing finite/fixed-time control strategies for uncertain nonlinear systems can only make system states approach to the neighborhood of origin over a finite/fixed time. Different from these results, we put forward a time-varying gain-based control algorithm to ensure that all system states return to the origin in a fixed time. With the help of the backstepping method, a time-varying adaptive controller is designed for a high-order nonlinear systems subject to uncertain parameters. Through constructing appropriate dynamic gain-based Lyapunov functions and utilizing our proposed stability criterion, it is proved that all states of the uncertain system can be adjusted to the origin in a fixed-time interval. Moreover, the settling time is not depend on the initial conditions of system and can be preset according to the actual requirements. Finally, the simulation results are provided to illustrate the effectiveness of the developed dynamic time-varying feedback control algorithm.