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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 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 is concerned with the fixed-time prescribed tracking control problem for the uncertain stochastic nonlinear systems subject to input quantization and unknown measurement sensitivity. Different from existing results, the sensitivity on the sensor for measuring the system state is considered as an unknown parameter instead of the known one. Due to unknown measurement sensitivity on the sensor, the real system state cannot be obtained by measurement; hence, we put forward a new feedback control algorithm by the use of the unreal measured value of the system state. Moreover, the fixed-time prescribed performance on the output tracking error is investigated by developing a novel performance function. By means of the backstepping method, an adaptive quantized controller is designed for the system. Based on the Lyapunov stability theory, it is proved that the controller can render the output tracking error that satisfies the fixed-time prescribed performance and all signals of the resulting closed-loop system are bounded in probability. Finally, simulation results are provided to illustrate the effectiveness of the proposed control algorithm.