This paper investigates the problem of event-triggered adaptive control for a class of nonlinear systems subject to asymmetric input saturation and time-varying partial state constraints. To facilitate analyzing the influence of asymmetric input saturation, the saturation function is converted into a linear form with respect to control input. To achieve the objective that partial states do not exceed the constraints, a more general form of Lyapunov function is offered. Different from some existing results about output/full state constraints, the proposed scheme only requires that the partial states satisfy the time-varying constraints. Moreover, an event-triggered scheme with a varying threshold is designed to reduce the communication burden. With the time-varying asymmetric barrier Lyapunov functions, a novel event-triggered control scheme is developed, which ensures that partial states are without violation of required constraints and the tracking error converges to a small neighborhood of the origin despite appearing as saturated phenomenon. Eventually, the theoretic results are confirmed by two examples.

In this article, a dynamic event-triggered control scheme for a class of stochastic nonlinear systems with unknown input saturation and partially unmeasured states is presented. First, a dynamic event-triggered mechanism (DEM) is designed to reduce some unnecessary transmissions from controller to actuator so as to achieve better resource efficiency. Unlike most existing event-triggered mechanisms, in which the threshold parameters are always fixed, the threshold parameter in the developed event-triggered condition is dynamically adjusted according to a dynamic rule. Second, an improved neural network that considers the reconstructed error is introduced to approximate the unknown nonlinear terms existed in the considered systems. Third, an auxiliary system with the same order as the considered system is constructed to deal with the influence of asymmetric input saturation, which is distinct from most existing methods for nonlinear systems with input saturation. Assuming that the partial state is unavailable in the system, a reduced-order observer is presented to estimate them. Furthermore, it is theoretically proven that the obtained control scheme can achieve the desired objects. Finally, a one-link manipulator system and a three-degree-of-freedom ship maneuvering system are presented to illustrate the effectiveness of the proposed control method.

This article focuses on the distributed adaptive fixed-time platoon tracking problem for third-order fully heterogeneous nonlinear vehicles. The modified dynamics of each vehicle are constructed, and a practically fixed-time criterion is set up. Moreover, the singularity problem in the fixed-time and finite-time control is addressed well by designing new virtual signals and exploiting the inequality technique and power transformation scheme instead of the approximation method. The distributed nonlinear fixed-time tracking protocol is constructed by virtue of the recursive algorithm, and the design process is simplified by a tracking differentiator. In particular, the upper bound of the settling time has nothing to do with initial conditions. Further, the disturbances are tackled via robust $H_{\infty }$ control theory. Finally, simulation tests are contained to illustrate the performance of the proposed protocols.

This paper studies the problem of adaptive fuzzy dynamic surface control (DSC) of non-strict-feedback nonlinear systems subject to unknown virtual control coefficients, dead zone and full state constraints. The Nussbaum gain technique is used to overcome the difficulty caused by the unknown virtual control coefficients. By utilizing the information of tan-type barrier Lyapunov function, the requirement of full state constraints is successfully achieved. In addition, to handle the problem of “explosion of complexity” resulted from backstepping itself, a DSC approach using the sliding mode differentiator is introduced. Then, based on backstepping control, we develop a new adaptive fuzzy DSC strategy, which ensures that all state constrains are not violated via designing parameters appropriately. Meanwhile, other signals existing in the closed-loop system are bounded. Finally, comparative results are provided to illustrate the effectiveness of the proposed approach.