Adaptive event-triggered output-feedback controller for uncertain nonlinear systems

Event-triggered adaptive optimal tracking control for nonlinear stochastic systems with dynamic state constraints

Fuzzy adaptive event-triggered finite-time constraint control for output-feedback uncertain nonlinear systems

Although rich collection of research results on event-triggered control exist, no effort has ever been made in integrating state/output triggering and controller triggering simultaneously with backstepping control design. The primary objective of this article is, by using intermittent output signal only, to build a backstepping adaptive event-triggered feedback control for a class of uncertain nonlinear systems. To do so, we need to tackle three technical obstacles. First, the nature of the event triggering makes the transmitted output signal discontinuous, rendering the regular recursive backstepping design method inapplicable as the repetitive differentiation of virtual control signals is literally undefined. Second, the effects arisen from the event-triggering action must be properly accommodated, but the current compensating method only works for systems in normal form, thus a new method needs to be developed in order to handle nonnormal form systems. Third, as only intermittent output signal is available, and at the same time, the impacts of certain terms containing unknown parameters (arising from event triggering) need to be compensated, it is rather challenging to design a suitable state observer. To circumvent these difficulties, we employ the dynamic filtering technique to avoid the differentiation of virtual control signals in the backstepping design, construct a new compensation scheme to deal with the effects of output triggering, and build a new form of state observer to facilitate the development of output feedback control. It is shown that, with the derived adaptive backstepping output-triggered control, all the closed-loop signals are ensured bounded and the transient system performance in the mean square error sense can be adjusted by appropriately adjusting design parameters. The benefits and effectiveness of the proposed scheme are also validated by numerical simulation.

In this article, the problem of event-triggered tracking control for a class of uncertain nonlinear systems with unknown Prandtl-Ishlinskii (PI) hysteresis is investigated. To solve this problem, two control schemes are proposed via synthesizing the techniques of the event-triggered strategy, fuzzy-logic systems (FLSs), and adaptive backstepping control. The first basic design scheme applies an effective method to keep a balance between communication constraints and system performance under the influence of actuator PI hysteresis, while the Zeno behavior can be avoided. Furthermore, the basic design scheme not only guarantees the tracking error asymptotically converges to zero but also establishes a preserved transient performance. Nevertheless, note that the inclusive sign functions of the basic design scheme will cause possible chattering phenomenon, an alternative event-triggered adaptive control approach is then proposed. Unlike the previous control scheme, the second chattering-avoidance design approach ensures asymptotic convergence of the tracking error within a prescribed boundary δ , and finally the [Formula: see text]-norm transient performance of the tracking error is constructed. Simulations verify the established theoretical results that the proposed schemes successfully overcome the communication constraints and compensate the actuator PI hysteresis, and also present different tracking performances between two control schemes for comparison.

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.

Event-triggered adaptive fixed-time fuzzy control for uncertain nonlinear systems with unknown actuator faults

This paper addresses the global stabilisation via adaptive event-triggered output-feedback for a class of uncertain nonlinear systems. Remarkably, the systems under investigation simultaneously allow large uncertainties and linearly unmeasured states dependent growth for the first time in the event-triggered framework, for which a dynamic high gain and an observer based on the high gain are introduced. By integrating compensation and time-varying strategies, an adaptive event-triggered output-feedback controller is established. Particularly, a new event-triggering mechanism is introduced, in which the time-varying threshold (strictly positive and gradually decaying) is key to ensure the validity of the event-triggered controller and achieve the convergence. A simulation example is given to illustrate the effectiveness of the proposed approach.

In this paper, an adaptive event-triggered constrained control strategy is proposed for uncertain nonlinear systems with input constraints by using reinforcement learning technology and disturbance observer. By constructing an Actor-Critic neural network (NN) framework, the unknown uncertainties can be tackled by online learning and more accurate compensation. The Actor-NN is adopted for generating actions (regarded as compensation signals), and the Critic-NN is employed to evaluate the performed actions (regarded as to monitor and assess the Actor-NN performance, including the control performance). Moreover, a self-learning disturbance observer with learning ability is designed to estimate the external disturbance. On the basis of the backstepping control technology, the event-triggered control method and the smooth approximation of input saturation nonlinearity, an improved event-triggered constrained control strategy is presented using reinforcement learning technique, and the rigorous theoretical proofs of the closed-loop system stability and the avoidance of Zeno behavior are presented. The application for the quadrotor unmanned aerial vehicle validates the effectiveness of the developed event-triggered control approach.

Finite-time adaptive event-triggered fault-tolerant control of nonlinear systems based on fuzzy observer

This paper considers the problem of event-triggered adaptive fuzzy control for a class of uncertain nonlinear systems. An adaptive event-triggered controller is designed by using the command filter techniques and backstepping method of fuzzy logic systems (FLS). In this design, the use of command filter technique solves the problem of the explosion of complexity in traditional backstepping approach. The proposed event-triggered adaptive fuzzy controller ensures that all signals in the closed-loop systems are bounded and saves network communication resources. Finally, the effectiveness of the proposed control strategy is proved by giving the simulation results.

Event-triggered Adaptive Backstepping Control of Nonlinear Uncertain Systems with Input Delay

This paper addresses the problem of adaptive compensation event-triggered control for uncertain nonlinear systems with input hysteresis. The communication resources cannot be saved effectively for the considered systems, as traditional event-triggered mechanisms do not consider input hysteresis with nonlinear gain. In a network control system, how to simultaneously overcome the problem of input hysteresis and save communication resources remains a challenge. In this paper, an extended fuzzy approximation method is proposed to estimate uncertain items for control design. The method considers the time-varying error of the approximation instead of viewing the error as a bounded constant. Furthermore, in combination with the extended fuzzy approximation method, an adaptive event-triggered compensation control method for uncertain nonlinear systems with input hysteresis is designed. Under the proposed event-triggered mechanism, the control method can effectively compensate for the input hysteresis and simultaneously save communication resources. Finally, this method is verified through two simulation experiments that the occupation of communication source has been reduced and the stability of the considered system can be guaranteed effectively.

Event-triggered adaptive neural network controller for uncertain nonlinear system

In this paper, an adaptive dynamic event-triggered output-feedback control scheme is proposed for a class of uncertain nonlinear systems. Specifically, a parameter estimator and a Nussbaum function are incorporated into the dynamic event-triggered control scheme to compensate for serious uncertainties, including the completely unknown parameter allowed in system nonlinearities and the unknown control coefficients (both signs and magnitudes are unknown). Importantly, an additional internal dynamic variable is introduced into the event-triggering mechanism, enabling the control scheme to save network resources more efficiently compared with the static ones in the related works. In addition, by skillfully designing the dynamics of this variable, the influence of the execution error can be fully counteracted. It is shown that, with the proposed control scheme, the system states can asymptotically converge to zero, instead of to a residual set, while excluding the Zeno phenomenon. To validate the effectiveness and superiority of the proposed scheme, two simulation examples are provided.

Recently, event-triggered control has received increasing attention due to its advantages on saving computation and communication resources. For the controller design of nonlinear systems, backstepping method plays a vital role in most existing results. However, the backstepping control with an event-triggered scheme (ETS) merely ensures the practical stability of the system subject to uncertainties. Additionally, backstepping techniques encounter a significant challenge known as “complex explosion” when dealing with high-dimensional systems. To address such issues, this paper proposes an event-triggered adaptive sliding mode control (SMC) scheme for a class of uncertain strict feedback nonlinear systems (SFNSs) based on fully actuated system (FAS) approach. By model transformation, an FAS model of the original systems is obtained, which makes the solution of control law simple and effective. Then, a high-order sliding mode surface is designed based on FAS model, where a hyperbolic tangent function is introduced such that a nonlinear equivalent control law can be synthesized to compensate the effect of ETS. As a result, a sliding mode dynamic with fewer variables is derived. Furthermore, the stability analysis ensures the asymptotic convergence of control system without requiring global Lipschitz conditions. Finally, the effectiveness of the proposed control strategy is also illustrated by a practical example.