Dynamic event-triggered output-feedback control of uncertain nonlinear systems with unknown control directions

Dynamic event-triggered neuro-optimal control for uncertain nonlinear systems with unknown dead-zone constraint

This paper investigates the cooperative output regulation problem for heterogeneous linear multiagent systems under fixed communication graphs via event-triggered control. A fully distributed event-triggered dynamic output feedback control law is proposed based on the feedforward design approach. At the same time, a fully distributed dynamic event-triggering mechanism is designed so that each agent can determine when to broadcast its information to its neighbors. Compared with existing related results, both the control law and the event-triggering mechanism in this paper are independent of any global information. It is shown that with the proposed dynamic event-triggered control strategy, the cooperative output regulation problem can be solved in a fully distributed manner by intermittent communication. Moreover, Zeno behavior can be strictly ruled out for each agent. Finally, the effectiveness of the proposed dynamic event-triggered control strategy is validated by a numerical example.

In this paper, a novel event-triggered (ET) adaptive neural tracking control scheme is proposed for uncertain strict-feedback systems subject to asymmetric time-varying full-state constraints, unknown virtual control coefficients and external disturbances. The lower-order time-varying asymmetric barrier Lyapunov function (TVABLF) is constructed to ensure that state constraints are not transgressed. Then, a simple backstepping-based control procedure is developed to remove the existing restrictions on a high power of piecewise TVABLF and high-order differentiability of virtual laws. In particular, a novel adaptive updating law is constructed to co-design the controller and the ET scheme by the combination of the Nussbaum gain technique, thereby solving the ET controller design difficulty resulting from unknown control coefficients. The proposed scheme can guarantee all states within the time-varying constrained areas, achieve the satisfactory tracking ability and save the communication resource. Finally, the effectiveness of the proposed scheme is testified by numerical and practical examples.

Observer-based adaptive fuzzy quantized control of uncertain nonlinear systems with unknown control directions

Adaptive output feedback event-triggered tracking control for nonlinear systems with unknown control coefficient

Dynamic event-triggered composite anti-disturbance fault-tolerant tracking control for ships with disturbances and actuator faults

It is nontrivial to achieve practical predefined-time (PPT) control for uncertain nonlinear systems (UNSs) in strict-feedback form. The main technical difficulty is that there is no relevant lemma to prove the predefined-time stability of the UNS with unknown control directions. This article proposes an adaptive neural predefined-time control scheme, capable of achieving PPT control for UNS with unknown control directions. The presented scheme is derived from a PPT function, associating with backstepping technique, generating a predefined-time control solution. Meanwhile, a dynamic event-triggered schedule is developed to update the control law, therefore reducing data transmission. Finally, an illustrative example is given to illustrate the effectiveness of the presented scheme.

In this paper, an additional internal dynamic variable is introduced into the continuous event-triggered mechanism (ETM) and the discrete ETM, respectively, reducing the number of triggers. The closed-loop system with time-varying communication delays and dynamic event-triggered control (ETC) is modeled as a time-delay system model. The stability and stabilization criteria are derived based on Lyapunov-Krasovskii (L-K) functional method, and a tighter delay-dependent condition in terms of linear matrix inequalities (LMIs) is obtained. By solving these LMIs, we can gain a joint-design method so that the state-feedback controller gain and the event-triggered strategy are obtained at the same time. The results can provide trade-offs between required communication resources and desired control performance. The numerical simulation is carried out to show the theoretical claims at the end.

Fuzzy rule-based neural appointed-time control for uncertain nonlinear systems with aperiodic samplings

This paper studies the problem of global output-feedback tracking control for a class of uncertain nonlinear systems. The system studied in this paper has unknown time-varying control coefficients and unknown reference signal. Notably, the nonlinearities of the system are bounded by the lower triangular linear unmeasured states multiplying the unknown constant, the polynomial-of-output and the polynomial-of-input growth rates, which indicates the presence of serious uncertainties. Motivated by the closely related works, by combining the ideas of universal control and deadzone with backstepping technique, this paper presents a new adaptive tracking control scheme based on two dynamic high-gains in the new forms. The proposed control scheme ensures that the tracking error converges to a specified arbitrarily small interval range after a finite time while the state of the resulting closed-loop system is globally bounded. Finally, the effectiveness of the control scheme is verified by an illustrative example.

The efficient utilization of computation and communication resources became a critical design issue in a wide range of networked systems due to the finite computation and processing capabilities of system components (e.g., sensor, controller) and shared network bandwidth. Event-triggered mechanisms (ETMs) are regarded as a major paradigm shift in resource-constrained applications compared to the classical time-triggered mechanisms, which allows a trade-off to be achieved between desired control/estimation performance and improved resource efficiency. In recent years, dynamic event-triggered mechanisms (DETMs) are emerging as a promising enabler to fulfill more resource-efficient and flexible design requirements. This paper provides a comprehensive review of the latest developments in dynamic event-triggered control and estimation for networked systems. Firstly, a unified event-triggered control and estimation framework is established, which empowers several fundamental issues associated with the construction and implementation of the desired ETM and controller/estimator to be systematically investigated. Secondly, the motivations of DETMs and their main features and benefits are outlined. Then, two typical classes of DETMs based on auxiliary dynamic variables (ADVs) and dynamic threshold parameters (DTPs) are elaborated. In addition, the main techniques of constructing ADVs and DTPs are classified, and their corresponding analysis and design methods are discussed. Furthermore, three application examples are provided to evaluate different ETMs and verify how and under what conditions DETMs are superior to their static and periodic counterparts. Finally, several challenging issues are envisioned to direct the future research.

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

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

Event-triggered low-computation adaptive output-feedback fuzzy tracking control of uncertain nonlinear systems

This article designs the static and dynamic event-triggered control (ETC) and self-triggered control (STC) algorithms to achieve the semiglobal stabilization of discrete-time systems with input constraints. First, a novel static ETC algorithm based on the discrete-time parametric Lyapunov equation (DPLE) is designed. In order to further increase the interevent times (IETs), the corresponding dynamic ETC is designed. Next, both static and dynamic STC, where the next control law updates depend on the previous triggered states, are proposed to avoid monitoring the measurement errors. The proposed algorithms are not only capable of reducing the number of transmissions significantly but also build a very simple and clear relationship between the only design parameter and the nontrivial IET (NIET). This allows us to change regularly IETs by adjusting the design parameter so that the nontriviality of static and dynamic ETC and STC is guaranteed and a tradeoff between the IETs and the control performance can be easily found. Specifically, by exploring the properties of DPLE, the designed algorithms avoid the complex relationship between the nontrivial condition and the system matrices. Finally, the designed static and dynamic ETC and STC algorithms are applied to the design of the spacecraft rendezvous control system and their effectiveness is verified by simulation results.