Event‐triggered model‐free adaptive control for nonlinear systems with output saturation

In this article, we develop a new adaptive event‐triggered asymptotic control scheme for strict‐feedback systems with fast time‐varying parameters. To deal with time‐varying parameters with unknown variation boundaries in the feedback path and the input path, we construct three adaptive laws for parameter estimation, two for the uncertain parameters in the feedback path and one for the uncertain parameters in the input path. In particular, two sets of tuning functions are introduced to avoid over‐parametrization. Additionally, an event‐triggering mechanism is embedded in this adaptive control framework to reduce the data transmission from the controller to the actuator. We also introduce a soft sign function to handle the perturbations caused by sampling errors to achieve asymptotic stability and avoid the so‐called parameter drift. The stability analysis shows that the closed‐loop system is globally uniformly asymptotically stable and the Zeno behavior can be excluded. Simulation results verify the effectiveness and performance of the proposed adaptive scheme.

Section: Introductionmentioning

confidence: 99%

Adaptive event‐triggered control for strict‐feedback systems With time‐varying parameters

Tan,

Wu,

Liu

2023

Event‐triggered consensus tracking strategy for data‐driven multi‐agent systems under DoS attacks

Liu,

Zha

et al. 2024

In this article, the event‐triggered data‐driven consensus problem is studied for multi‐agent systems (MASs) with switching topologies under denial‐of‐service (DoS) attacks. Based on the model‐free adaptive control (MFAC) approach, the controller is only correlated with the input/output (I/O) data of agents instead of the specific system model. First, the pseudo partial derivative (PPD) is employed to dynamically linearize the system model. Second, to save network bandwidth, an event‐triggered scheme is introduced according to the I/O measurement and the output estimated error. Third, an attack compensation mechanism is adopted for the purpose of reducing the influence of DoS attacks. Then, a data‐driven controller is designed to make the agents approach the desired trajectory on the basis of the estimation value of PPD. Moreover, by utilizing the Lyapunov stability theory, the tracking error is demonstrated to be convergent and the reliability of the controller is investigated. Finally, an example is simulated to verify the effectiveness of the consensus tracking strategy.

Dynamic event‐triggered model‐free adaptive control for networked control systems with random packet loss

Sun,

Lu,

Yan

et al. 2024

A novel dual‐channel dynamic event‐triggered model‐free adaptive control method with compensation is proposed for nonlinear networked control systems (NCSs) subjected to random packet loss. In order to tackle the issue of redundant data transmission, a dual‐channel triggering control framework is designed, where data transmission only occurs upon meeting the triggering conditions. Compared with the traditional static event‐triggered schemes with fixed triggering thresholds, the proposed method allows for dynamically adjustable triggering thresholds. This means that the number of data transmissions in the forward and feedback channels can be further reduced. In addition, considering the detrimental impact of random packet loss and output event‐triggered on the system, compensation is applied to the system's output data using the linear data model of the nonlinear system, which is directly derived from I/O data without incorporating any other mechanistic model information, thereby ensuring optimal control performance. In contrast to existing results, the proposed control strategy can enhance system performance while conserving network communication resources. The effectiveness and superiority of the proposed scheme have been validated through two simulations.