Neural network-based event-triggered MFAC for nonlinear discrete-time processes

Liu, Dong; Yang, Guang-Hong

doi:10.1016/j.neucom.2017.07.008

Cited by 50 publications

(21 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, the comparison in terms of computation load between neural network‐based event‐triggered MFAC method [23] and the proposed method is shown in Table 2. It can be seen from Table 2 that the triggering times of proposed event‐triggered MFAC with disturbance compensation is similar to neural network‐based event‐based method [23], which requires a lot of learning and computation. Therefore, the proposed event‐triggered MFAC with disturbance compensation algorithm has the significance in saving bandwidth resources.…”

Section: Simulation Examplementioning

confidence: 99%

“…Meanwhile, by comparing with Figs. 5 and 7 in [23], Figs. 9 and 10 show that the proposed method can be effectively applied to the heat exchanger control system as well.…”

Section: Simulation Examplementioning

confidence: 99%

“…Therefore, it is meaningful to develop a novel event‐triggered control method without that assumption constraint. Recently, an event‐triggered MFAC method was applied to discrete‐time non‐linear systems in [22, 23], in which the pseudogradient parameter was estimated through radial basis function neural networks (RBFNNs). Meanwhile, based on Lyapunov analysis approach, the event‐triggering mechanism was developed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data‐driven‐based event‐triggered tracking control for non‐linear systems with unknown disturbance

Wang

Zhang

2019

IET Control Theory & Applications

View full text Add to dashboard Cite

Section: Simulation Examplementioning

confidence: 99%

“…Meanwhile, by comparing with Figs. 5 and 7 in [23], Figs. 9 and 10 show that the proposed method can be effectively applied to the heat exchanger control system as well.…”

Section: Simulation Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data‐driven‐based event‐triggered tracking control for non‐linear systems with unknown disturbance

Wang

Zhang

2019

IET Control Theory & Applications

View full text Add to dashboard Cite

“…In [18]- [22], event-triggered control schemes have been proposed for linear and nonlinear systems. For uncertain nonlinear systems, adaptive function-approximation-based event-triggered control designs were studied in [25]- [27]. Based on these theoretical foundations, event-triggered control results have been extended to practical applications such as quadrotors [28], mobile robots [29], and flexible-link manipulators [30].…”

Section: Introductionmentioning

confidence: 99%

Distributed Event-Triggered Adaptive Formation Tracking of Networked Uncertain Stratospheric Airships Using Neural Networks

Kim

Yoo

2020

IEEE Access

View full text Add to dashboard Cite

This paper investigates a distributed event-triggered formation tracking problem of networked three-dimensional uncertain nonlinear stratospheric airships under directed networks. It is assumed that the nonlinearities of airship followers are unknown and the leader information can be obtained by only a subset of the airship followers. Approximation-based local adaptive tracking controllers with asynchronous event-triggering laws are developed to achieve the desired formations for both the positions and attitudes of uncertain stratospheric airship followers. We theoretically show that the stability and formation tracking performance of event-triggered closed-loop systems are ensured and Zeno behavior is excluded in the proposed asynchronous event-triggering mechanism. Finally, simulations illustrate the effectiveness of the proposed formation control protocol. INDEX TERMS Distributed adaptive formation tracking, event-triggered, neural networks, networked stratospheric airships.

show abstract

“…For nonlinear discrete-time systems, the control method of MFAC and its combination with other control methods was put forward, and the effective results have been obtained. Liu et al designed a data-driven MFAC algorithm and got a perfect control effect [7], Xu et al discussed a new model-free adaptive sliding mode control, which can facilitate the efficient by using the constrained tracking error [4], Dong et al introduced a model-free adaptive predictive control algorithm, which has significantly smaller overshoot and shorter rising time [6]. Hui et al proposed a new MFAC with an ESO in order to deal with the disturbance and uncertainty of a class of nonlinear systems, and the feasibility and effectiveness of the proposed method has been proved [8].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Yin

et al. 2019

Electronics

View full text Add to dashboard Cite

The control issue of Autonomous Underwater Vehicles (AUV) is very challenging since the precise mathematical model of AUV is hard to establish due to its strong coupling and time-varying features. Meanwhile, AUV movement is easily interfered with by ocean currents and waves, causing anti-interference performance of traditional Proportional-Integral-Derivative (PID) control to be unsatisfactory. Aiming to solve those problems, an algorithm of fuzzy optimized model-free adaptive control (MFAC) based on auto-disturbance rejection control (ADRC) was proposed and used in AUV heading control. The MFAC is used to overcome the difficulty with establishing a precise mathematical model, and the ADRC is introduced to handle the interference of currents and waves. In this paper, MFAC and ADRC are combined. First, the MFAC is performed based only on the I/O data of the controlled object, which is simple to implement with low calculation complexity and strong robustness. Then, a tracking differentiator (TD) is employed to track the input signal to overcome the antinomy of rapidity and hypertonicity in MFAC. After that, an extended-state observer (ESO) is added to control the variables of MFAC to estimate all the disturbances, which can greatly improve the anti-interference ability of the system. Due to the complexity and diversity of the marine environment, a fuzzy optimized MFAC based on ADRC is proposed to improve the adaptability of AUV to the marine environment. Simulations and experiments were carried out to verify the control effect of this algorithm in complex sea conditions.

show abstract

Neural network-based event-triggered MFAC for nonlinear discrete-time processes

Cited by 50 publications

References 29 publications

Data‐driven‐based event‐triggered tracking control for non‐linear systems with unknown disturbance

Data‐driven‐based event‐triggered tracking control for non‐linear systems with unknown disturbance

Distributed Event-Triggered Adaptive Formation Tracking of Networked Uncertain Stratospheric Airships Using Neural Networks

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Contact Info

Product

Resources

About