Event-Triggered Adaptive Neural Fault-Tolerant Control of Underactuated MSVs With Input Saturation

Zhu, Guibing; Ma, Yong; Li, Zhixiong; Malekian, Reza; Sotelo, Miguel Ángel

doi:10.1109/tits.2021.3066461

Cited by 143 publications

(81 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, according to (19), (20) in Assumption 2, and the property of absolute value inequality, (28) can be rewritten as…”

Section: Extended-state-observer Convergencementioning

confidence: 99%

“…17 Furthermore, Chang et al 18 developed a robust adaptive backstepping scheme to tackle the tracking control issue of an industrial manipulator subjected to input saturation. Input saturation also exists in other practical applications such as marine surface vessels 19 and wheeled mobile robots. 20 It is undeniable that the input saturation phenomenons caused by limited power seriously affect tracking performances and convergence characteristics of controllers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extended‐state‐observer‐based dynamic surface control of flexible‐joint robot systems with input saturation

Lian

Wang

2021

Adaptive Control & Signal

View full text Add to dashboard Cite

This article presents an extended-state-observer-based dynamic surface control approach for flexible-joint robot systems with asymmetric input saturation and large unknown dynamic knowledge. Traditional controllers for flexible-joint robot systems usually use approximation technology to deal with unknown dynamics knowledge. Unlike the traditional control algorithm, this article utilizes an extended state observer to estimate the unknown dynamics.For the closed-loop system, the delay strategy handles the time-scale separation issue, the filtering system overcomes the "explosion of differentiation" caused by the repeated differentiation of auxiliary control signals, and the mean-value-theorem solves the input saturation problem of the actuator. The stability analysis implies that estimation errors of extended state observers (ESOs) and other state variables are semiglobally uniformly ultimately bounded.Compared with fuzzy control algorithms, the novel ESO-based dynamic surface control approach not only omits online learning time but also uses only a few control parameters to obtain satisfactory tracking performance. Finally, a comparison simulation experiment is provided to illustrate the effectiveness of the gained conclusions.

show abstract

“…Then, according to (19), (20) in Assumption 2, and the property of absolute value inequality, (28) can be rewritten as…”

Section: Extended-state-observer Convergencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extended‐state‐observer‐based dynamic surface control of flexible‐joint robot systems with input saturation

Lian

Wang

2021

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…In [32], the adverse effect of input saturation on the adaptive capability of the system was effectively dealt with by designing an auxiliary dynamic system to correct the feedback error. For the issue of saturation constraint, there are several effective approaches, such as model augmentation [33], smooth function substitution [34], and augmented error signal (AES) [35].…”

Section: Introductionmentioning

confidence: 99%

“…Considering that the model approximation ability of BP neural network is very dependent on learning samples and the convergence rate is slow, the number of hidden layer nodes of the wavelet neural network is difficult to determine. Furthermore, RBF neural networks have arbitrary approximation performance and optimal approximation performance in theory, and the learning convergence rate is fast; therefore, this paper selects an RBF neural network [6,34,36] to approximate the dynamic uncertainty of the system model. In addition, in engineering practice, due to the presence of high-frequency and low-frequency parts in the marine environment, the high-frequency parts may enter the vessel rolling motion control system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

2022

Self Cite

View full text Add to dashboard Cite

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

show abstract

“…Several control methods are presented to render the underactuation problem of MSVs, including additional control methods[16],[17],[18], output redefinition control[19],[20], dynamic extension-based dynamic inversion[21], lineof-sight (LOS)[22],[23],[24], and so on. In[16],[17],[18], three additional control terms were devised to tackle the underactuation problem of MSVs.…”

mentioning

confidence: 99%

Event-Triggered Composite Learning Finite-Time Trajectory Tracking Control for Underactuated MSVs Subject to Uncertainties

et al. 2022

Self Cite

View full text Add to dashboard Cite

In this paper, a novel event-triggered composite learning finite-time control scheme is presented for underactuated marine surface vehicles (MSVs) trajectory tracking under unknown dynamics and unknown time-varying disturbances. Line-of-sight (LOS) tracking control method is employed to address the underactuation problem of MSVs. The neural networks (NNs) are untilized to approximate unknown dynamics. The serial-parallel estimation model is employed to construct the prediction error, and the prediction errors and tracking errors are fused with construct the NN weights updating. Combining the result of approximation information, the disturbances observers can be created to achieve disturbance estimation. Fractional power technology is artistically introduced to realize the finite-time trajectory tracking control of MSVs based on composite learning. The proposed control scheme ensures the simultaneous realization of high precision tracking performance and unknown information approximation. Moreover, an event-triggered mechanism is introduced to reduce the transmission load and the execution rate of actuators. It is proved that the proposed control scheme ensures all error signals of the MSVs trajectory tracking control system can converge to the neighborhood of zero within a finite time. Finally, the simulation results on an MSV verify the effectiveness and superiority of the proposed control scheme.

show abstract

Event-Triggered Adaptive Neural Fault-Tolerant Control of Underactuated MSVs With Input Saturation

Cited by 143 publications

References 68 publications

Extended‐state‐observer‐based dynamic surface control of flexible‐joint robot systems with input saturation

Extended‐state‐observer‐based dynamic surface control of flexible‐joint robot systems with input saturation

Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

Event-Triggered Composite Learning Finite-Time Trajectory Tracking Control for Underactuated MSVs Subject to Uncertainties

Contact Info

Product

Resources

About