A robust neuro-based adaptive control system design for a surface effect ship with uncertain dynamics and input saturation to cargo transfer at sea

Esmailian, Ehsan; Farzanegan, Behzad; Menhaj, Mohammad Bagher; Ghassemi, Hassan

doi:10.1016/j.apor.2018.02.010

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…α 0 1 is the virtual control law, which is used to stabilize the error z1 . According to the Lyapunov theorem and command filter design framework, α 0 1 is designed as the form of Equation (32).…”

Section: Control Law Designmentioning

confidence: 99%

“…Considering the Assumptions 1-3,the control law (40) based on the virtual control law (32) and the parameter adaptation law ( 41)-( 43) can ensure that the vessel fin stabilizer control system described by (10) has the following properties:…”

Section: Theoremmentioning

confidence: 99%

“…To handle the effect of input saturation, refs [30,31] used a gain-scheduling algorithm to deal with the input rate and amplitude saturation problem, which effectively improved the stability of vessel rolling control system. 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%

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

2022

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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.

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Section: Control Law Designmentioning

confidence: 99%

Section: Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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“…The execution function achieves optimization based on the evaluation value of the cost function, and the cost function must be minimized. Considering the good organizational and self‐adaptive capabilities of BPNNs, 23,24 we design the DHP controller based on BPNNs for each network.…”

Section: Design Of Dhp Controller For Earth Pressure In Chambermentioning

confidence: 99%

Optimal control of earth pressure balance of shield tunneling machine based on dual‐heuristic dynamic programming

Liu

Shao

2020

Optim Control Appl Methods

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Summary Earth pressure balance (EPB) shield tunneling machine has been widely used in underground construction. To avoid the catastrophic accidents caused by earth pressure imbalance, the earth pressure on excavation face must be controlled balance to that in chamber. To solve this problem better, a multi‐variable data‐driven optimal control method for shield machine based on dual‐heuristic programming (DHP) is proposed. The DHP controller is constructed with action network, model network, and critic network based on back‐propagation neural networks (BPNNs). Following Bellman's principle of optimality, a cost function of DHP controller for the chamber's earth pressure is presented, which simplifies a multi‐level optimization to a single‐level optimization. To minimize the cost function, the action network utilizes the critic network's error to achieve multi‐variable optimization, and the optimal control parameters for the tunneling process are obtained at last. The simulation results show that the method can effectively control the earth pressure balance. Even in case of disturbance, the system has strong anti‐interference ability and the control process is also quicker and steadier.

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“…In engineering, input constraint is inevitable due to the physical constraint of actuator whose neglect will damage the system performance or even result into instability ( [24][25][26][27][28][29][30][31]). However, most works on the formation control of MSVs ignore the input constraints (see e.g., [4, 6, 8-13, 15, 17, 19, 21, 23]).…”

Section: Introductionmentioning

confidence: 99%

Anti-saturation time-varying formation control via switching for a wider class of patterns of multiple uncertain marine surface vessels

Li,

Liang,

2024

Nonlinear Dyn

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This paper is devoted to the time-varying formation control of multiple marine surface vessels (MSVs) with input saturation. Remarkably, more serious uncertainties are involved than the related literature since external disturbance is considered while all the system parameters are unknown. Moreover, different from the severe constraints on the formation patterns and the leader's dynamics in the related literature, rather fairly weak conditions are required since the relative deviations are only first but not more order continuously differentiable which imply that a wider class of formation patterns can be allowed, while the time derivatives of relative deviations as well as the leader's output are not necessarily available for feedback. These two aspects make the traditional control methods on this topic ineffective. Then, by skilfully combining vectorial backstepping control method with logic-based switching mechanism, a switching controller is successfully designed for the formation control in this paper. It is worthwhile pointing out that, by the skilful design of a switching mechanism, the serious uncertainties are compensated while the severe assumptions on the formation patterns of the related literature are extremely relaxed. Finally, simulation results are provided to validate the effectiveness of the proposed theoretical results.

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A robust neuro-based adaptive control system design for a surface effect ship with uncertain dynamics and input saturation to cargo transfer at sea

Cited by 13 publications

References 25 publications

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

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

Optimal control of earth pressure balance of shield tunneling machine based on dual‐heuristic dynamic programming

Anti-saturation time-varying formation control via switching for a wider class of patterns of multiple uncertain marine surface vessels

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