Robust adaptive neural trajectory tracking control of surface vessels under input and output constraints

Zhu, Guibing; Du, Jialu; Kao, Yonggui

doi:10.1016/j.jfranklin.2020.06.010

Cited by 46 publications

(25 citation statements)

References 41 publications

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“…where m > 0, p > 1 and q > 1 are constants that satisfy (p − 1)(q − 1) = 1. Lemma 5: [36], [43] For a nonlinear function ϕ(Z) : R n → R defined on a compact set Z ⊂ Ω Z ∈ R n , there exists a radial basis function NN ϑ * T β(Z) such that the following equation holds…”

Section: Problem Formulation and Preliminaries A Problem Formulamentioning

confidence: 99%

“…However, the control solution proposed in [41] required the accurate knowledge of ship model. Whereafter, such a requirement was overcome in [42]- [43]. Specially, in terms of dealing with the output constraint issue, the BLF-based method was replaced by a constrained transform approach in [43].…”

Section: Introductionmentioning

confidence: 99%

“…Whereafter, such a requirement was overcome in [42]- [43]. Specially, in terms of dealing with the output constraint issue, the BLF-based method was replaced by a constrained transform approach in [43]. Meanwhile, this method was extended to solve the formation control for waterjet unmanned surface vessels under constraints both in input and output in [44].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the Nussbaum function is introduced to handle the issue of timevarying gain caused by the smooth function. For the output constrain issue, this work discards the BLF used in [21], [22], [26], [43], which is replace by a new function. In addition, RBF NNs are applied to approximate the unknown nonlinear function vector caused by the uncertain dynamic, directly.…”

Section: Introductionmentioning

confidence: 99%

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Neuroadaptive Control for Uncertain Euler-Lagrange Systems With Input and Output Constraints

Wang¹,

Kuang²

2021

IEEE Access

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This paper investigates the control issue of Euler-Lagrange systems (ELSs) subject to dynamic uncertainties and external disturbances under input and output constraints, and develops two neuroadaptive control schemes, i.e., direct neuroadaptive approximation method and indirect neuroadaptive approximation method. In the control design, a smooth saturation function called Gaussian error function is used to replace the saturation model, which is applied to solve the input saturation issue. Moreover, a new function is used to guarantee that the output does not violate the restricted boundary. The uncertain dynamic of the ELSs is reconstructed by the direct or indirect neuroadaptive method, and then a virtual-parameter learning method is proposed to reduce the computational load of control schemes. With the aid of Lyapunov stability theory, it is proven that all signals in the closed-loop control system are bounded and the tracking error of ELSs converges to zero under the proposed neuroadaptive control control schemes. The simulations on a robotic manipulator illuminate the effectiveness and preponderance of the developed neuroadaptive control schemes.INDEX TERMS Euler-Lagrange system, dynamic uncertainty, input and output constraints, neuroadaptive control.

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Section: Problem Formulation and Preliminaries A Problem Formulamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neuroadaptive Control for Uncertain Euler-Lagrange Systems With Input and Output Constraints

Wang¹,

Kuang²

2021

IEEE Access

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“…RBFs differ from other NN architectures, having simpler structures, employing faster training algorithms, and usually producing more accurate models than MLPs. Within the context of vessel trajectory prediction, RBFs have been integrated in control frameworks by approximating unknown vessel parameters [28][29][30]. Recently, RBFs have been applied on real AIS data in order to produce highly accurate models for one-step and multi-step ahead predictions [31], showing their potential in being integrated to receding horizon control methodologies.…”

Section: Introductionmentioning

confidence: 99%

Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions

Papadimitrakis

Stogiannos

Sarimveis

et al. 2021

Sensors

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The field of automatic collision avoidance for surface vessels has been an active field of research in recent years, aiming for the decision support of officers in conventional vessels, or for the creation of autonomous vessel controllers. In this paper, the multi-ship control problem is addressed using a model predictive controller (MPC) that makes use of obstacle ship trajectory prediction models built on the RBF framework and is trained on real AIS data sourced from an open-source database. The usage of such sophisticated trajectory prediction models enables the controller to correctly infer the existence of a collision risk and apply evasive control actions in a timely manner, thus accounting for the slow dynamics of a large vessel, such as container ships, and enhancing the cooperation between controlled vessels. The proposed method is evaluated on a real-life case from the Miami port area, and its generated trajectories are assessed in terms of safety, economy, and COLREG compliance by comparison with an identical MPC controller utilizing straight-line predictions for the obstacle vessel.

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Adaptive NN state error PCH trajectory tracking control for unmanned surface vessel with uncertainties and input saturation

Chen

et al. 2023

Asian Journal of Control

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A novel robust state error port controlled Hamiltonian (PCH) trajectory tracking controller of an unmanned surface vessel (USV) subject to time‐varying disturbances, dynamic uncertainties and control input saturation is presented. The proposed control scheme combines the advantages of the high robustness and energy minimization of the state error PCH approach and the approximation capability of adaptive radial basis function neural networks (RBFNNs). Adaptive RBFNNs are used to the time‐varying disturbances of the environment and unknown dynamics uncertainties of the USV model. The state error PCH control approach is designed such that the system can optimize energy consumption, and the state error PCH technique makes the designed trajectory tracking controller be easy to implement in practice. To handle the effect of the control input saturation, a Gaussian error function model is employed. It has been demonstrated that the proposed approach can maintain the USV's trajectory at the desired trajectory, while the closed‐loop control system can guarantee the uniformly ultimate boundedness. The energy consumption model of the USV is constructed to reveal to the energy consumption. Simulation results demonstrate the effectiveness of the proposed controller.

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Robust adaptive neural trajectory tracking control of surface vessels under input and output constraints

Cited by 46 publications

References 41 publications

Neuroadaptive Control for Uncertain Euler-Lagrange Systems With Input and Output Constraints

Neuroadaptive Control for Uncertain Euler-Lagrange Systems With Input and Output Constraints

Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions

Adaptive NN state error PCH trajectory tracking control for unmanned surface vessel with uncertainties and input saturation

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