Path following of underactuated surface ships based on model predictive control with neural network

Li, Ronghui; Huang, Jian; Pan, Xinxiang; Hu, Qionglei; Huang, Zhenkai

doi:10.1177/1729881420945956

Cited by 12 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Anchoring operation is one of the key operations of ships, which is affected by wind, wave and current, ship maneuverability [20][21][22], the accurate positioning of ships [23,24], congestion of anchorage [25], water depth, bottom material grip force [26], anchoring chain length, anchorage circle radius [27], safety distance between the anchoring ships [28] and the algorithm of the anchorage area detection. At present, the research on anchorage area detection focuses mainly on the following two aspects.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Intelligent Detection Algorithm of the Single Anchored Mooring Area for Maritime Autonomous Surface Ships

et al. 2022

View full text Add to dashboard Cite

Mooring area detection represents one of the key technological problems that must be solved in the development of Maritime Autonomous Surface Ships (MASS). In view of the lack of research on the current detection methods for ship mooring area, a new intelligent detection algorithm of the single anchored mooring area for MASS was proposed in this study, aiming at improving the detection ability and accuracy of the MASS mooring area. Firstly, the laws of short period swinging motion, long period circumferential motion and reciprocating motion in the radial direction of an anchoring ship were summarized. Secondly, an anchorage circle radius model and safety distance model between the anchor positions were established and various constrains were considered including ship type, ship particulars, draft, safety impact caused by other ships passing through the anchoring ship. Thirdly, the Monte-Carlo stochastic simulation method was used to measure the mooring area, which can detect the anchor position intelligently. Finally, a case study on MATLAB demonstrated that the proposed intelligent detection algorithm for MASS is effective under various marine scenarios. The results enrich the theory of MASS mooring area detection; therefore, the algorithm has great potential to be equipped on MASS in the future.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Research on Intelligent Detection Algorithm of the Single Anchored Mooring Area for Maritime Autonomous Surface Ships

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The RBF neural network can approximate any nonlinear function, deal with the laws that are difficult to analyze in the system, has a good generalization ability, and has a fast learning convergence speed [24]. In the RBF network, x = (x i ) T is the input of the network, the output of the hidden layer of the network is h = (h j ) T , and h j is the output of the j neuron of the hidden layer…”

Section: Design Of Rbf Neural Network Controllermentioning

confidence: 99%

“…A recursive neural network maneuvering simulation model for surface ships was proposed in [23]. A model predictive control (MPC) method [24] was proposed for the path following problem of underactuated surface ships with input saturation, parameter uncertainty, and environmental disturbance. It was used to compensate unknown factors such as parameter uncertainty and environmental disturbance.…”

Section: Introductionmentioning

confidence: 99%

Radial Basis Function Neural Network Sliding Mode Control for Ship Path Following Based on Position Prediction

Zhang

2021

JMSE

View full text Add to dashboard Cite

In the process of ship navigation, due to the characteristics of large inertia and large time delay, overshoot can easily occur in the process of path following. Once the ship deviates from the waypoint, it is prone to grounding and collision. Considering this problem, a sliding mode control algorithm based on position prediction using the radial basis function (RBF) neural network is proposed. The desired heading angle is designed according to a backstepping algorithm. The hyperbolic tangent function is used to design the sliding surface, and the course is controlled by sliding mode control. The second-order Taylor expansion is used to predict the future position, the current error and future error functions are constructed, and the total errors are fed back to the desired heading angle. In the sliding mode control system, the RBF neural network is used to approximate the total unknown term, and a velocity observer is introduced to obtain the surge velocity and sway velocity. To verify the effectiveness of the algorithm, the mathematical model group (MMG) model is used for simulation. The simulation results show the effectiveness and superiority of the designed controller. Therefore, the RBF neural network sliding mode controller based on predicted position has robustness for ship path following.

show abstract

“…Aiming at compensating disadvantages of model-based and data-driven predictors with each other, a hybrid predictor consists of both of them. In order to deal with the low fidelity of a mathematical model and the effect of environmental disturbance, NNs have been used to surrogate related terms in the mathematical model [18], [19].…”

Section: Hybrid Predictormentioning

confidence: 99%

A Multiple-Output Hybrid Ship Trajectory Predictor With Consideration for Future Command Assumption

Kanazawa

Skulstad

et al. 2021

IEEE Sensors J.

View full text Add to dashboard Cite

Onboard sensors contribute to data-driven understanding of complex and nonlinear ship dynamics in real time. By using sensors, precise ship trajectory prediction plays a key role in intelligent collision avoidance. A hybrid predictor makes prediction based on a mathematical model of which error is compensated by a black-box model. A Multiple-output Hybrid Predictor (MHP), which makes a long-horizon prediction at a time based on onboard sensor data, was developed in the previous study. However, it can not handle a time series of future command assumption. This limitation hinders an MHP from being applied to the evaluation of future command assumption in the predictive decision making. A novel architecture of MHP presented in this study converts a long time series of future command assumption into a fixed-length model-based-predicted vessel state; then, it is included in inputs of a black-box error compensator. This idea is robust to multidimensionality of commands and long control horizon. Assuming a low-fidelity vessel model and a limited data are available, simulation experiments are conducted. The effect of environmental disturbances and maneuverings on the prediction performance is examined comprehensively for the first time. The present study successfully incorporates a long time series of future command assumption in an MHP. It reduces the mean error by 81.8% compared to a model-based predictor and by 45.6% compared to a data-driven predictor under Beaufort wind force scale 4 wave, wind, and ocean current. Present study expands the application of MHP to the predictive decision making of future autonomous ships.

show abstract

Path following of underactuated surface ships based on model predictive control with neural network

Cited by 12 publications

References 33 publications

Research on Intelligent Detection Algorithm of the Single Anchored Mooring Area for Maritime Autonomous Surface Ships

Research on Intelligent Detection Algorithm of the Single Anchored Mooring Area for Maritime Autonomous Surface Ships

Radial Basis Function Neural Network Sliding Mode Control for Ship Path Following Based on Position Prediction

A Multiple-Output Hybrid Ship Trajectory Predictor With Consideration for Future Command Assumption

Contact Info

Product

Resources

About