Path following of under-actuated ships based on model predictive control with state observer

Li, Zongxuan; Li, Ronghui; Bu, Renxiang

doi:10.1007/s00773-020-00746-1

Cited by 23 publications

(10 citation statements)

References 23 publications

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“…Path following does not need to consider the time factor, so in this paper, the propeller speed is set as a fixed value, only considering the rudder angle input. Therefore, Equation (1) can be simplified as the design model of Equation ( 3) [17].…”

Section: Design Modelmentioning

confidence: 99%

“…In order to solve the problem of underactuated with less control input, the path following control problem is transformed into the heading control problem by the line-of-sight (LOS) guidance method [14]. LOS was originally used in automobile guidance, and later, Fossen applied it in the field of ships [15,16].On the basis of LOS guidance, reference [17] and reference [10] proposed a method of using a variable circle instead of a fixed circle to improve LOS performance, which can automatically adjust the radius of the receiving circle according to the angle J. Mar. Sci.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

2021

JMSE

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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.

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Section: Design Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

2021

JMSE

Self Cite

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“…A predictive ESO-based robust controller for uncertain process control applications was described by Pawar et al (2021). In Li et al (2020), a model predictive control method with linear ESO was presented to address the problem of UAV control under input constraints, uncertain parameters, and external disturbances without velocity measurement. Besides, the coupling disturbances of closed-loop system are usually supposed to have an upper bound to guarantee controller effectiveness, but it is difficult to detect the upper bound of the coupling disturbances.…”

Section: Introductionmentioning

confidence: 99%

Fixed-time robust trajectory tracking control for quadrotor UAV with disturbances

Shao

Wang

Zhao

et al. 2022

Transactions of the Institute of Measurement and Control

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Trajectory tracking control of nonlinear system with disturbances has been extensively investigated. A novel fixed-time robust trajectory tracking control scheme for quadrotor unmanned aerial vehicle (UAV) with disturbances is proposed in this paper. First, the quadrotor system is split into position and attitude loops. With the purpose of restraining coupling disturbances on position loop forced by imperfect attitude tracking, a novel fixed-time unknown input observer (UIO) is devised to accurately estimate the disturbances, with the unique feature that does not need the upper bound of disturbances or the derivatives. Second, taking unmeasurable velocity and unknown external disturbances into account for attitude loop, a fixed-time extended state observer (ESO) is proposed to precisely observe them within a fixed time. Third, considering control accuracy and convergence rate, robust fixed-time controllers are, respectively, designed for position and attitude loops, and the convergence time of overall system is independent of system initial states. Then, a comprehensive stability proof for the two-loop system is presented with a combination of bi-limit homogeneity and Lyapunov technique. Finally, simulations and comparisons are performed to demonstrate the effectiveness of the designed control scheme.

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“…The observer design method has been combined with various control theories and successfully applied to practical systems, such as gasoline engines and underwater robots [23,24]. In [25], the observer has been designed to estimate the surge and sway velocity for under-actuated ships. Using AIS data, the nonlinear observer has also been applied in designing the exogenous Kalman filter for the visualization and prediction of ship dynamics [26].…”

Section: Introductionmentioning

confidence: 99%

Development of Fuzzy Observer Gain Design Algorithm for Ship Path Estimation Based on AIS Data

2021

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This paper develops a Takagi-Sugeno fuzzy observer gain design algorithm to estimate ship motion based on Automatic Identification System (AIS) data. Nowadays, AIS data is widely applied in the maritime field. To solve the problem of safety, it is necessary to accurately estimate the trajectory of ships. Firstly, a nonlinear ship dynamic system is considered to represent the dynamic behaviors of ships. In the literature, nonlinear observer design methods have been studied to estimate the ship path based on AIS data. However, the nonlinear observer design method is challenging to create directly since some dynamic ship systems are more complex. This paper represents nonlinear ship dynamic systems by the Takagi-Sugeno fuzzy model. Based on the Takagi-Sugeno fuzzy model, a fuzzy observer design method is developed to solve the problem of estimating using AIS data. Moreover, the observer gains of the fuzzy observer can be adjusted systemically by a novel algorithm. Via the proposed algorithm, a more suitable or better observer can be obtained to achieve the objectives of estimation. Corresponding to different AIS data, the better results can also be obtained individually. Finally, the simulation results are presented to show the effectiveness and applicability of the proposed fuzzy observer design method. Some comparisons with the previous nonlinear observer design method are also given in the simulations.

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Path following of under-actuated ships based on model predictive control with state observer

Cited by 23 publications

References 23 publications

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

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

Fixed-time robust trajectory tracking control for quadrotor UAV with disturbances

Development of Fuzzy Observer Gain Design Algorithm for Ship Path Estimation Based on AIS Data

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