Path Following and Collision Avoidance of Underactuated Marine Vessels Based on MPC Design

2019 ◽  
Author(s):  
Guoping Zheng ◽  
Cheng Liu ◽  
Cheng Li
Keyword(s):  
Collision Avoidance ◽  
Predictive Control ◽  
Input Saturation ◽  
Control Design ◽  
Path Following ◽  
Shipping Industry ◽  
Computational Burden ◽  
Effectiveness And Efficiency ◽  
Projection Neural Network ◽  
Marine Vessels

Abstract Path following of underactuated marine vessels is a fundamental marine practice in shipping industry. However, the collision avoidance, which is frequently encountered during the process of path following of ships sailing in seaways, is neglected in traditional studies of path following. In this paper, a novel control design for path following with auxiliary system for collision avoidance is presented. Taking advantage of the capability of dealing with multi-variable system with the constraints, the model predictive control (MPC) method is employed to deal with the input saturation (rudder) and underactuated problem. Furthermore, the parallel computational nature of projection neural network (PNN) is included to reduce the computational burden of traditional MPC technique and make the control design more efficient. Simulations are conducted to validate the effectiveness and efficiency of the proposed control design.

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

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094595
Author(s):  
Ronghui Li ◽  
Ji Huang ◽  
Xinxiang Pan ◽  
Qionglei Hu ◽  
Zhenkai Huang
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Input Saturation ◽  
Path Following ◽  
Environmental Disturbances ◽  
Control Approach ◽  
Underactuated Ship ◽  
Mmg Model ◽  
The Mathematical Model

A model predictive control approach is proposed for path following of underactuated surface ships with input saturation, parameters uncertainties, and environmental disturbances. An Euler iterative algorithm is used to reduce the calculation amount of model predictive control. The matter of input saturation is addressed naturally and flexibly by taking advantage of model predictive control. The mathematical model group (MMG) model as the internal model improves the control accuracy. A radial basis function neural network is also applied to compensate the total unknowns including parameters uncertainties and environmental disturbances. The numerical simulation results show that the designed controller can force an underactuated ship to follow the desired path accurately in the case of input saturation and time-varying environmental disturbances including wind, current, and wave.

A Cooperative Driving NLMPC for Real Time Collision Avoidance

2015 ◽  
Author(s):  
Ugo Rosolia ◽  
Francesco Braghin ◽  
Edoardo Sabbioni ◽  
Andrew Alleyne ◽  
Stijn De Bruyne
Keyword(s):  
Model Predictive Control ◽  
Numerical Simulations ◽  
Linear Model ◽  
Collision Avoidance ◽  
Predictive Control ◽  
Optimal Solution ◽  
Path Following ◽  
Trajectory Control ◽  
Information Network ◽  
Cooperative Driving

A decentralized cooperative driving Non Linear Model Predictive Control (NLMPC) approach for path following and collision avoidance is presented in this paper. The proposed decentralized approach is based on an information network, which communicates when two or more vehicles are near and so they might collide. In the case in which vehicles are far, online trajectory control is independently computed on-board by means of a NLMPC. When two or more vehicles get closer, trajectory control is no more independently carried out: optimal solution for these vehicles is coupled and thus their trajectories are computed dependently. Performance of the proposed decentralized NLMPC for cooperative driving was assessed through numerical simulations involving two vehicles. Results were compared with ones of a centralized approach to assess optimality of the solution.

An efficient ship autopilot design using observer-based model predictive control

2020 ◽  
pp. 147509022092724 ◽  
Author(s):  
Wenxin Wang ◽  
Cheng Liu
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Design ◽  
Ship Motion ◽  
Traditional Model ◽  
Control Input ◽  
Strong Nonlinearity ◽  
Ship Autopilot ◽  
Projection Neural Network

An efficient model predictive control design for ship autopilot, which is a representative marine application, is proposed based on projection neural network in this article. Ship motion control at sea exhibits the characteristics of large inertia, strong nonlinearity, and large delay; furthermore, it is frequently influenced by the external disturbances, leading to a complex uncertain problem. In addition, the amplitude of control input—the rudder is constrained. Given the mechanism of on-line computing and the advantages of handling constraints, the model predictive control is one of the most favorable solutions for this problem. Nevertheless, the major challenge of the implementation of traditional model predictive control in application is the computation intensity. In this article, the capability of parallel computation of projection neural network is employed to optimize the objective function formulated by traditional model predictive control method, aiming to improve the computational efficiency. The overall information of ship motion is normally difficult to be obtained; therefore, a state observer should be also included. Extensive studies are conducted to illustrate the effectiveness of the proposed control design.

scholarly journals Model Predictive Control to Autonomously Approach a Failed Spacecraft

2018 ◽  
Vol 2018 ◽  
pp. 1-18
Author(s):  
Xun Wang ◽  
Zhaokui Wang ◽  
Yulin Zhang
Keyword(s):  
Model Predictive Control ◽  
Collision Avoidance ◽  
Predictive Control ◽  
Input Saturation ◽  
Sufficient Conditions ◽  
Control Input ◽  
Attitude Dynamics ◽  
Sequential Convex Programming ◽  
Angular Velocities ◽  
Tumbling Motion

In this study, a model predictive control (MPC) method is developed for a servicer spacecraft autonomously approaching a tumbling failed spacecraft at an ultraclose range. Flight safety and collision avoidance are basic requirements during the approach. Two types of a failed spacecraft with complex configurations are considered, and a double-ellipsoid composite envelope strategy is designed to model their keep-out zones. Given the keep-out zone of the servicer, two expanded ellipsoids are subsequently introduced to determine the collision and sufficient conditions for collision avoidance are derived by using the form of concave constraint. The tumbling motion of the target is considered, and a CW-based translational dynamics and derived attitude dynamics of the target are formulated to predict the motion of the docking point and keep-out zone. The MPC is formulated to drive the servicer tracking the docking point with collision avoidance and handle constraints including control input saturation and relative velocity bound. Convexification of the collision avoidance constraint and sequential convex programming are adopted for the implementation of MPC. Scenarios on the servicer with different initial positions approaching the target with different angular velocities are simulated, and the simulation results indicate that the proposed MPC method is effective.

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