A neural network approach to ship track-keeping control

This paper presents an on-line trained neural net work controller for ship track-keeping problems. Following a brief review of the ship track-keeping control development since the 1980's, an analysis of various existing backpropagation-based neural controllers is provided. We then propose a single-input multioutput (SIMO) neural control strategy for situations where the exact mathematical dynamics of the ship are not available. The aim of this study is to build an autonomous neural controller which uses rudder to regulate both the tracking error and heading error. During the whole control process, the proposed SIMO neural controller adapts itself on-line from a direct evaluation of the control accuracy, and hence the need for a “teacher” or an off-line training process can be removed. With a relatively modest amount of quantitative knowledge of the ship behavior, the design philosophy enables real time control of a nonlinear ship model under random wind disturbances and measurement noise. Three different track-keeping tasks have been simulated to demonstrate the effectiveness of the training method and the robust performance of the proposed neural control strategy     

Adaptive Autopilot Design of Time-Varying Uncertain Ships With Completely Unknown Control Coefficient

This paper develops an adaptive course controller for time-varying parametric uncertain nonlinear ships with completely unknown time-varying bounded control coefficient. The proposed design method does not require any a priori knowledge of the sign of the unknown time-varying control coefficient. The designed adaptive autopilot can guarantee the regulation of the ship course to any prescribed accuracy and the global uniform ultimate boundedness of all signals in the closed-loop system. The effectiveness of the presented autopilot has been demonstrated in a simulation involving a ship of 45 m in length.     

Unscented Kalman Filter trained neural network control design for ship autopilot with experimental and numerical approaches

In the recent decades, the application and research of unmanned surface vessels are experiencing considerable growth, which have caused the demands of intelligent autopilots to grow along with the ever-growing requirements. In this study, the design of an autopilot based on Unscented Kalman Filter (UKF) trained Radial Basis Function Neural Networks (RBFNN) was presented. In particular, in order to provide satisfactory control performance for surface vessels with random external disturbances, the modified UKF was utilised as the weights training mechanism for the RBFNN based controller. The configurations of the newly developed free running scaled model, as well as the online signal processing method, were introduced to enable the experimental studies. The experimental and numerical tests were carried out through using the physical scaled model and corresponding mathematical model to validate the capability of the designed control system under various sailing conditions. The results indicated that the UKF RBFNN based autopilot satisfied the functionalities of course keeping, course changing and trajectory tracking only using the rudder as the actuator. It was concluded that the developed control scheme was effective to track the desired states and robust against unpredictable external disturbances. Moreover, in comparison with Back-Propagation (BP) RBFNN and Proportional-Derivative (PD) based autopilots, the UKF RBFNN based autopilot has the comparable capability in the aspects of providing smooth and effective control laws.     

Autopilot control synthesis for path tracking maneuvers of underwater vehicles

This paper is concerned with the robust control synthesis of autonomous underwater vehicle(AUV) for general path following maneuvers.First,we present maneuvering kinematics and vehicle dynamics in a unified framework.Based on H∞ loop-shaping procedure,the 2-DOF autopilot controller has been presented to enhance stability and path tracking.By use of model reduction,the high-order control system is reduced to one with reasonable order,and further the scaled low-order controller has been analyzed in both the frequency and the time domains.Finally,it is shown that the autopilot control system provides robust performance and stability against prescribed levels of uncertainty.     

The nonlinear hydrodynamic model for simulating a ship steering in waves with autopilot system

In the paper, a hydrodynamic numerical model including wave effect is developed to simulate ship autopilot systems by using the time domain analysis. The PD controller and the sliding mode controller are adopted as the autopilot systems. The differences of simulation results between two controllers are analyzed by cost function composed of heading angle error and rudder deflection, either in calm water or in waves. The results in calm water show that both controllers are tracking well for the desired route with the similar cost function value by tuning the key design parameters. However, the course tracking ability of the controller using sliding mode in waves is generally better even the cost function value is similar.     

Multivariable sliding mode control for autonomous diving andsteering of unmanned underwater vehicles

A six-degree-of-freedom model for the maneuvering of an underwater vehicle is used and a sliding-mode autopilot is designed for the combined steering, diving, and speed control functions. In flight control applications of this kind, difficulties arise because the system to be controlled is highly nonlinear and coupled, and there is a good deal of parameter uncertainty and variation with operational conditions. The development of variable-structure control in the form of sliding modes has been shown to provide robustness that is expected to be quite remarkable for AUV autopilot design. It is shown that a multivariable sliding-mode autopilot based on state feedback, designed assuming decoupled modeling, is quite satisfactory for the combined speed, steering, and diving response of a slow AUV. The influence of speed, modeling nonlinearity, uncertainty, and disturbances, can be effectively compensated, even for complex maneuvering. Waypoint acquisition based on line-of-sight guidance is used to achieve path tracking     

On the track keeping and roll reduction of the ship in random waves using different sliding mode controllers

In the paper, an autopilot system composed of sliding mode controller and line-of-sight guidance technique are adopted to navigate the ship in random waves by altering the rudder deflection. Two kinds of sliding mode controller are considered; one is the separate system including sway–yaw control and roll control, the other is the compact system considering sway–roll–yaw control altogether. Both track keeping and roll reduction are accomplished by rudder control and the design parameters of controller are optimized by genetic algorithm. The present simulation results show both the separate controller and the compact controller work quite well, either for track keeping or roll reduction while the ship is sailing in random waves. However, the separate controller is recommended due to its simplicity.     

Design of an adaptive controller for submersibles via multimodel gain scheduling

An adaptive autopilot for submarines via gain scheduling is introduced. The procedure used in the design is based on the stochastic controller and observer techniques. The autopilot is designed to operate in various sea conditions. Steady-state estimator gains corresponding to different wave heights are calculated and utilized in estimating the real wave height by switching the gain sets. Since large perturbations require large hydroplane angles which cannot be realizable due to their physical limits, different controller gain sets are employed for the appropriate operation of the hydroplanes. The presented controller is not confined to a particular sea state, and possesses robustness against possible changes in the external environment, exploiting the multimodel representation of the sea state.     

An internal model control approach to the design ofyaw-rate-control ship-steering autopilot

This technical communication is concerned with the design of inland ship-steering autopilots characterized by turning rate regulation rather than heading regulation found on most ocean-going vessel autopilots. The autopilot design is based on the internal model control approach which allows clear connections between the controller structure and that of the ship model to be established. To fully utilize the limited rudder power without introducing the controller wind-up, which may lead to undesirable long transients, an inverse of the rudder saturation (SAT) and slew rate limitation (SRL) is implemented in the controller. Specifically, an inversion by feedback technique is employed by implementing the noninverted dynamics in a local feedback loop. This resolves the difficulty associated with inversion of the SAT and SRL mathematically. Consequently, high turning rate maneuver can be achieved in case of emergency encounters without causing the controller wind-up. This feature is particularly important for inland ships, since unexpected encounters with other ships are quite common and the capability of quick response is vital to ensure safe maneuvers     

Robust control based on feedback linearization for roll stabilizing of autonomous underwater vehicle under wave disturbances

In the case of Autonomous Underwater Vehicle(AUV) navigating with low speed near water surface,a new method for design of roll motion controller is proposed in order to restrain wave disturbance effectively and improve roll stabilizing performance.Robust control is applied,which is based on uncertain nonlinear horizontal motion model of AUV and the principle of zero speed fin stabilizer.Feedback linearization approach is used to transform the complex nonlinear system into a comparatively simple linear system.For parameter uncertainty of motion model,the controller is designed with mixed-sensitivity method based on H-infinity robust control theory.Simulation results show better robustness improved by this control method for roll stabilizing of AUV navigating near water surface.     

基于无记忆状态观测器的多时滞不确定非线性系统的自适应控制

针对一类多时滞不确定非线性系统,研究了基于无记忆状态观测器的自适应控制问题.时滞状态扰动的上界未知,在控制中通过自适应律估计未知参数,并利用估计值设计了不依赖于时滞的无记忆状态观测器和控制器,基于Lyapunov-Krasovskii函数证明了观测误差渐近收敛到零.最后仿真结果说明了该方法的有效性.     

The NDRE-AUV flight control system

The flight control system of an autonomous underwater vehicle (AUV) developed at the Norwegian Defence Research Establishment (NDRE) is presented. A mathematical model of the vehicle is derived and discussed. The system is separated into lightly interacting subsystems, and three autopilots are designed for steering, diving, and speed control. The design of the separate controllers is based on PID techniques. Results from extensive sea testing show robust performance and stability for the autopilot     

Robust Nonlinear Path-Following Control of an AUV

This paper develops a robust nonlinear controller that asymptotically drives the dynamic model of an autonomous underwater vehicle (AUV) onto a predefined path at a constant forward speed. A kinematic controller is first derived, and extended to cope with vehicle dynamics by resorting to backstepping and Lyapunov-based techniques. Robustness to vehicle parameter uncertainty is addressed by incorporating a hybrid parameter adaptation scheme. The resulting nonlinear adaptive control system is formally shown and it yields asymptotic convergence of the vehicle to the path. Simulations illustrate the performance of the derived controller .      

水下机器人-机械臂系统的滑模自抗扰控制

针对水下机器人机械臂系统的强耦合、强非线性、复杂海洋多源干扰等因素影响,提出了滑模自抗扰控制器,将复杂系统模型转变为简单的积分串联系统,将内部参数不确定性、测量误差、建模误差和海洋多源干扰等扰动归结为总扰动,并采用线性扩张观测器对其进行估计并抵消。利用滑模控制器提高系统对参数摄动的不敏感性,增强控制系统的抗干扰性能,通过李雅普诺夫理论分析了控制系统的有界稳定性。仿真结果表明滑模自抗扰与传统滑模控制和自抗扰控制相比,能使水下机器人机械臂实现更好的轨迹跟踪,且系统具有更好的抗干扰能力。     

Adaptive output feedback control based on DRFNN for AUV

The tracking control problem of AUV in six degrees-of-freedom (DOF) is addressed in this paper. In general, the velocities of the vehicles are very difficult to be accurately measured, which causes full state feedback scheme to be not feasible. Hence, an adaptive output feedback controller based on dynamic recurrent fuzzy neural network (DRFNN) is proposed, in which the location information is only needed for controller design. The DRFNN is used to online estimate the dynamic uncertain nonlinear mapping. Compared to the conventional neural network, DRFNN can clearly improve the tracking performance of AUV due to its less inputs and stronger memory features. The restricting condition for the estimation of the external disturbances and network's approximation errors, which is often given in the existing literatures, is broken in this paper. The stability analysis is given by Lyapunov theorem. Simulations illustrate the effectiveness of the proposed control scheme.     

Evaluation of the effects of the communication cable on the dynamics of an underwater flight vehicle

This paper presents a numerical scheme to evaluate the effects of the communication cable attached to an underwater flight vehicle. Both simulation and model validation results show that the numerical scheme is effective and provides a means for developing a feed-forward controller to compensate for the cable effects when developing an autopilot for the tethered vehicle. Moreover, the numerical scheme can also be applied to predict the effects of the ROVs umbilical during its deployment.     

Fuzzy logic for depth control of Unmanned Undersea Vehicles

Fuzzy logic is a viable control strategy for depth control of undersea vehicles. It has been applied to the low speed ballast control problem for ARPA's Unmanned Undersea Vehicle (UUV), designed and built by Draper Laboratory. A fuzzy logic controller has been designed and tested in simulation that issues pump commands to effect changes in the UUV depth, while also regulating the pitch angle of the vehicle. The fuzzy logic controller performs comparably to the current ballast control design. The controller is also less sensitive to variations in the vehicle configuration and dynamics. The benefits of the fuzzy logic approach for this problem are: 1) simplicity, by not requiring a dynamic model, thus allowing for rapid development of a working design and less sensitivity to plant variations; 2) better matching of the control strategy and complexity with performance objectives and limitations; 3) the insight provided and easy modification of the controller, through the use of linguistic rules     

Multi-variable adaptive back-stepping control of submersibles using SDU decomposition

A multi-variable adaptive autopilot for the dive-plane control of submarines is designed. The vehicle is equipped with bow and stern hydroplanes for maneuvering. It is assumed that the system parameters are not known, and the disturbance force is acting on the vehicle. Based on a back-stepping design approach, an adaptive control law is derived for the trajectory control of the depth and the pitch angle. To prevent singularity in the control law, the SDU decomposition of the high-frequency gain matrix is used for the design. In the closed-loop system, asymptotic tracking of the reference depth and pitch angle trajectories is accomplished. Simulation results are presented which show that the submarine performs dive-plane maneuvers in spite of the uncertainties in the system parameters and disturbance forces.     

海水有机磷农药实时数据采集系统设计

为实现海水中有机磷浓度现场监测,采用由单片机和工控机组成的海水有机磷农药实时数据采集系统的设计.给出了由PIC单片机构成的采集控制模块硬件设计原理和软件设计方法,工控机的程序流程和浓度的算法.     

A simplified approach to design fuzzy logic controller for an underwater vehicle

Fuzzy logic controller (FLC) performance is greatly dependent on its inference rules. In most cases, the more rules being applied to an FLC, the accuracy of the control action is enhanced. Nevertheless, a large set of rules requires more computation time. As a result, an FLC implementation requires fast and high performance processors. This paper describes a simplified control scheme to design a fuzzy logic controller (FLC) for an underwater vehicle namely, deep submergence rescue vehicle (DSRV). The proposed method, known as the single input fuzzy logic controller (SIFLC), reduces the conventional two-input FLC (CFLC) to a single input FLC. The SIFLC offers significant reduction in rule inferences and simplifies the tuning process of control parameters. The performance of the proposed controller is validated via simulation by using the marine systems simulator (MSS) on the Matlab/Simulink^® platform. During simulation, the DSRV is subjected to ocean wave disturbances. The results indicate that the SIFLC, Mamdani and Sugeno type CFLC give identical response to the same input sets. However, an SIFLC requires very minimum tuning effort and its execution time is in the orders of two magnitudes less than CFLC.