198 research outputs found
A Study on the Automatic Ship Control Based on Adaptive Neural Networks
Recently, dynamic models of marine ships are often required to design advanced control systems. In practice, the dynamics of marine ships are highly nonlinear and are affected by highly nonlinear, uncertain external disturbances. This results in parametric and structural uncertainties in the dynamic model, and requires the need for advanced robust control techniques. There are two fundamental control approaches to consider the uncertainty in the dynamic model: robust control and adaptive control. The robust control approach consists of designing a controller with a fixed structure that yields an acceptable performance over the full range of process variations. On the other hand, the adaptive control approach is to design a controller that can adapt itself to the process uncertainties in such a way that adequate control performance is guaranteed.
In adaptive control, one of the common assumptions is that the dynamic model is linearly parameterizable with a fixed dynamic structure. Based on this assumption, unknown or slowly varying parameters are found adaptively. However, structural uncertainty is not considered in the existing control techniques. To cope with the nonlinear and uncertain natures of the controlled ships, an adaptive neural network (NN) control technique is developed in this thesis. The developed neural network controller (NNC) is based on the adaptive neural network by adaptive interaction (ANNAI). To enhance the adaptability of the NNC, an algorithm for automatic selection of its parameters at every control cycle is introduced. The proposed ANNAI controller is then modified and applied to some ship control problems.
Firstly, an ANNAI-based heading control system for ship is proposed. The performance of the ANNAI-based heading control system in course-keeping and turning control is simulated on a mathematical ship model using computer. For comparison, a NN heading control system using conventional backpropagation (BP) training methods is also designed and simulated in similar situations. The improvements of ANNAI-based heading control system compared to the conventional BP one are discussed.
Secondly, an adaptive ANNAI-based track control system for ship is developed by upgrading the proposed ANNAI controller and combining with Line-of-Sight (LOS) guidance algorithm. The off-track distance from ship position to the intended track is included in learning process of the ANNAI controller. This modification results in an adaptive NN track control system which can adapt with the unpredictable change of external disturbances. The performance of the ANNAI-based track control system is then demonstrated by computer simulations under the influence of external disturbances.
Thirdly, another application of the ANNAI controller is presented. The ANNAI controller is modified to control ship heading and speed in low-speed maneuvering of ship. Being combined with a proposed berthing guidance algorithm, the ANNAI controller becomes an automatic berthing control system. The computer simulations using model of a container ship are carried out and shows good performance.
Lastly, a hybrid neural adaptive controller which is independent of the exact mathematical model of ship is designed for dynamic positioning (DP) control. The ANNAI controllers are used in parallel with a conventional proportional-derivative (PD) controller to adaptively compensate for the environmental effects and minimize positioning as well as tracking error. The control law is simulated on a multi-purpose supply ship. The results are found to be encouraging and show the potential advantages of the neural-control scheme.1. Introduction = 1
1.1 Background and Motivations = 1
1.1.1 The History of Automatic Ship Control = 1
1.1.2 The Intelligent Control Systems = 2
1.2 Objectives and Summaries = 6
1.3 Original Distributions and Major Achievements = 7
1.4 Thesis Organization = 8
2. Adaptive Neural Network by Adaptive Interaction = 9
2.1 Introduction = 9
2.2 Adaptive Neural Network by Adaptive Interaction = 11
2.2.1 Direct Neural Network Control Applications = 11
2.2.2 Description of the ANNAI Controller = 13
2.3 Training Method of the ANNAI Controller = 17
2.3.1 Intensive BP Training = 17
2.3.2 Moderate BP Training = 17
2.3.3 Training Method of the ANNAI Controller = 18
3. ANNAI-based Heading Control System = 21
3.1 Introduction = 21
3.2 Heading Control System = 22
3.3 Simulation Results = 26
3.3.1 Fixed Values of n and = 28
3.3.2 With adaptation of n and r = 33
3.4 Conclusion = 39
4. ANNAI-based Track Control System = 41
4.1 Introduction = 41
4.2 Track Control System = 42
4.3 Simulation Results = 48
4.3.1 Modules for Guidance using MATLAB = 48
4.3.2 M-Maps Toolbox for MATLAB = 49
4.3.3 Ship Model = 50
4.3.4 External Disturbances and Noise = 50
4.3.5 Simulation Results = 51
4.4 Conclusion = 55
5. ANNAI-based Berthing Control System = 57
5.1 Introduction = 57
5.2 Berthing Control System = 58
5.2.1 Control of Ship Heading = 59
5.2.2 Control of Ship Speed = 61
5.2.3 Berthing Guidance Algorithm = 63
5.3 Simulation Results = 66
5.3.1 Simulation Setup = 66
5.3.2 Simulation Results and Discussions = 67
5.4 Conclusion = 79
6. ANNAI-based Dynamic Positioning System = 80
6.1 Introduction = 80
6.2 Dynamic Positioning System = 81
6.2.1 Station-keeping Control = 82
6.2.2 Low-speed Maneuvering Control = 86
6.3 Simulation Results = 88
6.3.1 Station-keeping = 89
6.3.2 Low-speed Maneuvering = 92
6.4 Conclusion = 98
7. Conclusions and Recommendations = 100
7.1 Conclusion = 100
7.1.1 ANNAI Controller = 100
7.1.2 Heading Control System = 101
7.1.3 Track Control System = 101
7.1.4 Berthing Control System = 102
7.1.5 Dynamic Positioning System = 102
7.2 Recommendations for Future Research = 103
References = 104
Appendixes A = 112
Appendixes B = 11
Simulation Studies Relating to Rudder Roll Stabilization of a Container Ship Using Neural Networks
International audienceRRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully
Automatic Control and Routing of Marine Vessels
Due to the intensive development of the global economy, many problems are constantly emerging connected to the safety of shipsβ motion in the context of increasing marine traffic. These problems seem to be especially significant for the further development of marine transportation services, with the need to considerably increase their efficiency and reliability. One of the most commonly used approaches to ensuring safety and efficiency is the wide implementation of various automated systems for guidance and control, including such popular systems as marine autopilots, dynamic positioning systems, speed control systems, automatic routing installations, etc. This Special Issue focuses on various problems related to the analysis, design, modelling, and operation of the aforementioned systems. It covers such actual problems as tracking control, path following control, ship weather routing, course keeping control, control of autonomous underwater vehicles, ship collision avoidance. These problems are investigated using methods such as neural networks, sliding mode control, genetic algorithms, L2-gain approach, optimal damping concept, fuzzy logic and others. This Special Issue is intended to present and discuss significant contemporary problems in the areas of automatic control and the routing of marine vessels
Simulation studies relating to rudder roll stabilization of a container ship using neural networks
RRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully
An adaptive autopilot design for an uninhabited surface vehicle
An adaptive autopilot design for an uninhabited surface vehicle
Andy SK Annamalai
The work described herein concerns the development of an innovative approach to the
design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of
autonomous missions, uninhabited surface vehicles must be able to operate with a minimum
of external intervention. Existing strategies are limited by their dependence on a fixed
model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect
on performance. This thesis presents an approach based on an adaptive model predictive
control that is capable of retaining full functionality even in the face of sudden changes in
dynamics.
In the first part of this work recent developments in the field of uninhabited surface vehicles
and trends in marine control are discussed. Historical developments and different strategies
for model predictive control as applicable to surface vehicles are also explored. This thesis
also presents innovative work done to improve the hardware on existing Springer
uninhabited surface vehicle to serve as an effective test and research platform. Advanced
controllers such as a model predictive controller are reliant on the accuracy of the model to
accomplish the missions successfully. Hence, different techniques to obtain the model of
Springer are investigated. Data obtained from experiments at Roadford Reservoir, United
Kingdom are utilised to derive a generalised model of Springer by employing an innovative
hybrid modelling technique that incorporates the different forward speeds and variable
payload on-board the vehicle. Waypoint line of sight guidance provides the reference
trajectory essential to complete missions successfully.
The performances of traditional autopilots such as proportional integral and derivative
controllers when applied to Springer are analysed. Autopilots based on modern controllers
such as linear quadratic Gaussian and its innovative variants are integrated with the
navigation and guidance systems on-board Springer. The modified linear quadratic
Gaussian is obtained by combining various state estimators based on the Interval Kalman
filter and the weighted Interval Kalman filter.
Change in system dynamics is a challenge faced by uninhabited surface vehicles that result
in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms
are analysed and an innovative, adaptive autopilot based on model predictive control is
designed. The acronym βaMPCβ is coined to refer to adaptive model predictive control that
is obtained by combining the advances made to weighted least squares during this research
and is used in conjunction with model predictive control. Successful experimentation is
undertaken to validate the performance and autonomous mission capabilities of the adaptive
autopilot despite change in system dynamics.EPSRC (Engineering and Physical Sciences Research Council
Nonlinear steering control under input magnitude and rate constraints with exponential convergence
A ship steering control is designed for a nonlinear maneuvering model whose
rudder manipulation is constrained in both magnitude and rate. In our method,
the tracking problem of the target heading angle with input constraints is
converted into the tracking problem for a strict-feedback system without any
input constraints. To derive this system, hyperbolic tangent () function
and auxiliary variables are introduced to deal with the input constraints.
Furthermore, using the feature of the derivative of function, auxiliary
systems are successfully derived in the strict-feedback form. The backstepping
method is utilized to construct the feedback control law for the resulting
cascade system. The proposed steering control is verified in numerical
experiments, and the result shows that the tracking of the target heading angle
is successful using the proposed control law.Comment: 12 pages, 6 figures, a preprint submitted to the Journal of Marine
Science and Technolog
DESIGN CONTROL OF SURFACE MARINE VEHICLE USING DISTURBANCE COMPENSATING MODEL PREDICTIVE CONTROL (DC-MPC)
This research studied ship motion control by considering four degrees of freedom (DoF): yaw, roll, sway, and surge in which comprehensive mathematical modeling forming a nonlinear differential equation. Furthermore, this research also investigated solutions for fundamental yet challenging steering problems of ship maneuvering using advanced control method: Disturbance Compensating Model Predictive Control (DC-MPC) method, which based on Model Predictive Control (MPC). The DC-MPC allows optimizing a compensated control then consider sea waves as the environmental disturbances. Those sea waves influence the control and also becomes one of the constraints for the system. The simulation compared the varying condition of Horizon Prediction (Np) and another method showing that the DC-MPC can manage well the given disturbances while maneuvering in certain Horizon Prediction. The results revealed that the ship is stable and follows the desired trajector
Non-Linear Robust Observers For Systems With Non-Collocated Sensors And Actuators
Challenges in controlling highly nonlinear systems are not limited to the development of sophisticated control algorithms that are tolerant to significant modeling imprecision and external disturbances. Additional challenges stem from the implementation of the control algorithm such as the availability of the state variables needed for the computation of the control signals, and the adverse effects induced by non-collocated sensors and actuators.
The present work investigates the adverse effects of non-collocated sensors and actuators on the phase characteristics of flexible structures and the ensuing implications on the performance of structural controllers. Two closed-loop systems are considered and their phase angle contours have been generated as functions of the normalized sensor location and the excitation frequency. These contours were instrumental in the development of remedial actions for rendering structural controllers immune to the detrimental effects of non-collocated sensors and actuators.
Moreover, the current work has focused on providing experimental validation for the robust performances of a self-tuning observer and a sliding mode observer. The observers are designed based on the variable structure systems theory and the self-tuning fuzzy logic scheme. Their robustness and self-tuning characteristics allow one to use an imprecise model of the system and eliminate the need for the extensive tuning associated with a fixed rule-based expert fuzzy inference system. The first phase of the experimental work was conducted in a controlled environment on a flexible spherical robotic manipulator whose natural frequencies are configuration-dependent. Both controllers have yielded accurate estimates of the required state variables in spite of significant modeling imprecision.
The observers were also tested under a completely uncontrolled environment, which involves a 16-ft boat operating in open-water under different sea states. Such an experimental work necessitates the development of a supervisory control algorithm to perform PTP tasks, prescribed throttle arm and steering tasks, surge speed and heading tracking tasks, or recovery maneuvers. This system has been implemented herein to perform prescribed throttle arm and steering control tasks based on estimated rather than measured state variables. These experiments served to validate the observers in a completely uncontrolled environment and proved their viability as reliable techniques for providing accurate estimates for the required state variables
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