357 research outputs found
Intelligent Control Strategies for an Autonomous Underwater Vehicle
The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control
problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics
are highly non-linear, and the relative similarity between the linear and angular velocities about
each degree of freedom means that control schemes employed within other flight vehicles are not
always applicable. In such instances, intelligent control strategies offer a more sophisticated
approach to the design of the control algorithm. Neurofuzzy control is one such technique, which
fuses the beneficial properties of neural networks and fuzzy logic in a hybrid control architecture.
Such an approach is highly suited to development of an autopilot for an AUV.
Specifically, the adaptive network-based fuzzy inference system (ANFIS) is discussed in
Chapter 4 as an effective new approach for neurally tuning course-changing fuzzy autopilots.
However, the limitation of this technique is that it cannot be used for developing multivariable
fuzzy structures. Consequently, the co-active ANFIS (CANFIS) architecture is developed and
employed as a novel multi variable AUV autopilot within Chapter 5, whereby simultaneous control
of the AUV yaw and roll channels is achieved. Moreover, this structure is flexible in that it is
extended in Chapter 6 to perform on-line control of the AUV leading to a novel autopilot design
that can accommodate changing vehicle pay loads and environmental disturbances.
Whilst the typical ANFIS and CANFIS structures prove effective for AUV control system
design, the well known properties of radial basis function networks (RBFN) offer a more flexible
controller architecture. Chapter 7 presents a new approach to fuzzy modelling and employs both
ANFIS and CANFIS structures with non-linear consequent functions of composite Gaussian form.
This merger of CANFIS and a RBFN lends itself naturally to tuning with an extended form of the
hybrid learning rule, and provides a very effective approach to intelligent controller development.The Sea Systems and Platform Integration Sector,
Defence Evaluation and Research Agency, Winfrit
Safety Assurance of Non-Deterministic Flight Controllers in Aircraft Applications
Loss of control is a serious problem in aviation that primarily affects General Aviation. Technological advancements can help mitigate the problem, but the FAA certification process makes certain solutions economically unfeasible. This investigation presents the design of a generic adaptive autopilot that could potentially lead to a single certification for use in several makes and models of aircraft. The autopilot consists of a conventional controller connected in series with a robust direct adaptive model reference controller. In this architecture, the conventional controller is tuned once to provide outer-loop guidance and navigation to a reference model. The adaptive controller makes unknown aircraft behave like the reference model, allowing the conventional controller to successfully provide navigation without the need for retuning.
A strong theoretical foundation is presented as an argument for the safety and stability of the controller. The stability proof of direct adaptive controllers require that the plant being controlled has no unstable transmission zeros and has a nonzero high frequency gain. Because most conventional aircraft do not readily meet these requirements, a process known as sensor blending was used. Sensor blending consists of using a linear combination of the plantâs outputs that has no unstable transmission zeros and has a nonzero high frequency gain to drive the adaptive controller. Although this method does not present a problem for regulators, it can lead to a steady state error in tracking applications. The sensor blending theory was expanded to take advantage of the systemâs dynamics to allow for zero steady state error tracking. This method does not need knowledge of the specific systemâs dynamics, but instead uses the structure of the A and B matrices to perform the blending for the general case.
The generic adaptive autopilot was tested in two high-fidelity nonlinear simulators of two typical General Aviation aircraft. The results show that the autopilot was able to adapt appropriately to the different aircraft and was able to perform three-dimensional navigation and an ILS approach, without any modification to the controller. The autopilot was tested in moderate atmospheric turbulence, using consumer-grade sensors and actuators currently available in General Aviation aircraft. The generic adaptive autopilot was shown to be robust to atmospheric turbulence and sensor and actuator random noise. In both aircraft simulators, the autopilot adapted successfully to changes in airspeed, altitude, and configuration.
This investigation proves the feasibility of a generic autopilot using direct adaptive controller. The autopilot does not need a priori information of the specific aircraftâs dynamics to maintain its safety and stability arguments. Real-time parameter estimation of the aircraft dynamics are not needed. Recommendations for future work are provided
MODEL REFERENCE ADAPTIVE CONTROL-BASED GENETIC ALGORITHM DESIGN FOR HEADING SHIP MOTION
In this paper, the heading control of a large ship is enhanced with a specific end goal, to check the unwanted impact of the waves on the actuator framework. The Nomoto model is investigated to describe the shipâs guiding progression. First and second order models are considered here. The viability of the models is examined based on the principal properties of the Nomoto model. Different controllers are proposed, these are Proportional Integral Derivative (PID), Linear Quadratic Regulator (LQR) and Model Reference Adaptive Control Genetic optimization Algorithm (MRAC-GA) for a ship heading control. The results show that the MRAC-GA controller provides the best results to satisfy the design requirements. The Matlab/Simulink tool is utilized to demonstrate the proposed arrangement in the control loop
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 âaMPCâ 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
A SELF-ORGANISING FUZZY LOGIC AUTOPILOT FOR SMALL VESSELS
Currently small vessels use autopilots based on the Proportional plus Integral plus
Derivative (PID) algorithm which utilises fixed gain values. This type of autopilot is
known to often cause performance difficulties, a survey is therefore carried out to
identify the alternative autopilot methods that have been previously investigated. It
is shown that to date, all published work in this area has been based on large ships,
however, there are specific difficulties applicable to the small vessel which have therefore not been considered. After the recognition of artificial neural networks and
fuzzy logic as being the two most suitable techniques for use in the development of
a new, and adaptive, small vessel autopilot design, the basic concepts of both are
reviewed and fiizzy logic identified as being the most suitable for this application.
The remainder of the work herein is concerned with the development of a fuzzy
logic controller capable of a high level of performance in the two modes of coursekeeping
and course-changing. Both modes are integrated together by the use of nonlinear
fuzzy input windows. Improved performance is then obtained by using a nonlinear
fuzzy rulebase. Integral action is included by converting the fuzzy output
window to an unorthodox design described by two hundred and one fuzzy
singletons, and then by shifting the identified fuzzy sets to positive, or negative, in
order that any steady-state error may be removed from the vessel's performance.
This design generated significant performance advantages when compared to the
conventional PID autopilot. To develop further into an adaptive form of autopilot
called the self-organising controller, the single rulebase was replaced by two
enhancement matrices. These are novel features which are modified on-line by two
corresponding performance indices. The magnitude of the learning was related to
the observed performance of the vessel when expressed in terms of its heading error
and rate of change of heading error.
The autopilot design is validated using both simulation, and full scale sea trials.
From these tests it is demonstrated that when compared to the conventional PID
controller, the self-organising controller significantly improved performance for
both course-changing and course-keeping modes of operation. In addition, it has the
capability to learn on-line and therefore to maintain performance when subjected to
vessel dynamic or environmental disturbance alterations
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
Robust Adaptive Control of an Uninhabited Surface Vehicle
In this paper, we develop a novel and robust adaptive autopilot for uninhabited surface vehicles (USV). In practice, usually asudden change in dynamics results in aborted missions and the USV has to be rescued to avoid possible damage to other marine crafts inthe vicinity. This problem has been investigated in our innovative design, which enables the autopilot to cope well with significant changes in the system dynamics and empowers USVs to accomplish their desired missions. The model predictivecontrol technique is employed which adopts an online adaptive nature by utilising three algorithms. Even with random initialisation,significant improvements over the gradient descent and least squares approaches have been achieved by the modified weightedleast squares (WLS) method, which periodically reinitialising the covariance matrix. Extensive simulation studies have been performed to test and verify the advantages of the proposed method
A Predictive Fuzzy-Neural Autopilot for the Guidance of Small Motorised Marine Craft
This thesis investigates the design and evaluation of a control system, that is able to adapt
quickly to changes in environment and steering characteristics. This type of controller is
particularly suited for applications with wide-ranging working conditions such as those experienced
by small motorised craft.
A small motorised craft is assumed to be highly agile and prone to disturbances, being
thrown off-course very easily when travelling at high speed 'but rather heavy and sluggish
at low speeds. Unlike large vessels, the steering characteristics of the craft will change
tremendously with a change in forward speed. Any new design of autopilot needs to be to
compensate for these changes in dynamic characteristics to maintain near optimal levels of
performance.
This study identities the problems that need to be overcome and the variables involved.
A self-organising fuzzy logic controller is developed and tested in simulation. This type of
controller learns on-line but has certain performance limitations.
The major original contribution of this research investigation is the development of an
improved self-adaptive and predictive control concept, the Predictive Self-organising Fuzzy
Logic Controller (PSoFLC). The novel feature of the control algorithm is that is uses a
neural network as a predictive simulator of the boat's future response and this network is
then incorporated into the control loop to improve the course changing, as well as course
keeping capabilities of the autopilot investigated.
The autopilot is tested in simulation to validate the working principle of the concept and
to demonstrate the self-tuning of the control parameters. Further work is required to establish
the suitability of the proposed novel concept to other control
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