Intelligent controller design for the ship steering problem

Enab, Yehia M.

doi:10.1049/ip-cta:19960119

Cited by 25 publications

(8 citation statements)

References 8 publications

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“…The human behavior complexity and adaptability is, of course, a great advantage to the control designer. Since the designer can rely upon the human to make the most of any control system [3,4]. It is this detail which probably constitutes the most important reason for using persons in control loops.…”

Section: Discussionmentioning

confidence: 99%

“…The different phases of the controller design are invoked through the simulation program as illustrated in the following sections [3,4].…”

Section: Computer Simulationmentioning

confidence: 99%

“…HO is often expert in keeping the complex control system on the right track. The design is based on a concept of developing a mathematical Model for Human Operator Behavior(HOBM) [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Microelectronic controller design for pole balancing problem

Elnaby

Enab²,

Hemeda³

2000

ICM'99. Proceedings. Eleventh International Conference on Microelectronics (IEEE Cat. No.99EX388)

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The reason for the present upsurge of interest in intelligent control is that the present generations of control systems are incapable, to a greater or lesser extent, of dealing with problems characterized by a certain complexity. Fortunately, human operators (HO) are often experts in keeping the complex control systems on the right track. Among intelligent controller design techniques, Neurocontrol is a dynamic research field that has attached considerable attention from the scientific and control engineering community in the last several years. In this paper, the template learning Neurocontrol approach has been used for controller design where the HO, the most successful intelligent controller available until now, is the template controller. This approach has been applied to pole balancing problem. Several structures of feedforward neural networks had been used to implement the controller and their relative advantages and disadvantages were investigated.The resulting controller succeeded to balance the pole for reasonable time. The approach seemed to be useful in building complex non-linear systems controllers where no need for system models and ease of development.

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Section: Discussionmentioning

confidence: 99%

“…The different phases of the controller design are invoked through the simulation program as illustrated in the following sections [3,4].…”

Section: Computer Simulationmentioning

confidence: 99%

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Microelectronic controller design for pole balancing problem

Elnaby

Enab²,

Hemeda³

2000

ICM'99. Proceedings. Eleventh International Conference on Microelectronics (IEEE Cat. No.99EX388)

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“…The dynamic model of synchronous motor in d-q frame can be represented by the following equation [9]: θ ω (6) For more details about the machine, the reader can review [9].…”

Section: B Inner Loopmentioning

confidence: 99%

“…Also, the human can learn to control many systems without prior knowledge of system response behavior. HO behavior is modeled mathematically instead of modeling the process itself to develop the controller directly [6].…”

Section: Introductionmentioning

confidence: 99%

Double fuzzy logic control for the ship path following

Fraga

Liu

2011

2011 2nd International Conference on Intelligent Control and Information Processing

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Path following is an issue of the ship modern industry. However, the main problem of ships is the conventional controllers that are based on PID algorithms, and are generally set to work under specific conditions. In this paper, two fuzzy logic controllers are designed to force underactuated ships to follow a desired path, despite of the presence of environmental disturbances induced by wave, wind and ocean-current. Numerical simulations using real ship characteristics are provided to illustrate the effectiveness and the robustness of the proposed methodology for the path following of underactuated ships.

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Neural Controllers

Garg¹,

Kumar²

2007

Wiley Encyclopedia of Computer Science and Engineering

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Conventional control techiques such as DIP control, robust control, and optimal control have traditionally been used to control linear and nonlinear systems. These techniques are based on the model of the system, and their performance is largely dependent on the accuracy of the model. Modern control systems are charactgerized by their increassing complexity, nonlinearity, intelligence, enhanced ability to adapt and learn, and roubstness to uncertainty. Often, the dynamics of these systems are time‐delayed, ill‐defined, extremely nonlinear, and very complex, which makes it difficult to obtain an accurate analytical model of the system. This reality added to the uncertainties present in the environment and measurement devices, makes it particularly challenging to employ conventional techniques to control such systems. The ability of neural networks to handle nonlinear and ill‐defined problems, perform in the presence of noise and uncertainty, learn and adapt to changes in system parameters, and approximate functions and identify nonlinear systems have made them a popular choice for controlling such complex systems. Extensive research has been carried out over the last two decades for using neural networks to control nonlinear systems, and several neural network architectures have been proposed. This progress has led to a widespread application of neutral controllers in several of fields, such as the aerospace, communications, automated manufacturing, robotics, medical diagnosis systems, electric power systems, and process industries.

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Intelligent controller design for the ship steering problem

Cited by 25 publications

References 8 publications

Microelectronic controller design for pole balancing problem

Microelectronic controller design for pole balancing problem

Double fuzzy logic control for the ship path following

Neural Controllers

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