fuzzy logic control system for autonomous sailboats

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Fuzzy Logic Control System for Autonomous Sailboats

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International Journal of Electrical and Computer Engineering (IJECE)

Automatic ship heading control is a part of the automatic navigation system. It is charged with the task of maintaining the actual ship's course angle or actual ship's course without human intervention in accordance with the set course or setting parameter and maintaining this condition under the effect of disturbing influences. Thus, the corrective influence on deviations from a course can be rendered by the position of a rudder or controlling influence that leads to the rotary movement of a vessel around a vertical axis that represents a problem, which can be solved with the use of fuzzy logic. In this paper, we propose to consider the estimation of the efficiency of fuzzy controllers in systems of automatic control of ship movement, obtained by analysis of a method of the formalized record of a logic conclusion and structure of the fuzzy controller. The realization of this allows to carry out effective stabilization of a course angle of a vessel taking into account existing restrictions.

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This article discusses a control architecture for autonomous sailboat navigation and also presents a sailboat prototype built for experimental validation of the proposed architecture. The main goal is to allow long endurance autonomous missions, such as ocean monitoring. As the system propulsion relies on wind forces instead of motors, sailboat techniques are introduced and discussed, including the needed sensors, actuators and control laws. Mathematical modeling of the sailboat, as well as control strategies developed using PID and fuzzy controllers to control the sail and the rudder are also presented. Furthermore, we also present a study of the hardware architecture that enables the system overall performance to be increased. The sailboat movement can be planned through predetermined geographical way-points provided by a base station. Simulated and experimental results are presented to validate the control architecture, including tests performed on a lake. Underwater robotics can...

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Fuzzy Logic Control System for Autonomous Sailboats

Artificial Intelligence | Basic Sailing Skills | FUZZIEEE 2007 | Fuzzy Inference Systems | Rudder |

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Novel robust-optimal controllers based on fuzzy descriptor system

They show excellent performance in controlling the challenging rotary inverted pendulum, demonstrating great potential for autonomous systems.

Nonlinear systems have applications in many diverse fields from robotics to economics. Unlike linear systems, the output is not proportional to the input is such systems. A classic example is the motion of a pendulum. Due to the inherent nature of nonlinear systems, their mathematical modelling and, consequently, control is difficult. In this context, the Takagi-Sugeno (T-S) fuzzy system emerges as a highly effective tool. This system leverages fuzzy logic to map input and output values to approximate a nonlinear system as multiple linear systems which are easier to model. Fuzzy logic is a form of mathematical logic in which, instead of requiring all statements to be true (1) or false (0), the truth values can be any value between 0 and 1. T-S fuzzy system has thus served as the foundation for several nonlinear control methods, with the Parallel Distributed Compensator (PDC) method being the most prominent.

Furthermore, scientists have developed an enhanced version of this system, known as the fuzzy descriptor system (FDS). It combines the T-S fuzzy system with the powerful space-state representation, which describes a physical system in terms of state variables, input variables, and output variables. Despite extensive research, optimal control strategies in the context of T-S FDSs are still largely unexplored. Additionally, while robust control methods, which protect against disturbances, have been explored for T-S FDS using methods like Linear Matrix Inequalities (LMI), these methods introduce additional complexity and optimization challenges.

To overcome these limitations, a group of researchers, led by Associate Professor Ngoc-Tam Bui from the Innovative Global Program of the College of Engineering at Shibaura Institute of Technology in Japan and including Thi-Van-Anh Nguyen, Quy-Thinh Dao, and Duc-Binh Pham, all from Hanoi University of Science and Technology, developed novel optimal and robust-optimal controllers based on the T-S fuzzy descriptor model. Their study was published in the journal Scientific Reports on March 07, 2024.

To develop the controllers, the team first utilized the powerful Lyapunov stability theory to establish the stability conditions for the mathematical model of the FDS. However, these stability conditions cannot be directly used. As Dr. Bui explains, "The stability conditions for the FDS model make it difficult to solve using established mathematical tools. To make them more amenable, we systematically transformed them into LMI." These modified conditions formed the basis for developing three controllers: the stability controller which uses PDC to manage deviations, the optimal controller which minimizes a cost function to obtain optimal control, and the robust-optimal controller which combines the benefits of both of them.

The researchers demonstrated the effectiveness of these controllers in controlling a rotary inverted pendulum, a challenging system comprising an inverted pendulum sitting on a rotating base. The problem is to keep the pendulum upright by controlling the rotation of the base. The researchers tested the performance of the controllers using distinct simulation scenarios. Simulations revealed that the stability controller effectively stabilized the system when the initial displacement angle was small, while with larger initial angles, there were more oscillations, and the settling time was higher. The high settling time was effectively addressed by the optimal controller, reducing it from 13 to 2 seconds, representing a six-fold reduction. Moreover, it also reduced the maximum amplitudes during oscillations.

The robust-optimal controller was tested using two different scenarios. In the first case, the mass of the pendulum bar was changed, while in the second, white noise was introduced into the control input. Compared to the optimal controller, it performed the same in the first scenario. However, the controller was considerably better in the second scenario, showing no oscillations while the optimal controller showed clear oscillations. Notably, the robust-optimal controller showed the lowest error values.

These results highlight the adaptability and potential of these controllers in practical scenarios. "The research findings hold promising implications for various real-life applications where stable control in dynamic and uncertain environments is paramount. Specifically, autonomous vehicles and industrial robots can achieve enhanced performance and adaptability using the proposed controllers," remarks Dr. Bui. "Overall, our research opens avenues for advancing control strategies in various domains, ultimately contributing to more capable autonomous systems, making transportationsafer, healthcare more effective, and manufacturing more efficient."

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Journal Reference :

• Duc-Binh Pham, Quy-Thinh Dao, Ngoc-Tam Bui, Thi-Van-Anh Nguyen. Robust-optimal control of rotary inverted pendulum control through fuzzy descriptor-based techniques . Scientific Reports , 2024; 14 (1) DOI: 10.1038/s41598-024-56202-2

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An Embedded Low-Power Control System for Autonomous Sailboats

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• J. Cabrera-Gámez 3 , 4 ,
• A. Ramos de Miguel 3 ,
• A. C. Domínguez-Brito 3 , 4 ,
• J. D. Hernández-Sosa 3 , 4 ,
• J. Isern-González 4 &
• E. Fernández-Perdomo 4  

1047 Accesses

8 Citations

This work presents a small and affordable autonomous sailboat platform designed to be transported and operated by one or two people without any special means. The sailboat is based on a RC One Meter class vessel equipped with a low power 8-bit microcontroller board and a set of navigation sensors (compass, GPS, wind vane, ...) and a 868 MHz RF module. It has been designed to serve as a low cost replicable testbed platform for research in autonomous sailing. The embedded control system makes the sailboat completely autonomous to sail a route determined as a sequence of waypoints, adapting its sailing point dynamically to wind conditions. The control system is completed with an off-board base station that permits to monitor and control the boat or defining a new route. The system is characterized by its long autonomy and robustness in case of communication failures.

This work has been partially funded by Canary Government and FEDER funds under ACIISI ProId2010/0062

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XBee 868 Pro specifications, http://www.digi.com/products/wireless-wired-embedded-solutions/zigbee-rf-modules/point-multipoint-rfmodules/xbee-pro-868#specs

A1084 GPS receiver hardware manual, http://ec-mobile.ru/user_files/File/Tyco/A1084_HM_V1.0.pdf

Libelium’s Waspmote manual, http://www.libelium.com/v11-files/documentation/waspmote/waspmote-technical_guide_eng.pdf

PNI’s legacy TCM2.5 electronic compass manual, http://www.pnicorp.com/download/347/99/TCM2.52.6Manualr09.pdf

US Digital absolute encoder MA3, http://www.usdigital.com/products/encoders/absolute/rotary/shaft/ma3

ACS712 product page, https://www.sparkfun.com/products/8883

LibXBee library, http://code.google.com/p/libxbee/

EFLL fuzzy logic library, https://github.com/zerokol/eFLL

ATIRMA video, http://www.youtube.com/watch?v=JoCVoFabJMg

Stelzer, R., Pröll, T.: Autonomous sailboat navigation for short course racing. Robotics and Autonomous Systems 56, 604–614 (2008)

Article   Google Scholar  

Stelzer, R., Pröll, T., John, R.I.: Fuzzy Logic Control System for Autonomous Sailboats. In: FUZZ-IEEE 2007, pp. 97–102 (2007)

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Stelzer, R., Jafarmadar, K.: A Layered System Architecture to Control an Autonomous Sailboat. In: Proceedings of TAROS 2007, Aberystwyth, UK (2007)

Alvira, M., Barton, T.: Small and Inexpensive Single-Board Computer for Autonomous Sailboat Control, Robotic Sailing 2012, pp. 105–116. Springer (2013)

Koch, M., Petersen, W.: Using ARM7 and uC/OS-II to Control an Autonomous Sailboat Robotic Sailing 2011, pp. 101–112. Springer (2012)

Neal, M., Sauze, C., Thomas, B., Alves, J.C.: Technologies for Autonomous Sailing: Wings and Wind Sensors. In: Proceedings of the 2nd IRSC, Matosinhos, Portugal, July 6-12, pp. 23–30 (2009)

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Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en, Ingeniería (IUSIANI), Las Palmas de Gran Canaria, Spain

J. Cabrera-Gámez, A. Ramos de Miguel, A. C. Domínguez-Brito & J. D. Hernández-Sosa

Dept. Informática y Sistemas, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

J. Cabrera-Gámez, A. C. Domínguez-Brito, J. D. Hernández-Sosa, J. Isern-González & E. Fernández-Perdomo

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Cabrera-Gámez, J., Ramos de Miguel, A., Domínguez-Brito, A.C., Hernández-Sosa, J.D., Isern-González, J., Fernández-Perdomo, E. (2014). An Embedded Low-Power Control System for Autonomous Sailboats. In: Bars, F., Jaulin, L. (eds) Robotic Sailing 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02276-5_6

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Novel robust-optimal controllers based on fuzzy descriptor system

by Shibaura Institute of Technology

Nonlinear systems have applications in many diverse fields from robotics to economics. Unlike linear systems, the output is not proportional to the input is such systems. A classic example is the motion of a pendulum. Due to the inherent nature of nonlinear systems, their mathematical modeling and, consequently, control is difficult.

In this context, the Takagi–Sugeno (T–S) fuzzy system emerges as a highly effective tool. This system leverages fuzzy logic to map input and output values to approximate a nonlinear system as multiple linear systems which are easier to model.

Fuzzy logic is a form of mathematical logic in which, instead of requiring all statements to be true (1) or false (0), the truth values can be any value between 0 and 1. T–S fuzzy system has thus served as the foundation for several nonlinear control methods, with the Parallel Distributed Compensator (PDC) method being the most prominent.

Furthermore, scientists have developed an enhanced version of this system, known as the fuzzy descriptor system (FDS). It combines the T–S fuzzy system with the powerful space–state representation, which describes a physical system in terms of state variables, input variables, and output variables.

Despite extensive research, optimal control strategies in the context of T–S FDSs are still largely unexplored. Additionally, while robust control methods, which protect against disturbances, have been explored for T–S FDS using methods like Linear Matrix Inequalities (LMI), these methods introduce additional complexity and optimization challenges.

To overcome these limitations, a group of researchers, led by Associate Professor Ngoc-Tam Bui from the Innovative Global Program of the College of Engineering at Shibaura Institute of Technology in Japan and including Thi-Van-Anh Nguyen, Quy-Thinh Dao, and Duc-Binh Pham, all from Hanoi University of Science and Technology, developed novel optimal and robust-optimal controllers based on the T–S fuzzy descriptor model. Their study was published in the journal Scientific Reports .

To develop the controllers, the team first utilized the powerful Lyapunov stability theory to establish the stability conditions for the mathematical model of the FDS. However, these stability conditions cannot be directly used. As Dr. Bui explains, "The stability conditions for the FDS model make it difficult to solve using established mathematical tools. To make them more amenable, we systematically transformed them into LMI."

These modified conditions formed the basis for developing three controllers: the stability controller which uses PDC to manage deviations, the optimal controller which minimizes a cost function to obtain optimal control, and the robust-optimal controller which combines the benefits of both of them.

The researchers demonstrated the effectiveness of these controllers in controlling a rotary inverted pendulum, a challenging system comprising an inverted pendulum sitting on a rotating base. The problem is to keep the pendulum upright by controlling the rotation of the base.

The researchers tested the performance of the controllers using distinct simulation scenarios. Simulations revealed that the stability controller effectively stabilized the system when the initial displacement angle was small, while with larger initial angles, there were more oscillations, and the settling time was higher.

The high settling time was effectively addressed by the optimal controller, reducing it from 13 to 2 seconds, representing a six-fold reduction. Moreover, it also reduced the maximum amplitudes during oscillations.

The robust-optimal controller was tested using two different scenarios. In the first case, the mass of the pendulum bar was changed, while in the second, white noise was introduced into the control input. Compared to the optimal controller, it performed the same in the first scenario. However, the controller was considerably better in the second scenario, showing no oscillations while the optimal controller showed clear oscillations. Notably, the robust-optimal controller showed the lowest error values.

These results highlight the adaptability and potential of these controllers in practical scenarios. "The research findings hold promising implications for various real-life applications where stable control in dynamic and uncertain environments is paramount. Specifically, autonomous vehicles and industrial robots can achieve enhanced performance and adaptability using the proposed controllers," remarks Dr. Bui.

"Overall, our research opens avenues for advancing control strategies in various domains, ultimately contributing to more capable autonomous systems, making transportation safer, health care more effective, and manufacturing more efficient."

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