Haptic Feedback Control of a Smart Wheelchair

Hadj-Abdelkader, Amine; Bourhis, Guy; Cherki, Brahim

doi:10.1155/2012/921982

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…Various control systems have been developed to achieve efficient and stable control of robotic systems. These systems have found applications in many fields, including Micro-Robotics [1], Microgrid [2], Mini Drone [3], Welding robot [4], [5], smart wheelchair [6], [7], [8], [9], [10], [11], Balance robot [12], [13], [14], [15], [16], [17], and omnidirectional wheels [18], [19], [20], [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

Chotikunnan

Pititheeraphab

2023

Journal of Robotics and Control

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This study aims to develop an expert system implementation of P controller and fuzzy logic controller to address issues related to improper control input estimation, which can arise from incorrect gain values or unsuitable rule-based designs. The research focuses on improving the control input adaptation by using an expert system to resolve the adjustment issues of the P controller and fuzzy logic controller. The methodology involves designing an expert system that captures error signals within the system and adjusts the gain to enhance the control input estimation from the main controller. In this study, the P controller and fuzzy logic controller were regulated, and the system was tested using step input signals with small values and larger than the saturation limit defined in the design. The PID controller used CHR tuning to least overshoot, determining the system's gain. The tests were conducted using different step input values and saturation limits, providing a comprehensive analysis of the controller's performance. The results demonstrated that the adaptive fuzzy logic controller performed well in terms of %OS and settling time values in system control, followed by the fuzzy logic controller, adaptive P controller, and P controller. The adaptive P controller showed similar control capabilities during input saturation, as long as it did not exceed 100% of the designed rule base. The study emphasizes the importance of incorporating expert systems into control input estimation in the main controller to enhance the system efficiency compared to the original system, and further improvements can be achieved if the main processing system already possesses adequate control ability. This research contributes to the development of more intelligent control systems by integrating expert systems with P controllers and fuzzy logic controllers, addressing the limitations of traditional control systems and improving their overall performance.

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Section: Introductionmentioning

confidence: 99%

Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

Chotikunnan

Pititheeraphab

2023

Journal of Robotics and Control

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“…Novel approaches use facial expression, hand gesture, and eye detection for movement control [34]- [37]. Physiological signals like EOG [38], [39], EEG [41], [42], and haptic feedback [43] have been employed. Mecanum wheels offer a solution to mobility challenges, investigated in wheelchair designs [44], [45] and dynamic modeling [46]- [48].…”

Section: Introductionmentioning

confidence: 99%

Application of PID Control System in Mecanum Wheelchair

Chotikunnan,

Thongpance

et al. 2023

ijmst

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This research centers on the design and implementation of a control system for an electric wheelchair equipped with Mecanum wheels. The study details a comprehensive research methodology starting with the creation of a block diagram to guide system design, hardware selection, and overall implementation. The electric wheelchair system incorporates power resources, input devices, and energy output mechanisms, utilizing a 24 VDC battery and a joystick with a 10K ohm potentiometer connected to an Arduino Due microcontroller. The operational workflow of the system is defined, enabling the wheelchair to respond to joystick commands for forward, left turn, right turn, and other movements. A PID control system is employed to regulate motor movement, enhancing control precision. The Cohen-Coon tuning method is used to determine the PID controller's gain, ensuring efficient closed-loop control. Results from PID controller experiments under P control and PD control are presented, demonstrating the system's responses for different gain values. Optimal performance is observed with a Kp value of 80 and Kd value of 1.2, showcasing improved response speed, reduced rise time, enhanced setting time, and lower percent overshoot. In conclusion, the combined proportional and derivative control system, specifically with Kp = 80 and Kd = 1.2, proves to be effective in enhancing the Mecanum wheelchair's performance. This study provides valuable insights into precise parameter adjustments for optimal control in Mecanum wheelchair applications.

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“…Thus in ref. [19], a haptic information depending on the distance to the obstacles around the chair is sent back to the pilot using a force feedback joystick. Two types of algorithms are proposed: "passive" force feedback (the joystick resists movement towards an obstacle) and "active" force feedback (the joystick moves the wheelchair away from obstacles).…”

Section: Introductionmentioning

confidence: 99%

Traded and combined cooperative control of a smart wheelchair

et al. 2022

Self Cite

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This article deals with a human–machine cooperative system for the control of a smart wheelchair for people with motor disabilities. The choice of a traded control mode is first argued. The paper then pursues two objectives. The first is to describe the design of the cooperative system by focusing on the dialogue and the interaction between the pilot and the robot. The second objective is to introduce a new cooperative mode. In this one, three features are proposed: two semi-autonomous features, a wall following and a doorway crossing, during which the user can intervene punctually to rectify a trajectory or a path, and an assisted mode where, conversely, the machine intervenes in a manual control to avoid obstacles. This mode of intervention of an entity, human or machine, supervising a movement controlled by the other is referred as “combined control.” Examples of scenarios exploiting the cooperative capabilities of the system are presented and discussed.

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Haptic Feedback Control of a Smart Wheelchair

Cited by 14 publications

References 12 publications

Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

Application of PID Control System in Mecanum Wheelchair

Traded and combined cooperative control of a smart wheelchair

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