Robust Control of a Robot Arm Using an Optimized PID Controller

Bingöl, Mustafa Can; Akpolat, Zühtü Hakan; Koca, Gonca Özmen

doi:10.1007/978-3-319-65960-2_60

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…12, and the iron tube is on the right side of the center point of the lens. In the process of the iron tube tracking experiment, the PID algorithm (Bingol et al 2017) is used to control the steering gear to drive the lens movement to eliminate the deviation between the iron tube and the lens. The process of tracking the iron tube by the lens is like the process of automatically correcting the grab angle by the mechanical arm.…”

Section: Methodsmentioning

confidence: 99%

Automatic drill pipe emission control system based on machine vision

Zhang

Guan

2019

J Petrol Explor Prod Technol

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Drilling is not as modern as other industries and still has great potential for automatic control. Automatic drill pipe emission control system is mostly used for offshore drilling, and the derrick of the land drilling rig cannot be directly compatible with the mature drill pipe emission system of the offshore drilling rig because of its unique structure. Drill pipe emissions from land drilling are usually done by worker-assisted mechanical arm. In this kind of working environment, workers are labor intensive and prone to fatigue and safety cannot be effectively guaranteed. In order to reduce labor intensity and improve drilling efficiency, this paper presents an automatic control system of drill pipe emission based on machine vision. In the control process of the tripping operations, visual servo control is added, and the visual sensor is used to detect the centerline of the drill pipe in real time. After the system finding the deviation between the drill pipe and the mechanical arm, the posture of the arm is adjusted in real time to compensate for the deviation. When the claw mechanism is aligned with the centerline of the drill pipe, the drill pipe manipulator grabs the drill pipe to complete the emission task. In this paper, four methods for extracting the centerline are compared, and the linear regression method is the best. Keywords Machine vision • Mechanical arm • Drill pipe emission • Image processing List of symbols mm A unit of length in the metric system (millimeter) kN Common unit of mechanical calculation (1 kN = 1000 kg × 1 m/s 2) EX The abscissa of the intersection minus the abscissa of the center point of the lens (pixel) FPS Frame per second (Hz)

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Section: Methodsmentioning

confidence: 99%

Automatic drill pipe emission control system based on machine vision

Zhang

Guan

2019

J Petrol Explor Prod Technol

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“…A fuzzy logic controller is used in order to control the speed of the robot; also, the two speed reference signals were used in the above Equations (5) and (6). The FLC control model can be found in Figure 7.…”

Section: Fuzzy Logic Controller For the Lego Mindstorms Ev3mentioning

confidence: 99%

“…These controllers can be used to solve a problem when there is no knowledge of the process, when its objective is to obtain greater precision in the control of the system, and when related works in the literature can be observed [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of the Conventional Mathematical and Fuzzy Logic Controllers for Velocity Regulation

Valdez

Castillo

Caraveo

et al. 2019

Axioms

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Currently, we are in the digital era, where robotics, with the help of the Internet of Things (IoT), is exponentially advancing, and in the technology market we can find multiple devices for achieving these systems, such as the Raspberry Pi, Arduino, and so on. The use of these devices makes our work easier regarding processing information or controlling physical mechanisms, as some of these devices have microcontrollers or microprocessors. One of the main challenges in speed control applications is to make the decision to use a fuzzy logic control (FLC) system instead of a conventional controller system, such as a proportional integral (PI) or a proportional integral-derivative (PID). The main contribution of this paper is the design, integration, and comparative study of the use of these three types of controllers—FLC, PI, and PID—for the speed control of a robot built using the Lego Mindstorms EV3 kit. The root mean square error (RMSE) and the settling time were used as metrics to validate the performance of the speed control obtained with the controllers proposed in this paper.

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“…But the walking robot is a complex time-varying dynamic system and vulnerable to environmental interference, the traditional controller cannot meet its high-performance control requirements. Therefore, in order to further improve the control performance of robot system, many advanced control strategies and nonlinear controllers have been proposed and applied, such as predictive control [4], neural network control [5], fuzzy logic control [6], sliding mode control(SMC) [7] and robust control [8]. Among them, SMC has long been considered as an effective and simple special nonlinear control scheme, which can overcome the uncertainty of the system, and has strong robustness to nonsingular external disturbances and unmodeled dynamics of the system, especially for the nonlinear systems [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Super Twisting Sliding Mode Control With Fractional Order Sliding Surface for Trajectory Tracking of a Mobile Robot

Ren

Zhang

2021

Preprint

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This paper presents the position tracking performance of the robot system with uncertainties and external disturbances by using super twisting sliding mode control (STSMC) with fractional order (FO) sliding surface. In this scheme, fractional calculus theory is applied to the design of the sliding surface of STSMC, which can reduce the chattering caused by the switch control action and ensure that the control system has strong robust characteristics and fast convergence. Based on Lyapunov stability theory, the controller ensures the existence of sliding mode of sliding surface in finite time. Moreover, an adaptive STSMC reaching law is adopted. By using the fractional order nonlinear switching manifold and adaptive reaching law, the control performance can be obtained more effectively in sliding mode phase and the reaching phase, respectively. Finally, in order to validate the effectiveness and robustness of the proposed control strategy, the linear PID control strategy and the classical STSMC strategy are designed for comparative analysis, and the numerical calculation is carried out according to the dynamic model to study the position tracking accuracy of the robot under uncertainty and external interference.

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Robust Control of a Robot Arm Using an Optimized PID Controller

Cited by 7 publications

References 9 publications

Automatic drill pipe emission control system based on machine vision

Automatic drill pipe emission control system based on machine vision

Comparative Study of the Conventional Mathematical and Fuzzy Logic Controllers for Velocity Regulation

Adaptive Super Twisting Sliding Mode Control With Fractional Order Sliding Surface for Trajectory Tracking of a Mobile Robot

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