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All Journal Journal of Robotics and Control (JRC)
Kadhim, Saleem Khalefa
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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A Comparative Study Between Convolution and Optimal Backstepping Controller for Single Arm Pneumatic Artificial Muscles Ahmed, Amna Suri; Kadhim, Saleem Khalefa
Journal of Robotics and Control (JRC) Vol 3, No 6 (2022): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i6.16064

Abstract

This study was based on the dynamic modeling and parameter characterization of the one-link robot arm driven by pneumatic artificial muscles. This work discusses an up-to-date control design based on the notion of a conventional and optimal backstepping controller for regulating a one-link robot arm with conflicting biceps and triceps positions supplied by pneumatic artificial muscles. The main problems found in systems that utilize pneumatic artificial muscle as actuators are primarily the large uncertainties, non-linearities, and time-varying features that severely impede movement performance in tracking control. In consideration of the uncertainty, high nonlinearity, and external disturbances that can exist during the motion. Lyapunov-based backstepping control technique was utilized to assure the stability of the system with improved dynamic performance. The bat algorithm optimization method is utilized in order to modify the variables used in the design of the controller to enhance the efficiency of the suggested controller. According to the conclusions, a quantitative comparison of the response in the PAM actuated the arm model in the current study and earlier investigations with the Backstepping controlled system revealed fair agreement with a variation of 37.5% from the optimal classical synergetic controller. In addition, computer simulations were utilized in order to compare the effectiveness of the proposed conventional controls and the optimal background. It has been proven that an optimal controller can control the uncertainties and maintain the controlled system’s stability.
Dynamic Motion Control of Two-Link Robots with Adaptive Synergetic Algorithms Abbas, Aya Khudhair; Kadhim, Saleem Khalefa
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22985

Abstract

Robotics is advancing to assist with daily tasks by developing human-like robotic limbs, which involves challenges in integrating software, control systems, electronics, and mechanical designs. To address these challenges, Classic Synergetic Controller (CSC) and Adaptive Synergetic Controller (ASC) algorithms were created using mathematical equations to regulate the robot arm's joint angle position and achieve precise tracking. A comparison with Adaptive Sliding Mode Control (ASMC) and Classical Sliding Mode Control (CSMC) demonstrated that CSC and ASC outperform in efficiency and robustness. ASC improved by 63%, providing smoother angular position tracking and faster response times. CSC reached the desired position angle in 1.5 seconds with oscillations, while ASC achieved it in 2.4 seconds without oscillations and eliminated chattering. CSC's Root Mean Square (RMS) was 1.57 rad, whereas ASC had no RMS value. The improvement rate of ASC over CSC was 100%, ensuring seamless motion, better rise time, and eliminating oscillations, thus providing robust control against disturbances and parameter variations.
Dynamic Motion Control of Two-Link Robots with Adaptive Synergetic Algorithms Abbas, Aya Khudhair; Kadhim, Saleem Khalefa
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22985

Abstract

Robotics is advancing to assist with daily tasks by developing human-like robotic limbs, which involves challenges in integrating software, control systems, electronics, and mechanical designs. To address these challenges, Classic Synergetic Controller (CSC) and Adaptive Synergetic Controller (ASC) algorithms were created using mathematical equations to regulate the robot arm's joint angle position and achieve precise tracking. A comparison with Adaptive Sliding Mode Control (ASMC) and Classical Sliding Mode Control (CSMC) demonstrated that CSC and ASC outperform in efficiency and robustness. ASC improved by 63%, providing smoother angular position tracking and faster response times. CSC reached the desired position angle in 1.5 seconds with oscillations, while ASC achieved it in 2.4 seconds without oscillations and eliminated chattering. CSC's Root Mean Square (RMS) was 1.57 rad, whereas ASC had no RMS value. The improvement rate of ASC over CSC was 100%, ensuring seamless motion, better rise time, and eliminating oscillations, thus providing robust control against disturbances and parameter variations.
Co-Authors Abbas, Aya Khudhair Ahmed, Amna Suri