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All Journal Journal of Robotics and Control (JRC) Involvement International Journal of Business
Qasim, Mohammed
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mechanical Engineering

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Design of a Robust Component-wise Sliding Mode Controller for a Two-Link Manipulator Qasim, Mohammed; Abdulla, Abdulla Ibrahim; Ayoub, Abdurahman Basil
Journal of Robotics and Control (JRC) Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i2.25632

Abstract

Compared to conventional Multiple-Input Multiple-Output (MIMO) Sliding Mode Control (SMC) techniques, the component-wise SMC approach offers several advantages, including improved decoupling of system dynamics, enhanced robustness, and greater flexibility in controller design. This paper proposes a novel trajectory tracking controller for a two-link manipulator based on the component-wise sliding mode control approach. The design methodology involves determining controller gains by solving a set of inequalities. This analysis results in conditions on the system parameter uncertainties that guarantee the existence of a feasible solution to the set of inequalities. Furthermore, an algorithm is presented to determine the maximum allowable uncertainties that ensure the feasibility of the controller gains. To evaluate the performance and robustness of the proposed tracking controller, the manipulator is subjected to a series of challenging trajectories, including circular and figure-8 ones, under both nominal and maximum allowable uncertainty conditions. The proposed controller demonstrates superior performance across both circular and figure-8 trajectories, exhibiting excellent transient response and minimal steady-state error even under the maximum permissible uncertainties, which extend up to 27% in link masses. This performance is validated through a quantitative analysis that incorporates a comparative evaluation against two conventional MIMO SMC techniques. The comparison is conducted using the Integral Norm of Error (INE) to assess tracking accuracy and the Integral Norm of Control Action (INU) to evaluate the energy efficiency of the controllers. These metrics provide a comprehensive basis for analyzing both the precision and the energy consumption of the proposed control strategy in relation to established methods.
Reconceptualizing Evidence-Based Business Decisions in the Era of Data Analytics and Artificial Intelligence Mastan, Danish; Qasim, Mohammed; Ali, Mohammed Aijaz; Zafar, Ayyan
Involvement International Journal of Business Vol. 3 No. 1 (2026): January 2026
Publisher : PT Agung Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62569/iijb.v3i1.166

Abstract

Business organizations are increasingly required to move beyond intuition-based decision-making toward evidence-based approaches supported by data analytics. Advances in data analytics, machine learning, and predictive modeling have reshaped how firms interpret information, forecast trends, and respond to market dynamics. However, despite growing adoption, challenges related to data quality, organizational readiness, and ethical governance continue to limit the effective use of analytics in strategic decision-making. This study adopts a qualitative-descriptive approach using secondary data analysis and real-world case illustrations across multiple industries, including retail, healthcare, finance, logistics, and technology. Drawing on the Data-Driven Decision-Making (DDDM) framework and business intelligence theory, the paper synthesizes insights from academic literature, industry reports, and documented organizational practices to examine how data analytics supports evidence-based decisions and operational efficiency. The findings demonstrate that data analytics significantly enhances decision accuracy, operational efficiency, and strategic agility. Predictive analytics and machine learning enable organizations to anticipate market trends, personalize customer engagement, reduce operational risks, and optimize resource allocation. Empirical illustrations indicate notable improvements in efficiency, risk reduction, revenue growth, and customer satisfaction when analytics-driven approaches are systematically implemented. However, data quality issues, talent shortages, resistance to change, and data privacy concerns remain critical barriers. The study highlights that the transformative value of data analytics lies not only in technological adoption but also in cultivating a data-driven organizational culture supported by leadership commitment and ethical governance.
Co-Authors Abdulla, Abdulla Ibrahim Ali, Mohammed Aijaz Ayoub, Abdurahman Basil Mastan, Danish Zafar, Ayyan