Simplified Sliding Mode Control of a Single-Link Planar Robot

Robotic technologies are becoming more advanced and are becoming more popular. Requirements are high levels of accuracy and operational speed, such as in surgery and manufacturing. These targets were highly challenging and could be enhanced by employing suitable control systems. Nonlinear control schemes were characterised by high accuracy and robustness, but by high computational time. Hence, the study simplified the Sliding Mode Control (SMC) scheme for a single link planar robot and was referred to as the Simplified Sliding Mode Control (RSMC), which was compared with the normal version referred to as Normal Sliding Mode Control (NSMC) and the gain scheduling based Proportional Derivative Control (GPDC). The effect considered was friction, and it was limited to simulation studies conducted using the SIMULINK/MATLAB software. Results showed that the proposed controller, RSMC, has an algorithm length that is at least 50% shorter than that of the NSMC, with a simplicity similar to that of the GPDC. Results also indicated achieving a settling time of 1.5s, a rise time of 0.8s, 0% overshoot, and a cumulative error of about 250, maintaining the same results with disturbance. The system with the NSMC showed the corresponding parameters as 2.0s, 1.8s, 0% and 260, respectively, without disturbance, and as 3s, 3s, 0% and 310, respectively, with disturbance. It therefore implied that a shorter RSMC algorithm means lower execution time. The RSMC showed superior performance, indicating higher accuracy, operational speed and robustness.

Authors

Publication Details

Journal
Emerging technologies and engineering journal.
Published
2026-04-30
DOI
https://doi.org/10.53898/etej2026314
Primary Topic
Adaptive Control of Nonlinear Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Simplified Sliding Mode Control of a Single-Link Planar Robot

Salihu Abdulmumini Jalo, Muhammad Usman Ilyasu, Abdulqadiri Bello Abdulqadiri, Mohammed Ahmed et al.
Emerging technologies and engineering journal.
Adaptive Control of Nonlinear Systems
article

Simplified Sliding Mode Control of a Single-Link Planar Robot

Salihu Abdulmumini Jalo, Muhammad Usman Ilyasu, Abdulqadiri Bello Abdulqadiri, Mohammed Ahmed, Babul Salam Kader Ibrahim, Balkisu Umar Bapetel
article en

Abstract

Robotic technologies are becoming more advanced and are becoming more popular. Requirements are high levels of accuracy and operational speed, such as in surgery and manufacturing. These targets were highly challenging and could be enhanced by employing suitable control systems. Nonlinear control schemes were characterised by high accuracy and robustness, but by high computational time. Hence, the study simplified the Sliding Mode Control (SMC) scheme for a single link planar robot and was referred to as the Simplified Sliding Mode Control (RSMC), which was compared with the normal version referred to as Normal Sliding Mode Control (NSMC) and the gain scheduling based Proportional Derivative Control (GPDC). The effect considered was friction, and it was limited to simulation studies conducted using the SIMULINK/MATLAB software. Results showed that the proposed controller, RSMC, has an algorithm length that is at least 50% shorter than that of the NSMC, with a simplicity similar to that of the GPDC. Results also indicated achieving a settling time of 1.5s, a rise time of 0.8s, 0% overshoot, and a cumulative error of about 250, maintaining the same results with disturbance. The system with the NSMC showed the corresponding parameters as 2.0s, 1.8s, 0% and 260, respectively, without disturbance, and as 3s, 3s, 0% and 310, respectively, with disturbance. It therefore implied that a shorter RSMC algorithm means lower execution time. The RSMC showed superior performance, indicating higher accuracy, operational speed and robustness.

Emerging technologies and engineering journal.Vol. 3(1)
Openalex Percentile: Top 9%
Adaptive Control of Nonlinear Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.