Kumeresan A. Danapalasingam

Associate Professor

Control & Mechatronics Engineering

About Me

Kumeresan was born in Kuala Lumpur in 1981. He obtained his primary and secondary educations in Gombak, Selangor. After earning his degree in Bachelor of Engineering (Electrical – Mechatronics) from Universiti Teknologi Malaysia (UTM), he was appointed as a tutor in the Department of Control & Mechatronics Engineering, Faculty of Electrical Engineering, UTM in 2003. In 2005, he was promoted to lecturer after completing his studies in Master of Engineering (Electrical – Mechatronics & Automatic Control) at UTM.

He was then awarded a scholarship by the Ministry of Higher Education, Malaysia to pursue his Ph.D. at Aalborg University, Denmark. During the doctoral program, he had the opportunity to conducting research activities at Georgia Institute of Technology, United States. Upon the completion of Ph.D. in Electrical & Electronic Engineering, he was appointed as a senior lecturer in 2012.

Since 2014, he has been a Corporate Member of the Institution of Engineers, Malaysia and has been a Professional Engineer with Practising Certificate as awarded by the Board of Engineers Malaysia since 2015. At present, he is one of the Key Opinion Leaders representing Malaysia in the ASEAN High Performance Computing Taskforce  and the ASEAN Hydroinformatics Data Centre Taskforce. He is also a member of the Technical Committee on Smart Manufacturing, Department of Standards Malaysia.


The SKEM4143 Robotics course is offered to 4th year students of the Bachelor of Engineering (Electrical – Mechatronics) programme, in Semester 1 of the academic session. The MKEM1713 Artificial Intelligence course is usually taken by the Master of Engineering (Mechatronics & Automatic Control) students in Semester 2 of the academic session.

SKEM4143 Robotics

This course introduces students the basic principles underlying the design, analysis, and synthesis of robotic systems. Students are introduced to various classifications and types of industrial robots, methods of deriving and analyzing robot kinematics, robot dynamics, and the design of robot trajectory. Students are also introduced to various robot sensors and vision systems. By adapting the knowledge obtained, students will be able to effectively apply the forward kinematics, the inverse kinematics, the dynamics, and the robot trajectory planning to carry out tasks employing various industrial robots.

MKEM1713 Artificial Intelligence

Artificial intelligence (AI) involves the development of algorithms derived from biological intelligence that have capabilities such as learning, reasoning, generalization, adaptation, reproduction, etc. AI techniques have made their way into many domestic & industrial products & provided solutions to many difficult engineering problems. In this course, students are exposed to the theories and hands-on implementations of several AI techniques, i.e. Machine Learning, Artificial Neural Networks, Fuzzy Logic, Genetic Algorithm & Particle Swarm Optimization. Students will design AI solutions to engineering & non-engineering problems and will learn to implement using Python coding.


The simulation below shows the PUMA 560 robot arm performing a simple pick and place operation. A sliding mode controller is designed for each joint of the robot arm, and the trajectory of Joint 1 switching function, s is shown in the left figure. The trajectory consists of a number of reaching phases during which trajectories starting off the manifold s = 0 (black line) move towards it and reach it in finite time. Each reaching phase is followed  by a sliding phase during which the motion is confined to the manifold s = 0.

Student Supervision

The following are some of my previous and current research students. Those who are interested to pursue a Ph.D. or a Master (Research), please contact me.

Mohamad Faizrizwan bin Mohd Sabri

Doctor of Philosophy (Electrical Engineering)

Fuzzy Logic-Based Energy Management Strategy for Through-the-Road Hybrid Electric Vehicle


Nor Mohd Haziq Norsahperi

Doctor of Philosophy (Electrical Engineering)

Optimal Integral Sliding Mode Control for Uncertain Robotic Manipulators


Labib Sharrar

Master of Philosophy (Electrical Engineering)

Intelligent Anomaly Detection System for Industrial Motors


Mohd Azali bin Zainal Abidin

Master of Engineering (Mechatronics & Auto. Control)

Fuzzy Logic in Battery Energy Storage System


Omar Mohammed Shafiq Marei

Master of Engineering (Mechatronics & Auto. Control)

Computer Vision System For Industrial Screwing Automation


Nabil Abduljalil Abdo Mohamed

Master of Engineering (Mechatronics & Auto. Control)

Control & Monitoring of Battery Energy Storage System using PLC



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ASEAN High Performance Computing Taskforce

The ASEAN High Performance Computing (HPC) Taskforce is formed to develop the technical feasibility, and to discuss the methodology and work plan towards establishing the ASEAN HPC Facility. The ASEAN HPC Facility aims to be a key enabler for ASEAN science, technology and innovation collaboration through the use of HPC and high performance data analytics for the creation of research and development knowledge, and the development of human capital in the HPC space.

ASEAN Hydroinformatics Data Centre Taskforce

The ASEAN Hydroinformatics Data Centre is proposed by the Hydro and Agro Informatics Institute, Thailand. It was endorsed by the Sub-Committee on Microelectronics and Information Technology (SCMIT) on May 22, 2017, and was approved by the ASEAN Committee on Science and Technology (COST) on May 24, 2017 (COST-72). The establishment of the ASEAN Hydroinformatics Data Centre allows ASEAN people to prepare and process enormous data into information that can be used to support and promptly respond to usual and crisis situations, consequently reducing water-related disasters towards sustainable developments.


Reach Me

Block P19A, School of Electrical Engineering,
Universiti Teknologi Malaysia,
81310 Johor Bahru, Johor Darul Takzim,