Research Work
Advanced Resilient and Intelligent Control for Heterogeneous Cyber-Physical Mobile Robot Systems: In order to enhance robotic systems with the ability to adapt to, deal with, and recover from various system, environment, and communication malfunctions, the ultimate goal of this project is to develop and design advanced resilient and intelligent control frameworks and algorithms for multiple mobile robots under the cyber-physical system. The heterogeneity of grounded mobile robots, aerial mobile robots, mobile manipulators, and human-robot interaction will all be considered and studied in this project. By the end of this project, it is anticipated that the emerging technology will be highly developed for the control and robotic society, and the outcomes of this project will become a significant foundation worldwide in these research topics, and more importantly, this project is expected to pilot and lead the relevant societies to enter the new world of cyber-physical resilient and intelligent mobile robot control systems. This work resulted in the form of publication of well-reputed impact factor journal and conference publication.
Stabilization of Nonholonomic Systems: This work was mainly focused on the design of feedback control laws for the stabilization of nonholonomic systems with different structures. Created a working model for the extended nonholonomic double integrator, a rigid body, a unicycle, a front-wheel car, a car with a trailer, and a firetruck model. For this purpose, the methodologies adopted are based on adaptive backstepping, adaptive integral sliding mode control, and smooth super twisting sling mode control technique. The control laws are formulated using Lyapunov stability analysis. In all cases, the control laws design for the transformed models is derived first, which is then used to achieve the overall control design of the kinematic model of particular nonholonomic systems. This work resulted in the publication of four well-reputed impact factor journals and three conference publications.
Face Recognition Using Principal Component Analysis: One of my Master’s degree Projects is in Face Recognition Using Principal Component Analysis. As the real environment data is quite large it needs a significant amount of computational time and complexity factor. So, a method PCA is used to compress the information and retain as much information as possible. For dimension reduction of large data sets, PCA uses a mathematical vector space transform projection of the original data set, which may have involved significant variables, and can often be interpreted in just a few variables called (the principal components). The detection accuracy is more than 90 % for the daylight image sequence.
Robust Cancellation of EEG from the Surface of ECG: During my Master’s, I also worked on Signal Processing Algorithms such as LMS, RLS, RLMS, and RRLS to modify the Iterative Version of Adaptive Kalman and Recursive Least Square (RLS) Adaptive Filtering algorithms to cancel out the effect of EEG from the surface of ECG signal. I have also worked on developing A Robust Adaptive Kalman Equalizer for Time-Varying Channel. The work is published in the International Conference on Engineering & Emerging Technologies Lahore, Pakistan, March 20-21, 2014.
Signal Modeling Using Transmultiplexer Structure: During my Master’s research, I also worked on signal modelling techniques by using different Transmultiplexer structures, and the emphasis is mostly on the general infrastructure.
Spectrum Sensing in Cognitive Radio Networks: In the 4th year of my bachelor's degree, I worked on spectrum sensing in cognitive radio networks. During this project, I was working in a professional environment, and cooperating with various managers and engineers to create a design framework based on spectrum sensing that met the requirements of the problem.