Sikender Gul
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Sikender Gul has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Adeel Mehmood
School of Digital and Physical Sciences, Faculty of Science and Engineering, University of Hull, Hull HU6 7RX, UK
1 shared publication - Khurram Ali
Department of Computer Engineering, COMSATS University Islamabad, Islamabad 45550, Pakistan
1 shared publication - Khurram Shehzad
Department of Electrical Engineering, COMSATS University Islamabad, Islamabad 45550, Pakistan
1 shared publication - Syed Ali Ajwad
Capgemini Engineering, 92130 Issy Les Moulineaux, France
1 shared publication
Latest Publications
- Open Access
Actuator Fault-Tolerant Control of Anthropomorphic Manipulator Using Adaptive Backstepping and Neural Estimation of LuGre Friction Torque
This paper presents a fault-tolerant control (FTC) strategy for a six-degree-of-freedom (DoF) anthropomorphic manipulator operating under actuator faults and complex friction dynamics. The proposed framework integrates a backstepping control methodology with LuGre friction modeling and a feedforward neural network (FFNN) for friction estimation. Actuator faults are considered in the form of multiplicative efficiency losses and additive disturbances. An adaptive control law is developed to estimate and compensate for both friction and actuator faults in real time. The stability of the closed-loop system is guaranteed through Lyapunov theory. The simulation results validate the effectiveness and robustness of the proposed approach in ensuring precise trajectory tracking despite faults and friction uncertainties.
Open AccessActuator Fault-Tolerant Control of Anthropomorphic Manipulator Using Adaptive Backstepping and Neural Estimation of LuGre Friction Torque
This paper presents a fault-tolerant control (FTC) strategy for a six-degree-of-freedom (DoF) anthropomorphic manipulator operating under actuator faults and complex friction dynamics. The proposed framework integrates a backstepping control methodology with LuGre friction modeling and a feedforward neural network (FFNN) for friction estimation. Actuator faults are considered in the form of multiplicative efficiency losses and additive disturbances. An adaptive control law is developed to estimate and compensate for both friction and actuator faults in real time. The stability of the closed-loop system is guaranteed through Lyapunov theory. The simulation results validate the effectiveness and robustness of the proposed approach in ensuring precise trajectory tracking despite faults and friction uncertainties.