To repeat what others have said, requires education.

To challenge it, requires brains.

· Research Fellow, Ministry of Human Resource and Development (MHRD) Fellowship, Full Time, Department of Electronics and Computer Engineering, January 2005 to December 2008, IIT Roorkee, India.

Title: Intelligent control of Robot manipulators using Soft computing techniques.

Research Interests:

Systems and Control, Robot control, Quantum control, Computational intelligence, and Bacterial swarm optimization.

Research Contributions:

•Using the ability of ANFIS (Adaptive Neuro-Fuzzy Inference System) to learn from training data, it is possible to create ANFIS; an implementation of a representative fuzzy inference system using a BP neural network-like structure, with limited mathematical representation to provide fast and acceptable solutions of the inverse kinematics problem.

•Hybrid Fuzzy PD plus conventional I controller is developed for the control of a six degrees of freedom robot arm (PUMA Robot). Complexity of the proposed controller is minimized as possible and only two design variables are used to adjust the rate of variations of the proportional gain and derivative gain.

• Some new hybrid Adaptive Neuro Fuzzy control algorithms (ANFIS) have been proposed for manipulator control with uncertainties.

•A novel algorithm based on the foraging behavior of E-coli bacteria and particle swarm optimization to optimize Precompensated Fuzzy PD with proportional velocity feedback control of two link flexible manipulator is carried out. The proposed algorithm performs local search through the chemotactic movement operation of BFO whereas the global search over the entire search space is accomplished by a PSO operator. In this way it balances between exploration and exploitation enjoying best of both the techniques.

•The proposed hybrid fuzzy logic based precompensation scheme consisting of fuzzy PD precompensator and a conventional PD Controller has superior steady state and transient performance, good stabilization and tracking performance compared to a conventional PD controller.

•Another hybrid novel optimization algorithm, Genetically Bacterial Swarm Optimization (GBSO) combines the best features of GA based selection, crossover, and mutation and PSO based convergence of search direction vector with bacterial chemotaxis and shows a better convergence rate than the other algorithms reaching to the optimal solution .

• Project Trainee, Honeywell India Software Operation Pvt. Ltd., Bangalore, July 01 to Jan 02.