System identification and control of nonlinear systems can be efficiently carried out using intelligent control schemes. In this framework, our research work primarily focus on direct, indirect and model predictive control schemes using T-S fuzzy model, neural and fuzzy-neural networks. These control algorithms are being tested in real-time systems such as 7DOF Power-cube robot manipulator and inverted pendulum.
Visual-motor coordination, also referred to as hand-eye coordination, in the context of robotics is the process of using visual information to control a robot manipulator to reach a target point in its workspace. The task requires learning the mapping that exists between camera output and desired end-effector location. Biological organisms have demonstrated their superior adaptive capabilities in motion control over present-day robotic systems. Inspired by this fact, various neural network models based on biological systems have been developed for robot control tasks. We have been working on various self-organizing schemes such as Kohonen SOM, Quantum clustering SOM and parametrized SOM to learn this map using minimum learning example. Simultaneously we are extending these approaches to the redundant manipulator system which involves optimization of multiple objective functions.
The work is in progress for visual tracking where various algorithms such as Kalman filter, Unscented Kalman filter, and quantum neural network based algorithms are being investigated for efficient tracking of the moving target by a mobile robot. The experiments are being conducted on Patrolbot.
Research in this area focusses on efficient training schemes that would provide fast convergence, better generalization and least computational effort.
Computing power has grown at an astounding pace since the synthesis of first transistor in 1947. Gordan Moore in 1965 stated that computing power will double for constant cost every two years, and this statement, which has stood the test of time, has come to be known as Moore's Law. At this exponential speed, it was conjectured that it will not be long before we have extremely small sized computing devices. At such small sizes, laws of classical mechanics are no longer valid. Thus came the idea of Quantum computation in his celebrated paper, but it was not before discovery of algorithm for polynomial time factorization by Shor, that the general interest in Quantum computation started increasing.
Thus topics related to "Quantum Computation" and "Quantum Information Processing" are at the forefront of research interest due to rapid theoritical and experimental advances. Apart from the computational power derived from quantum computer, the field of quantum learning systems is poised to provide a potent framework to study subjective aspect of matter. The challenge to bridge the gap between physical and mental (or objective and subjective or mind and body) notions of matter can be countered within the quantum learning systems. In this framework, notion of "quantum brain" and concepts of "recurrent quantum neural networks(RQNN)" have already been coined by researchers.
First step to develop quantum learning system is to build quantum neural network models for applications for which human brain is very capable of. Some such applications are pattern recognition and language understanding. Quantum dynamics of such learning system can be based using Schroedinger wave equation, Feynman path integral, density matrix approach and linear operator theory in Hilbert space, Thus the field of quantum learning system is pregnant with many novel ideas and possibilities.