Query based model learning and stable tracking of a robot arm using radial basis function network
In the context of a robot manipulator, a generalized neural emulator over the complete workspace is very difficult to obtain because of dimensionally insufficient training data. A query based learning algorithm is proposed in this paper that can generate new examples where control inputs are independent of states of the system. This algorithm is centered around the concept of network inversion using an extended Kalman filtering based algorithm. This is a novel idea since robot manipulator is an open loop unstable system and generation of control input independent of state is a research issue for neural model identification. Two trajectory independent stable control schemes have been designed using the neural emulator. One of the control schemes uses forward-inverse-modeling approach to update the controller parameters adaptively following Lyapunov function synthesis technique. The proposed scheme is trajectory independent unlike the back-propagation scheme. The second type of controller predicts the minimum variance estimate of control action using recall process (network inversion) and the control law is derived following a Lyapunov function synthesis approach so that the closed loop system consisting of controller and neural emulator remains stable. The simulation experiments show that the model validation approach is efficient and the proposed control schemes guarantee stable accurate tracking.
query based learning network inversion extended kalman filtering adaptive control