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In this study, it is discussed whether fast converged optimization methods should optimize online the covariance matrices of the Extended Kalman Filter (EKF) based algorithm used in estimating the stator current components, the rotor flux linkage components, the rotor speed and the load torque of Induction Motors(IM) for the sensorless vector control applications. If this scheme is possible, it will provide a solution approach for the sensitivity problem on parameter variations encountered while in estimation process; because the covariance matrices of system noises that are related with these variations will have been tuned online. At this point, speediness becomes restrictive feature on the choice of an appropriate optimization method because of the possible increase on computational load in each estimation sample. For this reason, the Big Bang - Big Crunch, to be the fastest heuristic optimization method, has been employed in determining the covariance matrices in the filter algorithm. Consequently, results from the simulation studies where BB-BC is compared with another heuristic optimization method, so called Simulated Annealing, distinguished that BB-BC is better with respect to convergence speed; however, in terms of reaching optimum solution while under parameter variations, none could perform promising estimation results. © 2011 Praise Worthy Prize S.r.l. - All rights reserved.

Network delays in the feedback and control loop can give rise to loss of transparency and instability in teleoperation and bilateral control systems, especially with the use of the interne, as the medium of communication. This is a current problem of bilateral control, with numerous approaches taken to improve the performance in the face of network delay This study is based on the inure recent approach of taking communication delay effects into account as disturbance, and addressing the problem via the design of a disturbance observer. To this aim, a novel sliding-mode observer (WO) and an EKF observer are developed and tested for improved performance in bilateral position control systems. The SMO runs on the master side and estimates the slave position using delayed measurement feedback from the slave side. The estimated feedback is used in a PD controller, also running on the master side, the output of which is sent to the slave through the interne, as control input for trajectory tracking dynamics. As another contribution to bilateral control, in this study, an extended Kalman Filter (EKF) is also developed to estimate the variable load changes on the slave side, which are further compensated by a "plus' term added on the slave side to the PD control input sent by the master. The designed Sill and EKF based bilateral control approach is tested with experiments conducted on a slave system comprising a single link arm under gravitational load. For added challenge, the system is tested for bidirectional load and reference trajectory variations and for both constant and random measurement and control input delays of 1-2 seconds in all cases. The experiment results demonstrate that the designed Sill and EKF based observers perform very well under no control input delay, and well- known slave parameters and initial conditions. The developed system performs reasonably well with bi-directional load and reference variations under parameter uncertainties, and definitely maintains stability even under simultaneous random measurement and control delays of 1-2 seconds, which is considerably more than the considered delays in current literature. Copyright (C) 2010 Praise Worthy Prize - All rights reserved.