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Sliding mode high-gain observers for a class of uncertain nonlinear systems
Veluvolu, K.C.,Lee, D. Pergamon Press ; Elsevier Science Ltd 2011 APPLIED MATHEMATICS LETTERS Vol.24 No.3
A robust high-gain observer for state and unknown input estimations for a special class of single-output nonlinear systems is developed in this article. Ensuring the observability of the unknown input with respect to the output, the disturbance can be estimated from the sliding surface. In the sliding mode, the convergence of the estimation error dynamics is proven similar to the analysis of high-gain observers.
Multistep Prediction of Physiological Tremor for Surgical Robotics Applications
Veluvolu, Kalyana C.,Tatinati, Sivanagaraja,Hong, Sun-Mog,Ang, Wei Tech IEEE 2013 IEEE Transactions on Biomedical Engineering Vol.60 No.11
<P>Accurate canceling of physiological tremor is extremely important in robotics-assisted surgical instruments/procedures. The performance of robotics-based hand-held surgical devices degrades in real time due to the presence of phase delay in sensors (hardware) and filtering (software) processes. Effective tremor compensation requires zero-phase lag in filtering process so that the filtered tremor signal can be used to regenerate an opposing motion in real time. Delay as small as 20 ms degrades the performance of human-machine interference. To overcome this phase delay, we employ multistep prediction in this paper. Combined with the existing tremor estimation methods, the procedure improves the overall accuracy by 60% for tremor estimation compared to single-step prediction methods in the presence of phase delay. Experimental results with developed methods for 1-DOF tremor estimation highlight the improvement.</P>
Gautam, A.,Veluvolu, K.C.,Soh, Y.C. Pergamon Press [etc.] 2017 Journal of the Franklin Institute Vol.354 No.9
<P>This paper presents an analysis of the tradeoff between repeated communications and computations for a fast distributed computation of global decision variables in a model-predictive-control (MPC)-based coordinated control scheme. We consider a coordinated predictive control problem involving uncertain and constrained subsystem dynamics and employ a formulation that presents it as a distributed optimization problem with sets of local and global decision variables where the global variables are allowed to be optimized over a longer time interval. Considering a modified form of the dual-averaging -based distributed optimization scheme, we explore convergence bounds under ideal and non-ideal wireless communications and determine the optimal choice of communication cycles between computation steps in order to speed up the convergence per unit time of the algorithm. We apply the algorithm for a class of dynamic-policy based stochastic coordinated control problems and illustrate the results with a simulation example. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.</P>
Evolutionary Algorithm Based Feature Optimization for Multi-Channel EEG Classification
Wang, Yubo,Veluvolu, Kalyana C. Frontiers Media S.A. 2017 Frontiers in neuroscience Vol.11 No.-
<P>The most BCI systems that rely on EEG signals employ Fourier based methods for time-frequency decomposition for feature extraction. The band-limited multiple Fourier linear combiner is well-suited for such band-limited signals due to its real-time applicability. Despite the improved performance of these techniques in two channel settings, its application in multiple-channel EEG is not straightforward and challenging. As more channels are available, a spatial filter will be required to eliminate the noise and preserve the required useful information. Moreover, multiple-channel EEG also adds the high dimensionality to the frequency feature space. Feature selection will be required to stabilize the performance of the classifier. In this paper, we develop a new method based on Evolutionary Algorithm (EA) to solve these two problems simultaneously. The real-valued EA encodes both the spatial filter estimates and the feature selection into its solution and optimizes it with respect to the classification error. Three Fourier based designs are tested in this paper. Our results show that the combination of Fourier based method with covariance matrix adaptation evolution strategy (CMA-ES) has the best overall performance.</P>
Tatinati, Sivanagaraja,Veluvolu, Kalyana C.,Wei Tech Ang IEEE 2015 IEEE transactions on cybernetics Vol.45 No.2
<P>For effective tremor compensation in robotics assisted hand-held device, accurate filtering of tremulous motion is necessary. The time-varying unknown phase delay that arises due to both software (filtering) and hardware (sensors) in these robotics instruments adversely affects the device performance. In this paper, moving window-based least squares support vector machines approach is formulated for multistep prediction of tremor to overcome the time-varying delay. This approach relies on the kernel-learning technique and does not require the knowledge of prediction horizon compared to the existing methods that require the delay to be known as a priori. The proposed method is evaluated through simulations and experiments with the tremor data recorded from surgeons and novice subjects. Comparison with the state-of-the-art techniques highlights the suitability and better performance of the proposed method.</P>