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Mehdi M.R.,Center for Advanced Studies in Engineering | Mahmood A.,Center for Advanced Studies in Engineering
2016 2nd International Conference on Robotics and Artificial Intelligence, ICRAI 2016 | Year: 2016

This paper describes a modeling and control of Zero Voltage Switching (ZVS) DC-DC converter. State space averaging technique is used to get the nominal model of the full bridge isolated DC-DC converter. Robust Control theory is then applied to modify this nominal model to standard format used for parametric uncertainties. H∞ control technique is implemented to regulate the output voltage in the presence of parametric uncertainties. System is then simulated in MATLAB Simulink as standard feedback system and results are compared with the LQR and H2 control. © 2016 IEEE.

Mughal A.M.,Center for Advanced Studies in Engineering | Iqbal K.,University of Arkansas at Little Rock
2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings | Year: 2016

Successful execution of biomechanical sit-to-stand (STS) task combines a forward thrust phase with an upward extension phase and stable movement termination. We have previously developed a fuzzy dynamic model to analyze the STS task by joining two local linear models, defined at the equilibrium positions, via Gaussian membership functions. The local linear models were obtained from a four-segment biomechanical representation of the human body dynamics in the sagittal plane. Our fuzzy controller model uses an observer to reconstruct velocity data from noisy observation of joint positions. In this study, we propose a reduced order observer with an optimal controller design for the STS task. The fuzzy optimal controller generates feedback and feedforward components of joint torques, whereby the latter are derived from a reference trajectory. Our movement control strategy employing fuzzy reduced order observer with fuzzy controller leads to physiologically tractable simulation of the STS movement with results that are superior to those previously obtained with full order compensators. © 2016 IEEE.

Mufti F.,Center for Advanced Studies in Engineering | Mahony R.,Australian National University | Heinzmann J.,Seeing Machines Ltd.
Robotics and Autonomous Systems | Year: 2012

A fundamental problem in autonomous vehicle navigation is the identification of obstacle free space in cluttered and unstructured environments. Features such as walls, people, furniture, doors and stairs, etc are potential hazards. The approach taken in this paper is motivated by the recent development on infra-red time-of-flight cameras that provide video frame rate low resolution depth maps. We propose to exploit the temporal information content provided by the high refresh rate of such cameras to overcome the limitations due to low spatial resolution and high depth uncertainty and aim to provide robust and accurate estimates of planar surfaces in the environment. These surfaces' estimates are then used to provide statistical tests to identify obstacles and dangers in the environment. Classical 3D spatial RANSAC is extended to 4D spatio-temporal RANSAC by developing spatio-temporal models of planar surfaces that incorporate a linear motion model as well as linear environment features. A 4D-vector product is used for hypotheses generation from data that is randomly sampled across both spatial and temporal variations. The algorithm is fully posed in the spatio-temporal representation and there is no need to correlate points or hypothesis between temporal images. The proposed algorithm is computationally fast and robust for estimation of planar surfaces in general and the ground plane in particular. There are potential applications in mobile robotics, autonomous vehicular navigation, and automotive safety systems. The claims of the paper are supported by experimental results obtained from real video data for a time-of-flight range sensor mounted on an automobile navigating in an undercover parking lot. © 2011 Elsevier B.V. All rights reserved.

Saleem M.,Center for Advanced Studies in Engineering | Di Caro G.A.,Instituto Dalle Molle Of Studi Sullintelligenza Artificiale Idsia | Farooq M.,National University of Computer and Emerging Sciences
Information Sciences | Year: 2011

Swarm intelligence is a relatively novel field. It addresses the study of the collective behaviors of systems made by many components that coordinate using decentralized controls and self-organization. A large part of the research in swarm intelligence has focused on the reverse engineering and the adaptation of collective behaviors observed in natural systems with the aim of designing effective algorithms for distributed optimization. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. These are key properties in the context of network routing, and in particular of routing in wireless sensor networks. Therefore, in the last decade, a number of routing protocols for wireless sensor networks have been developed according to the principles of swarm intelligence, and, in particular, taking inspiration from the foraging behaviors of ant and bee colonies. In this paper, we provide an extensive survey of these protocols. We discuss the general principles of swarm intelligence and of its application to routing. We also introduce a novel taxonomy for routing protocols in wireless sensor networks and use it to classify the surveyed protocols. We conclude the paper with a critical analysis of the status of the field, pointing out a number of fundamental issues related to the (mis) use of scientific methodology and evaluation procedures, and we identify some future research directions. © 2011 Elsevier Inc. All rights reserved.

Iqbal M.,Center for Advanced Studies in Engineering | Bhatti A.I.,Mohammad Ali Jinnah University | Ayubi S.I.,Mohammad Ali Jinnah University | Ayubi S.I.,University of Leicester | Khan Q.,Mohammad Ali Jinnah University
IEEE Transactions on Industrial Electronics | Year: 2011

This paper presents the design, simulation, and experimental results of a new scheme for the robust parameter estimation of uncertain nonlinear dynamic systems. The technique is established on the estimation of robust time derivatives using a variable-structure differentiator observer. A second-order sliding motion is established along designed sliding manifolds to estimate the time derivatives of flat outputs and inputs, leading to better tracking performance of estimates during transients. The parameter convergence and accuracy analysis is rigorously explored systematically for the proposed class of estimators. The proposed method is validated using two case studies; first, the parameters of an uncertain nonlinear system with known, but uncertain nominal parametric values are estimated to demonstrate the convergence, accuracy, and robustness of the scheme; in the second application, the experimental parameter estimation of an onboard-diagnosis-II-compliant automotive vehicle engine is presented. The estimated parameters of the automotive engine are used to tune the theoretical mean value engine model having inaccuracies due to modeling errors and approximation assumptions. The resulting dynamics of the tuned engine model matches exactly with experimental engine data, verifying the accuracy of the estimates. © 2011 IEEE.

Rasheed A.,Mohammad Ali Jinnah University | Ajmal S.,Center for Advanced Studies in Engineering | Qayyum A.,Mohammad Ali Jinnah University
The Scientific World Journal | Year: 2014

High relative node velocity and high active node density have presented challenges to existing routing approaches within highly scaled ad hoc wireless networks, such as Vehicular Ad hoc Networks (VANET). Efficient routing requires finding optimum route with minimum delay, updating it on availability of a better one, and repairing it on link breakages. Current routing protocols are generally focused on finding and maintaining an efficient route, with very less emphasis on route update. Adaptive route update usually becomes impractical for dense networks due to large routing overheads. This paper presents an adaptive route update approach which can provide solution for any baseline routing protocol. The proposed adaptation eliminates the classification of reactive and proactive by categorizing them as logical conditions to find and update the route. © 2014 Asim Rasheed et al.

Rizvi M.A.,Mohammad Ali Jinnah University | Bhatti Sr. A.I.,Mohammad Ali Jinnah University | Butt Q.R.,Center for Advanced Studies in Engineering
IEEE Transactions on Industrial Electronics | Year: 2011

This paper proposes a novel hybrid model for an internal combustion engine, with the power generated due to combustion as the input and the crankshaft speed fluctuations as the output. The individual cylinders of the engine are considered as subsystems for which a nonlinear model, based on the physical principles, is derived. The proposed model is linearized at an operating point, and a switched linear model is formed. The simulation results of the proposed model are validated by matching the results with the experimentally observed data. Using the properties of the validated model, it is shown that the crankshaft speed variations observed in the engine are a Markov process. A novel algorithm that is based on the Markov chain is proposed to detect the misfire in the spark ignition engines. In the ensuing engine rig experiments, an igniter misfire is introduced in the system and is successfully detected. The analysis of the data shows that the engine also has an air leakage in a cylinder (a developing misfire), which is experimentally confirmed later. © 2009 IEEE.

Rehan M.,Center for Advanced Studies in Engineering | Khan Z.H.,Center for Emerging science
2012 International Conference on Robotics and Artificial Intelligence, ICRAI 2012 | Year: 2012

This paper describes a robust formation control strategy for aerial refueling. Two types of control algorithms are designed and compared: first a conventional control system based on the Proportional Integral derivative (PID) controller is used for stability and control augmentation, and then a robust formation controller is designed to minimize the effect of disturbances and un-modeled dynamics. Both controllers are then compared for their performance under disturbance conditions. © 2012 IEEE.

Khan I.,Center for Advanced Studies in Engineering
IEEE Transactions on Multimedia | Year: 2014

Non-rigid structure-from-motion is one of the difficult and challenging problems in computer vision, especially when the only input available is 2D correspondences in monocular video sequence. This paper proposed a new constraint based framework for underconstrained non-rigid structure-from-motion problem to constrain the space of solution. The proposed method is based on a point trajectory approach with an additional uniqueness constraint applied to shape coefficients to reduce the basis required to construct the non-rigid 3D shape. A framework for occluded and incomplete measured data is also proposed using low rank matrix fitting which is a robust factorization scheme for the matrix completion problem. This method offers not only new theoretical insight, but also a practical, everyday solution, to non-rigid structure-from-motion. The proposed method is positively compared to the state-of-the-art in non-rigid structure-from-motion, providing improved results on high-frequency deformations of both articulated and simpler deformable shapes. © 1999-2012 IEEE.

Mufti F.,Center for Advanced Studies in Engineering | Mahony R.,Australian National University
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2011

Three-dimensional imaging systems have evolved significantly in the last two decades due to increase in demand for tasks in the field of close range photogrammetry. The fast and growing need of 3D imaging devices has given rise to range image technology, especially time-of-flight (TOF) cameras, that provide direct measurement of distance between the camera and the targeted surface. A significant advantage of TOF devices over traditional range data sensors is their capability to provide frame rate range data over a full image array. In phase shift TOF cameras, phase shift sampling of the received signal is used to measure amplitude, phase and the offset (intensity) of the received signal. As a result, the quality of the measurement of these sensors depends heavily on signal-to-noise (SNR) of the incoming signal and the subsequent processing algorithms. A detailed understanding of the statistical distributions of the measurement parameters is crucial for accurate distance measurement analysis especially in low SNR scenarios. In this paper, we provide explicit noise models for the three parameters of amplitude, phase and intensity. The proposed stochastic model helps in investigating the effect of noise on signal and classifying range data reliability in TOF cameras. The model is used for prediction of errors in a TOF camera under various SNR conditions. Experimental verification confirms the validity of the model using real data for range error classification under different noise conditions. © 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

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