@article{cuenca_ojha_salt_chow_2015, title={A non-uniform multi-rate control strategy for a Markov chain-driven Networked Control System}, volume={321}, ISSN={0020-0255}, url={http://dx.doi.org/10.1016/j.ins.2015.05.035}, DOI={10.1016/j.ins.2015.05.035}, abstractNote={In this work, a non-uniform multi-rate control strategy is applied to a kind of Networked Control System (NCS) where a wireless path tracking control for an Unmanned Ground Vehicle (UGV) is carried out. The main aims of the proposed strategy are to face time-varying network-induced delays and to avoid packet disorder. A Markov chain-driven NCS scenario will be considered, where different network load situations, and consequently, different probability density functions for the network delay are assumed. In order to assure mean-square stability for the considered NCS, a decay-rate based sufficient condition is enunciated in terms of probabilistic Linear Matrix Inequalities (LMIs). Simulation results show better control performance, and more accurate path tracking, for the scheduled (delay-dependent) controller than for the non-scheduled one (i.e. the nominal controller when delays appear). Finally, the control strategy is validated on an experimental test-bed.}, journal={Information Sciences}, publisher={Elsevier BV}, author={Cuenca, Ángel and Ojha, Unnati and Salt, Julián and Chow, Mo-Yuen}, year={2015}, month={Nov}, pages={31–47} }
 @inproceedings{ojha_asr_chow_2012, title={Gene library for real-time monitoring of large scale time-sensitive systems}, DOI={10.1109/isie.2012.6237319}, abstractNote={For time-sensitive applications with hard real-time constraint, the utility of a decision goes to zero if the deadline is missed thus it is very important to use methodologies that can deliver solutions within their time limit. For large scale monitoring and prediction systems with small time periods this problem renders conservative optimization techniques to be useless especially because of the time they take to calculate optimal values. In order to make real-time decisions, we need to introduce methods that are computationally light and can still maintain accuracy that is close to results given by optimization methods. A gene library was formulated that stored (i) the regulatory proteins in order to select the relevant features that determined the system behavior and (ii) computationally simple mappings that mapped these relevant features to the desired system state. The proposed method was implemented in an Intelligent Transportation System scenario to determine the rollover risk. A gene library was created that eliminated the need to perform heavy computations (solving second order differential equation) while still maintaining the accuracy of prediction to +/- 4% of the actual value in the normal operating range.}, booktitle={2012 IEEE International Symposium on Industrial Electronics (ISIE)}, author={Ojha, U. and Asr, N. R. and Chow, M. Y.}, year={2012}, pages={1535–1540} }
 @inproceedings{ojha_chow_2011, title={Gene libraries for a next generation warning system in intelligent transportation}, DOI={10.1109/iecon.2011.6119681}, abstractNote={Driver warning systems are the first step towards Intelligent Transportation System. There is a need for next generation warning systems that can integrate the information that is currently available in the vehicles with the information about the environment that the vehicles are operating in order to make more informed and accurate decisions. Integration of data from such different sources implies higher complexity of computation which is difficult to implement in real-time. Thus it is necessary to develop new methods that can integrate huge amount of data while meeting the hard real-time constraints of Intelligent Transportation Systems. In this paper, we introduce gene libraries that are based on the processes involved in gene expression. It is shown that gene libraries are capable of reducing the complexity of the problem by storing only the relevant information. A formulation for next generation warning system within the framework of gene libraries is proposed and simulations are presented that compare this approach with fuzzy inference system. Results show that gene library based approach is at least 23 times faster and 3.85 times more space efficient than fuzzy inference systems based approach.}, booktitle={Iecon 2011: 37th annual conference on ieee industrial electronics society}, author={Ojha, U. and Chow, M. Y.}, year={2011}, pages={2376–2381} }
 @article{klingenberg_ojha_chow_2009, title={Predictive Constrained Gain Scheduling For UGV Path Tracking in a Networked Control System}, ISBN={["978-1-4244-3803-7"]}, DOI={10.1109/iros.2009.5354413}, abstractNote={This paper presents a predictive gain scheduler for path tracking control in a networked control system with variable delay. The controller uses the plant model to predict future position and find the amount of travel possible with the global path as a constraint. Based on variable network conditions and vehicle trajectory's curvature the vehicle is allowed to travel farther on the current control signal while the vehicle trajectory matches the path constraint. This method uses path specific characteristics to evaluate the effectiveness of each generated control signal. By scheduling the gain on the control signal the vehicle tracking performance is maintained with an increase in network delay. The tracking time is decreased compared to other methods since the proposed control method allows the controller to look ahead and thus evaluate predicted effect of each control signal before scaling it. The proposed method is compared with existing delay compensation methods through simulation.}, journal={2009 IEEE-RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS}, author={Klingenberg, Bryan R. and Ojha, Unnati and Chow, Mo-Yuen}, year={2009}, pages={1935–1940} }