Conference Papers-Randomized Algorithms

Communications & Networks

Image & Signal Processing

Load Balancing

Microwave Propelled Sail

Randomized Algorithms

Time Delay

Multi-Agent Coordination

Engineering Education

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Note: The papers on this website may differ from the published versions, both in format and in content.

1. M. Ariola, C. T. Abdallah, and V. Koltchinski, "Applications of Statistical-Learning Control in Systems and Control", Proceedings IFAC Workshop on Adaptation and Learning in Control and Signal Processing, pp. 175-180, Villa Erba, Cernobbio - Como (Italy), August 29-31, 2001.   [pdf]  [ps]

   Extended Abstract




2. M. Ariola, C. T. Abdallah, and V. Koltchinski, "Applications of Statistical-Learning Control in Systems and Control", Proceedings IFAC Workshop on Adaptation and Learning in Control and Signal Processing, pp. 175-180, Villa Erba, Cernobbio - Como (Italy), August 29-31, 2001.   [pdf]  [ps]

   Extended Abstract




3. V. Koltchinski, C. T. Abdallah, M. Ariola, P. Dorato and D. Pachenko, "Improved Sample Complexity Estimates for Statistical Learning Control of Uncertain Systems", IEEE Transactions on Automatic Control, Vol. 45, No. 12 , pp. 2383-2388, December 2000.   [pdf]  [ps]

   Abstract: Recently, probabilistic methods and statistical learning theory have been shown to provide approximate solutions to “difficult” control problems. Unfortunately, the number of samples required in order to guarantee stringent performance levels may be prohibitively large. This paper introduces bootstrap learning methods and the concept of stopping times to drastically reduce the bound on the number of samples required to achieve a performance level. We then apply these results to obtain more efficient algorithms which probabilistically guarantee stability and robustness levels when designing controllers for uncertain systems.




4. C.T. Abdallah, M. Ariola, R. Byrne, "Statistical-Learning Control of an ABR Explicit Rate Algorithm for ATM Switches", Proceedings of the 39th IEEE Conference on Decision and Control, pp.53-54, Sydney, Australia, Dec. 2000.   [pdf]

   Abstract: This paper illustrates the application of statisticallearning control results for the design of an Available Bit Rate (ABR) congestion control algorithm. The proposed methodology allows us to take into account the nonlinearities of the model and the uncertainty of the parameters in the design phase. Some simulation results are shown.




5. V. Koltchinskii, C.T. Abdallah, M. Ariola, P. Dorato, and D. Panchenko, "Improved Sample Complexity Estimates for Statistical Learning Control of Uncertain Systems", IEEE Transactions On Automatic Control, VOL. 45, NO. 12, pp.2383-2388, Dec. 2000.   [pdf]

   Abstract: Recently, probabilistic methods and statistical learning theory have been shown to provide approximate solutions to “difficult” control problems. Unfortunately, the number of samples required in order to guarantee stringent performance levels may be prohibitively large. This paper introduces bootstrap learning methods and the concept of stopping times to drastically reduce the bound on the number of samples required to achieve a performance level.We then apply these results to obtain more efficient algorithms which probabilistically guarantee stability and robustness levels when designing controllers for uncertain systems.




6. K. Horspool, R. Scott Erwin,C. T. Abdallah, and P. Dorato, "A Randomized Approach to the H₂/H_∞ Control Problem via Q-Parametrization", Proceedings 2000 ACC, Chicago, Illinois, pp. 1400-1404, June 2000.   [pdf]  [ps]

   Abstract: In this paper we show that the mixed H₂/H_∞ control problem can be realistically solved using random algorithms, and searching over an interval in an intelligent manner. Q-parameterization provides a mechanism to search over all stabilizing controllers, and thus gives more freedom to search for H₂ minimizing controllers, while still providing robustness. Finally, we are able to show that we can get results that are comparable to a more traditional approach such as gradient search.




7. F.L. Lewis, B.G. Horne, and C.T. Abdallah, "Computational Complexity of Determining Resource Loops in Reentrant Flow Lines", IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, VOL. 30, NO. 2, pp.222-229, March 2000.   [pdf]

   Abstract: This paper presents a comparison study of the computational complexity of the general job shop protocol and the more structured flow line protocol in a flexible manufacturing system. It is shown that the representative problem of finding resource invariants is N P-complete in the case of the job shop, while in the flow line case it admits a closed form solution. The importance of correctly selecting part flow and job routing protocols in flexible manufacturing systems to reduce complexity is thereby conclusively demonstrated.




8. C. T. Abdallah, M. Ariola, and R.H. Byrne, "Statistical-Learning Control of an ABR Explicit Rate Algorithm for ATM Switches", Proceedings IEEE Conference on Decision and Control, 2000 IEEE CDC, Sydney, Australia, pp. 53-54.    [pdf]  [ps]

   Extended Abstract




9. V. Koltchinskii, M. Ariola, C.T. Abdallah, P. Dorato, "Statistical Controller Design for the Linear Benchmark Problem", Proceedings of the 38th Conference on Decision and Control, pp.507-509, Phoenix, AZ, Dec. 1999.   [pdf]

   Abstract: In this paper some fixed-order controllers are designed via statistical methods for the Benchmark Problem originally presented at the 1990 American Control Conference. Based on some recent results by the authors, it is shown that the statistical approach is a valid method to design robust controllers. Two different controllers are proposed and their performance are compared with controllers with the same structure, designed using different techniques.




10. V. Koltchinskii, C.T. Abdallah, M. Ariola, "Statistical Learning Control of Delay Systems: Theory and Algorithms", Proceedings of the 38th Conference on Decision and Control, pp.4696-4699, Phoenix, AZ, Dec. 1999.   [pdf]

    Abstract: Recently, probabilistic methods and statistical learning theory have been shown to provide approximate solutions to "difficult" control problems. Unfortunately, the number of samples required in order to guarantee stringent performance levels may be prohibitively large. In this paper, using recent results by the authors, a more efficient statistical algotithm is presened. Using this algorithm we design static output controllers for a nonlinear plant with uncertain delay.




11. V. Koltchinski, C. T. Abdallah, M. Ariola, P. Dorato and D. Pachenko, "Improved Sample Complexity Estimates for Statistical Learning Control of Uncertain Systems", IEEE Transactions on Automatic Control, VOL. 45, NO. 12, pp.2383-2388, Dec. 2000. [pdf]

    Abstract: Recently, probabilistic methods and statistical learning theory have been shown to provide approximate solutions to “difficult” control problems. Unfortunately, the number of samples required in order to guarantee stringent performance levels may be prohibitively large. This paper introduces bootstrap learning methods and the concept of stopping times to drastically reduce the bound on the number of samples required to achieve a performance level.We then apply these results to obtain more efficient algorithms which probabilistically guarantee stability and robustness levels when designing controllers for uncertain systems.