Conference Papers-Robust Control

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

11. A. Jadbabaie, C.T. Abdallah, and P. Dorato, "Guaranteed-Cost Control of Polynomial Nonlinear Systems", Proceedings of the 37th IEEE Conference on Decision and Control, pp.4100-4102, Tampa, FL., Dec. 1998.   [pdf]

    Abstract: This paper deals with the control of a class of nonlinear systems which are affine in the control. We use Bernstein polynomials and Polytopic Linear Differential Inclusion (PLDI) to design gain-scheduled controllers using a guaranteed cost framework.




12. V. koltchinski, S. Efromovich, C.T. Abdallah, G.L. Heileman "Tracking Control of Uncertain Systems", Proceedings of the 37th IEEE Conference on Decision and Control, pp.1867-1868, Tampa, FL., Dec. 1998.   [pdf]

    Abstract: This paper deals with the problem of designing output tracking controllers for uncertain systems. The systems we consider may be non-minimum phase but are restricted to be linear. The problem is motivated by control applications where a desired output trajectory is specied, and the corresponding input to the system is to be found.




13. F. L. Lewis, G. Maliotis, C. T. Abdallah, "Robust Adaptive Control for a Class of Partially Known Nonlinear Systems", Proceedings of the 27th Conference on Decision and Control, pp. 24-30, Austin, TX, Dec. 1988.   [pdf]

    Abstract: An adaptive controller is developped that takes advantage of the structure and any known dynamics of the system in order to increase speed of adaptation and relax the conditions required for convergence.
    The control design method has two stages. First, the known dynamics are separated out and used to perform a global linearization on the nonlinear system. Second, a model-reference adaptive control, based on the lyapunov stability criterion, is designed for the remaining unknown portion of the plant. This control scheme is shown to relax several assumptions usually made in applying adaptive control to a manipulator system. For instance, it relaxes the common assumption that the time varying plant is close to the desired model.




14. A. Jadbabaie, C.T. Abdallah, M. Jamshidi, and P. Dorato, "Guaranteed-Cost Control of the Nonlinear Benchmark Problem Using Model-Based Fuzzy Systems", Proceedings of 1998 IEEE International Conference on Control Application, pp.792-796, Trieste , Italy, 1-4 Sept. 1998.   [pdf]

    Abstract: In this paper we design a state-feedback controller for the nonlinear benchamrk problem. Our approach relies on the use of Takagi-Sugeno fuzzy models to approxiamte the nonlinear system. Once the fuzy model is obtained, we develop a guaranteed-cost framework to design the controller using Linear Matrix Inequality methods and recently obtained relaxed stability conditions. We show that our proposed controller will not only stabilize the system, but also has satisfactory disturbance attenuation properties.




15. A. Jadbabaie, C.T. Abdallah, D. Famularo, P. Dorato, "Robust, Non-Fragile and Optimal Controller Design Via Linear Matrix Inequalities", Proceedings of the American Control Conference, pp.2842-2846, Philadelphia, PN, June 1998.   [pdf]

    Abstract: In this article, we introduce a robust non-fragile state feedback controller which is also optimal with respect to a quadratic performance index, using Linear Matrix Inequalities (LMIs). The uncertainties are assumed to be polytopic, both in the controller gains and the system dynamics. A numerical example is presented to demonstrate the efficiency of this method, and the controller turns out to be robust with respect to the uncertainties in the plant and the controller.




16. D. Famularo, C.T. Abdallah, A. Jadbabaie, P. Dorato W.M. Haddad, "Robust Non-fragile LQ Controllers: The Static State Feedback Case", Proceedings of the American Control Conference, pp.1109-1113, Philadelphia, PN, June 1998.   [pdf]

    Abstract: This paper describes the synthesis of non-fragile or resilient regulators for linear systems. The general framework for fragility is described using state-space methodologies, and the LQ/Xz static state-feedback case is examined in detail. We discuss the multiplicative structured uncertainties case, and propose remedies of the fragility problem using a convex programming framework (LMIs) as a possible solution scheme. The benchmark problem is taken as an example to show how controller gain variations can affect the performance of the closed-loop system.




17. R.A. Luke, P. Dorato, C.T. Abdallah, "Guaranteed Gain-Phase Margins for Multi-Model Control", Proceedings of the American Control Conference, pp.3497-3501, Philadelphia, PN, June 1998.   [pdf]

    Abstract: In the simultaneous performance design problem considered by the authors [5], linear-quadratic cost function state and control weightings are assumed. A single static state feedback gain is determined which minimizes the guaranteed-cost bound [1] for each of the systems. It is now shown that subject to certain restrictions, the guaranteed-cost gain results in “non-fragile” system control: an infinite increasing gain margin, a decreasing gain margin of 1/2, and phase margins of sixty degrees for each system. The converse is also considered: given a guaranteed-cost gain, the set of all state and control weightings are found for which that gain remains optimal. This is possible through the use of a Kalman matrix identity.




18. P. Dorato, C. T. Abdallah and D. Famularo, "On the Design of Non-Fragile Compensators via Symbolic Quantifier Elimination", World Automation Congress(WAC'98), Alaska, May 10-14, 1998. Proceedings printed in Intelligent Automation and Control, Volume 6, TSI Press, pp. 363-368, Albuquerque, NM.   [pdf]  [ps]

    Abstract: In this paper symbolic quantifier elimination methods are used to explore the fragility of feedback compensators, and to design feedback systems with non-fragile compensators. A compensator is said to be fragile if given variations in compensator parameters result in significant deterioration of feedback performance. The issue of fragility is important in understanding the level of acurracy required to implement a given compensator design.




19. V. Koltchinski, S. Efromovich, C. T. Abdallah and G.L. Heileman, "Tracking Control of Uncertain Systems", Proceedings IEEE Conference on Decision and Control, pp.1867-1868, 1998.   [pdf]  [ps]
    

    Abstract: This paper deals with the problem of designing output tracking controllers for uncertain systems. The systems we consider may be non-minimum phase but are restricted to be linear. The problem is motivated by control applications where a desired output trajectory is specified, and the corresponding input to the system is to be found.




20. C. T. Abdallah and F.Perez-Gonzalez, "On the Fragility of High-Dimensional Controllers", Proceedings IEEE Conference on Decision and Control, 1998.   [pdf]  [ps]
    

    Abstract:In this paper we study the fragility of controllers designed to optimize some performance indices. We trace the fragility problem to the dimension of the resulting controllers, and use results from high-dimensional geometry to analyze the problem both in the continuous and discrete domains...

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