In collaboration with Payame Noor University and the Iranian Society of Instrumentation and Control Engineers

Document Type : Research Article

Author

Department of Basic Sciences‎, ‎Technical and Vocational University (TVU)‎, ‎Tehran‎, ‎Iran‎.

Abstract

This paper analyzes systems of linear first-order ordinary differential equations (ODEs) with parametric coefficients‎, ‎a class of problems that arises in control theory‎, ‎optimization‎, ‎and applied mathematics‎. ‎We introduce the notion of a comprehensive solution system for such parametric ODEs, constructed using Gröbner systems from computer algebra‎. ‎ Our approach partitions the parameter space into finitely many cells and associates an explicit solution with each cell‎. ‎ Furthermore, ‎we present an algorithm that computes a comprehensive solution system for any given parametric system‎. ‎To address the computational challenges inherent in Gröbner systems‎, ‎we adopt the GES algorithm‎, ‎a parametric variant of Gaussian elimination‎, ‎which eliminates the need for Gröbner bases. This method builds upon the LDS algorithm proposed in 2017‎. ‎ Both algorithms have been implemented in Maple‎, ‎and we illustrate the structural framework of the main algorithm with a straightforward example. The results highlight the practicality and effectiveness of the proposed methods for solving parametric linear first-order ODE systems.

Highlights

  • Investigation of linear first-order ODE systems with parametric coefficients, motivated by applications in control theory, optimization, and applied mathematics.
  • Definition of a comprehensive solution system (CSS) for parametric linear ODEs, which organizes parameter space into finite cells, with each cell paired to an explicit closed-form solution.
  • Development of the CSS algorithm to compute comprehensive solution systems for any given parametric linear first-order ODE system.
  • Adoption of the GES algorithm (parametric Gaussian elimination) to tackle computational challenges in Gröbner-system-based approaches, reducing reliance on Gröbner bases.
  • The CSS framework builds upon prior LDS methodology and integrates parametric linear algebra techniques to efficiently handle parameter dependencies.
  • All algorithms are implemented in Maple, enabling practical experimentation and reproducibility.
  • The Improved-CSS algorithm (based on GES) outperforms traditional Gröbner-based methods in efficiency across benchmark examples, indicating potential for real-time and large-scale parameter analyses.
  • Potential impact on control theory and optimization through systematic, parameter-aware analysis, including gain scheduling, robust stability analysis, and design of controllers or estimators with stability guarantees across parameter ranges.

Keywords

Main Subjects

[1] Åström, K.J., Murray, R.M. (2008). “Feedback systems: An introduction for scientists and engineers”. Princeton University Press, doi:https://doi.org/10.1515/9781400828739.
[2] Cox, D., Little, J.B., O’Shea, D.B. (2007). “Ideals, varieties, and algorithms. An introduction to computational algebraic geometry and commutative algebra”. 3rd ed. New York, NY: Springer, doi:http://dx.doi.org/10.1007/978-3-319-16721-3.
[3] Dehghani Darmian, M. (2024). “Improvement of an incremental signature-based comprehensive Gröbner system algorithm. Mathematics in Computer Science, 18(12), doi:http://dx.doi.org/10.1007/s11786-024-00587-w.
[4] Dehghani Darmian, M., Hashemi, A. (2024). “A Parametric F4 Algorithm”. Iranian Journal of Mathematical Sciences and Informatics, 19(1), 117-133, doi:http://dx.doi.org/10.61186/ijmsi.19.1.117.
[5] Dehghani Darmian, M. (2024). “Efficient algorithm for computing inverse of parametric matrices.” Scientific Annals of Computer Science, 34(1), 1-22, doi:http://dx.doi.org/10.47743/SACS.2024.1.1.
[6] Dehghani Darmian, M., Hashemi, A. (2017). “Parametric FGLM algorithm.” Journal of Symbolic Computation, 82, 38-56, doi:https://doi.org/10.1016/j.jsc.2016.12.006.
[7] Dehghani Darmian, M., Hashemi, A., Montes, A. (2011). Erratum to ”A new algorithm for discussing Gröbner bases with parameters” [J. Symbolic Comput. 33 (1-2) (2002) 183-208]. Journal of Symbolic Computation, 46(10), 1187-1188, doi:https://doi.org/10.1016/j.jsc.2011.05.002.
[8] Hashemi, A., Dehghani Darmian, M., Barkhordar, M. (2017). “Gröbner systems conversion.” Mathematics in Computer Science, 11(1), 61-77, doi:https://doi.org/10.1007/s11786-017-0295-3.
[9] Hashemi, A., Dehghani Darmian, M., Barkhordar, M. (2018). “Universal Gröbner basis for parametric polynomial ideals.” Mathematical software - ICMS 2018. 6th International Conference, South Bend, IN, USA, 2018. Proceedings, pages 191-199. Cham: Springer, doi:http://dx.doi.org/10.1007/978-3-319-96418-8_23.
[10] Hashemi, A., M.-Alizadeh, B., Dehghani Darmian, M. (2013). “Minimal polynomial systems for parametric matrices.” Linear and Multilinear Algebra, 61(2), 265-272, doi:https://doi.org/10.1080/03081087.2012.670235.
[11] Kapur, D., Sun, Y., Wang, D. (2011). “Computing comprehensive Gröbner systems and comprehensive Gröbner bases simultaneously.” ISSAC ’11: Proceedings of the 36th International Symposium on Symbolic and Algebraic Computation, 193-200, doi:http://dx.doi.org/10.1145/1993886.1993918.
[12] Kapur, D., Sun, Y., Wang, D. (2010). “A new algorithm for computing comprehensive Gröbner systems.” Proceedings of the 35th International Symposium on Symbolic and Algebraic Computation, ISSAC 2010, Munich, Germany, pages 29-36. New York, NY: Association for Computing Machinery (ACM), doi:https://doi.org/10.1145/1837934.1837946.
[13] Khalil, H.K. (2002). “Nonlinear systems”. 3rd Edition, Prentice Hall, Upper Saddle River.
[14] Logan, J.D. (2015). “A first course in differential equations.” Undergraduate Texts Mathematics, Cham: Springer, 3rd edition, doi:https://doi.org/10.1007/978-3-319-17852-3.
[15] Manubens, M., Montes, A. (2009). “Minimal canonical comprehensive Gröbner systems.” Journal of Symbolic Computation, 44(5), 463-478, doi:https://doi.org/10.1016/j.jsc.2007.07.022.
[16] Manubens, M., Montes, A. (2006). “Improving the DisPGB algorithm using the discriminant ideal.” Journal of Symbolic Computation, 41(11), 1245-1263, doi:https://doi.org/10.1016/j.jsc.2005.09.013.
[17] Montes, A. (2002). “A new algorithm for discussing Gröbner bases with parameters.” Journal of Symbolic Computation, 33(2), 183-208, doi:https://doi.org/10.1006/jsco.2001.0504.
[18] Montes, A., Castro, J. (1995). “Solving the load flow problem using the Gröbner basis.” ACM SIGSAM Bulletin, 29(1), 1-13, doi:https://doi.org/10.1145/216685.216686.
[19] Ogata, K. (2010). “Modern control engineering.” Prentice-Hall Electrical Engineering Series. Instrumentation and Controls Series.
[20] Rawlings, J.B., Mayne, D.Q., Diehl, M. (2017). “Model predictive control: Theory, computation, and design.” Nob Hill Publishing.
[21] Ritt, J.F. (1932). “Differential equations from the algebraic standpoint.Nature, 131, page 456, doi: https://doi.org/10.1038/131456a0.
[22] Ritt, J.F. (1950). “Differential algebra, Colloquium Publications, American Mathematical Society. Volume 33, doi:https://doi.org/10.1090/coll/033.
[23] Skogestad, S., Postlethwaite, I. (2005). “Multivariable feedback control: Analysis and design.” Wiley-Interscience.
[24] Suzuki, A., Sato, Y. (2006). “A simple algorithm to compute comprehensive Gröbner bases using Gröbner bases.” Proceedings of the 2006 International Symposium on Symbolic and Algebraic Computation, ISSAC ’06, Genova, Italy, pages 326-331. New York, NY: ACM Press, doi:http://dx.doi.org/10.1145/1145768.1145821.
[25] Weispfenning, V. (1992). “Comprehensive Gröbner bases.” Journal of Symbolic Computation, 14(1), 1-29, doi:https://doi.org/10.1016/0747-7171(92)90023-W.
[26] Wu, W-T. (1978).“On the decision problem and the mechanization of theorem-proving in elementary geometry.” Scientia Sinica, 21(2) , 117-138, doi:https://doi.org/10.1142/9789812791085_0008.
[27] Wu, W.T. (1986). “On zeros of algebraic equations - An application of Ritt principle.” Kexue Tongbao, Science Bulletin, 31, 225-229, doi:https://doi.org/10.1142/9789812791085_0013.
[28] Zhou, K., Doyle, J.C., Glover, K. (1996). “Robust and optimal control”. Feher/Prentice Hall Digital and Prentice Hall.