On the performance improvement of the optimal-sampling-inspired self-triggered control at implementation stage

  1. Rosero Chandi, Carlos Xavier 1
  2. Vaca Orellana, Cristina Fernanda 1
  3. Benavides Piedra, Juan Pablo 1
  1. 1 Universidad Técnica del Norte
    info

    Universidad Técnica del Norte

    Ibarra, Ecuador

    ROR https://ror.org/03f0t8b71

Revista:
MASKAY

ISSN: 1390-6712

Año de publicación: 2017

Título del ejemplar: MASKAY

Volumen: 7

Número: 1

Páginas: 15-21

Tipo: Artículo

DOI: 10.24133/MASKAY.V7I1.344 DIALNET GOOGLE SCHOLAR

Otras publicaciones en: MASKAY

Resumen

The self-triggered control includes a sampling strategy that focuses on decreasing the use of computational resources (processor and network) while preserving the same control performance as the one obtained via a controller with periodic sampling. Within this framework it has been developed recently a self-triggered control technique inspired by a sampling pattern whose optimal density minimizes the control cost, this approach is called “optimal-sampling inspired self-triggered control”. However, the strategies used to implement it on microprocessor-controlled systems working under perturbation are still unclear; this paper addresses some techniques to organize and improve the implementation on actual controllers. The proposed solution comprises both the formulation of two algorithms to organize the implementation and the insertion of a closed-loop observer to deal with the perturbation that normally appears on real plants. Regarding the former, certain computationally expensive processes involved in the implementation of this control technique are treated through their replacement by lightweight polynomials fitted at design stage. Simulations and practical experiments confirm the solution is effective and there could be an open research topic concerning observation in optimal-sampling self-triggered control strategies.

Referencias bibliográficas

  • [1] M. Velasco, J.M. Fuertes, and P. Mart´ı, “The Self Triggered Task Model for Real-Time Control Systems,” in Proc. RTSS, Cancun, Mexico, Dec., 2003, pp. 67-70.
  • [2] A. Anta and P. Tabuada, “To Sample or Not to Sample: Self-Triggered Control for Nonlinear Systems,” IEEE Trans. Autom. Control, vol. 55, no. 9, pp. 2030-2042, Sept. 2010.
  • [3] M. Mazo Jr., A. Anta and P. Tabuada, “An ISS Self-Triggered Implementation of Linear Controllers,” in Automatica, vol. 46, no. 8, pp. 1310-1314, Aug. 2010.
  • [4] J. Almeida, C. Silvestre and A.M. Pascoal, “Self-Triggered Output Feedback Control of Linear Plants,” in Proc. ACC, San Francisco, CA, USA, June-July, 2011, pp. 2831-2836.
  • [5] A. Molin and S. Hirche, ”On the Optimality of Certainty Equivalence for Event-Triggered Control Systems”, in IEEE Trans. Autom. Control, vol. 58, no. 2, pp. 470-474, Feb. 2013.
  • [6] E. Bini and G.M. Buttazzo, “The Optimal Sampling Pattern For Linear Control Systems,” in IEEE Trans. Autom. Control, vol. 59, no. 1, pp. 78-90, Jan. 2014.
  • [7] M. Velasco, P. Mart´ı and E. Bini, “Optimal-Sampling-inspired Self-Triggered Control,” in Int. Conf. EBCCSP, Krakow, Poland, June, 2015, pp. 1-8.
  • [8] K.J. A° stro¨m and B. Wittenmark, Computer-Controlled Systems: Theory and Design, 3rd ed., Upper Saddle River, NJ, USA: Prentice Hall, 1997.
  • [9] T. Gommans, D. Antunes, T. Donkers, P. Tabuada, and M. Heemels, ”Self-Triggered Linear Quadratic Control”, in Automatica, vol. 50, no. 4, pp. 1279-1287, Apr. 2014.
  • [10] C. Rosero, C. Vaca, L. Tobar and F. Rosero, “Performance of Self-Triggered Control Approaches,” in Enfoque UTE, vol. 8, no. 2, pp. 107-120, Mar. 2017.
  • [11] J. Almeida, C. Silvestre and A.M. Pascoal, “Observer Based Self-Triggered Control of Linear Plants with Unknown Disturbances,” in Proc. ACC, Montreal, Canada, June, 2012, pp. 5688-5693.
  • [12] X. Wang and M.D. Lemmon, “Self-Triggering Under State-Independent Disturbances,” in IEEE Trans. Autom. Control, vol. 55, no. 6, pp. 1494-1500, June 2010.
  • [13] W.F. Arnold and A.J. Laub, “Generalized Eigenproblem Algorithms and Software for Algebraic Riccati Equations,” in Proc. IEEE, vol. 72, no. 12, pp. 1746-1754, Dec. 1984.
  • [14] D. Luenberger, “An Introduction to Observers,” in IEEE Trans. Autom. Control, vol. 16, no. 6, pp. 596-602, Dec. 1971.
  • [15] J. Ackermann, “On the Synthesis of Linear Control Systems with Specified Characteristics,” in Automatica, vol. 13, no. 1, pp. 89-94, Jan. 1977.
  • [16] F. Paez, R. Cayssials, J. Urriza, E. Ferro and J. Orozco, “Frequency Domain Analysis of a RTOS in Control Applications,” in Cong. CASE, Buenos Aires, Argentina, Aug., 2016, pp. 21-26.
  • [17] S.A. Dyer and X. He, “Least-squares fitting of data by polynomials,” in IEEE Instrum. Meas. Mag., vol. 4, no. 4, pp. 46-51, Dec. 2001.
  • [18] C. Lozoya, P. Mart´ı, M. Velasco, J. Fuertes and E. Martin, “Resource and Performance Trade-offs in Real-Time Embedded Control Systems,” in J. Real-Time Systems, vol. 49, no. 3, pp. 267-307, May 2013.
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