A Software Tool for Multiple Model Adaptive Control Design in Active Suspension System Applications

  • Authors

    • Ahmad Ruzaini Rahim
    • Sallehuddin Mohamed Haris
    https://doi.org/10.14419/ijet.v7i3.17.21915

  • Abstract

    The multiple model adaptive control (MMAC) method has the possibility to keep up control performance over an extensive variety of parameter variations. A key part in the design of a MMAC system is the choice of candidate models. A computer package tool has been developed to optimize and determine these candidate models. The method exploits the relationship between Grobner bases and polynomial spectral factorization through the notion of sum of roots. The symbolic solution to the Algebraic Riccati Equation, and hence the H2 optimal control problem, is obtained using computer algebra technique. The MMAC system was implemented on a quarter car active suspension with changing sprung mass. The symbolic solution giving the relationship between the H2 cost function and varying sprung mass values was obtained. The user inputs the number of candidate models to be used, and the package selects suitable intervals between candidate cost values. From the analytical solution obtained, sprung mass values corresponding to each selected candidate cost value could be found.  The software functions as a tool to optimally choose sprung mass values for each candidate model in the MMAC active suspension system.

  • References

    1. [1] Gaspar, P., Szaszi, I and Bokor, J. 2003. Design of robust controllers for active vehicle suspension using the mixed µ-synthesis, Vehicle System Dynamics, 40(4), pp. 193–228.

      [2] Athans, M., Dunn, K.-p., Greene, C.S., Lee, W.H., Sandell, N.R., Segall, I. and Willsky, A.S. 1975. The stochastic control of the F-8C aircraft using the multiple model adaptive control (MMAC) method, 1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes, Houston, TX, USA, 217-228.

      [3] Chen W. and Anderson, B.D.O. 2009. Multiple model adaptive control (MMAC) for nonlinear systems with nonlinear parameterization, Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference, CDC/CCC 2009, 7669 - 7674.

      [4] Du, H., Zhang, N. and Lam, J. 2008. Parameter-dependent input delayed control of uncertain vehicle suspensions, Journal of Sound and Vibration, 317:537-556.

      [5] Gao, G. and Yang, S. 2006. Semi-active control performance of railway vehicle suspension featuring magnetorheological dampers, Proc. of the 1ST IEEE Conference on in Industrial Electronics and Applications, 1-5.

      [6] Du, H. and Zhang, N. 2007. H∞ control of active vehicle suspensions with actuator time delay, Journal of Sound and Sibration, 301, pp. 236-252, 2007.

      [7] Haris, S.M., Shavalipour, A., and Nopiah, Z.M. 2015. International Review on Modelling and Simulations (I.RE.MO.S.), 8(3):377-385.

      [8] Haris, S.M., Shavalipour, A., Nopiah, Z.M. and Yaacob, Y. 2016. Multiple model adaptive control design: A computer algebra approach, International Journal of Advancements in Mechanical and Aeronautical Engineering, 3(1):131-136.

      [9] Hespanha, J.P. and Morse, A.S., 2002. Switching between stabilizing controllers, Automatica, 38:1905–1917.

      [10] Hespanha, J.P., Liberzon, D. and Morse, A.S. 2003. Overcoming the limitations of adaptive control by means of logic-based switching, Systems and Control Letters, 49:49–65.

      [11] Koch, G. 2011. Adaptive Control of Mechatronic Vehicle Suspension, Ph.D Thesis, Technical University of Munich, Institute of Automatic Control.

      [12] Zhong, X., Ichchou, M., Gillot, F. and Saidi, A. 2010. A dynamic-reliable multiple model adaptive controller for active vehicle suspension under uncertainties. Smart Materials and Structures, 19(4), 045007.

      [13] Anai, H., Hara, S., Kanno, M. and Yokoyama, K. 2009. Parametric polynomial spectral factorization using the sum of roots and its application to a control design problem‖, Journal of Symbolic Computation, 44:703-725.

      [14] Kanno, M. and Hara, S. 2012. Symbolic-numeric hybrid optimization for plant/controller integrated design in H∞ loop-shaping design. Journal of Math-for-Industry (JMI), 4:135-140.

      The multiple model adaptive control (MMAC) method has the possibility to keep up control performance over an extensive variety of parameter variations. A key part in the design of a MMAC system is the choice of candidate models. A computer package tool has been developed to optimize and determine these candidate models. The method exploits the relationship between Grobner bases and polynomial spectral factorization through the notion of sum of roots. The symbolic solution to the Algebraic Riccati Equation, and hence the H2 optimal control problem, is obtained using computer algebra technique. The MMAC system was implemented on a quarter car active suspension with changing sprung mass. The symbolic solution giving the relationship between the H2 cost function and varying sprung mass values was obtained. The user inputs the number of candidate models to be used, and the package selects suitable intervals between candidate cost values. From the analytical solution obtained, sprung mass values corresponding to each selected candidate cost value could be found. The software functions as a tool to optimally choose sprung mass values for each candidate model in the MMAC active suspension system.

  • Downloads

  • How to Cite

    Rahim, A. R., & Haris, S. M. (2018). A Software Tool for Multiple Model Adaptive Control Design in Active Suspension System Applications. International Journal of Engineering & Technology, 7(3.17), 234-238. https://doi.org/10.14419/ijet.v7i3.17.21915

    Received date: 2018-11-27

    Accepted date: 2018-11-27