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

  • 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

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      [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.

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      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.

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  • 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