Input constraints handling in an MPC/feedback linearization scheme

Jiamei Deng, V. M. Becerra, Richard Stobart

    Research output: Contribution to journalArticlepeer-review


    The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real time MPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.
    Original languageEnglish
    Pages (from-to)219-232
    Number of pages14
    JournalInternational Journal of Applied Mathematics and Computer Science
    Issue number2
    Publication statusPublished - Jul 2009


    • predictive control, feedback linearization, neural networks, nonlinear, systems, constraints,


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