Abstract
LMI (linear matrix inequality) techniques offer more flexibility in the design of dynamic linear systems than techniques that minimize a scalar functional for optimization. For linear state space models, multiple goals (performance bounds) can be characterized in terms of LMIs, and these can serve as the basis for controller optimization via finite-dimensional convex feasibility problems. LMI formulations of various standard control problems are described in this article, including dynamic feedback stabilization, covariance control, LQR, H ∞ control, L ∞ control, and information architecture design.
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Skelton, R.E. (2014). Linear Matrix Inequality Techniques in Optimal Control. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_207-1
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DOI: https://doi.org/10.1007/978-1-4471-5102-9_207-1
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Latest
Linear Matrix Inequality Techniques in Optimal Control- Published:
- 16 October 2019
DOI: https://doi.org/10.1007/978-1-4471-5102-9_207-2
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Linear Matrix Inequality Techniques in Optimal Control- Published:
- 03 March 2014
DOI: https://doi.org/10.1007/978-1-4471-5102-9_207-1