Abstract
This entry reviews optimization algorithms for both linear and nonlinear model predictive control (MPC). Linear MPC typically leads to specially structured convex quadratic programs (QP) that can be solved by structure exploiting active set, interior point, or gradient methods. Nonlinear MPC leads to specially structured nonlinear programs (NLP) that can be solved by sequential quadratic programming (SQP) or nonlinear interior point methods.
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Diehl, M. (2013). Optimization Algorithms for Model Predictive Control. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_9-1
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DOI: https://doi.org/10.1007/978-1-4471-5102-9_9-1
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