Abstract: Addressing computational issues in Model Predictive Control (MPC) is
critical in making MPC applicable for systems with fast dynamics and
limited computational resources. One MPC implementation strategy which
alleviates computational demands is to approximate the MPC optimal
control solution by a nominal solution (often pre-computed or computed
off-line) and a perturbation solution. For systems without constraints,
an optimal perturbation analysis has been well developed in the
literature. The talk introduces a perturbation analysis method for
discrete-time optimal control problems subject to a general class of
inequality constraints, in order to approximate the optimal control
solution. Moreover, an Integrated Perturbation Analysis and Sequential
Quadratic Programming (IPA-SQP) algorithm which uses approximation of
optimal solution according to perturbation analysis combined with SQP
method will be presented. The proposed algorithm combines the
complementary features of perturbation analysis and SQP in a single
unified framework, thereby leading to improved computational
efficiency. An experimental application of our approach to a DC/DC
converter control. If time permits the talk will continue to address
the the robustness of biological models with respect to parameter
variations and how it affects the oscillatory behavior.
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