| Description | Abstract: We consider the Model Predictive Control (MPC) algorithm applied to a finite-horizon continuous-time optimal control problem with non-
linear dynamics, an integral cost, and control constraints. Under a coercivity
condition and the condition that the optimal control is an isolated solution
of the variational inequality in the maximum principle, we establish uniform
Lipschitz stability of the discretized problem, the existence of an optimal
feedback control, and uniform convergence of the Newton/SQP method. We also give an estimate for the L1 norm of the difference between the MPC-
generated control and the optimal feedback control. The talk is based on the following papers available as research reports at
orcos.tuwien.ac.at…
1. A. L. Dontchev, M. I. Krastanov, V. M. Veliov: On the existence of
Lipschitz continuous optimal feedback control. Vietnam J. Math. 47 (2019),
no. 3, 579{597.
2. A. L. Dontchev, I. V. Kolmanovsky, M. I. Krastanov, M. M. Nicotra,
V. M. Veliov: Lipschitz Stability in Discretized Optimal Control. SIAM J.
Control Optim. 57 (2019), no. 1, 468{489.
3. A. L. Dontchev, M. Huang, I. V. Kolmanovsky, M. M. Nicotra: Inexact
Newton-Kantorovich Methods for Constrained Nonlinear Model Predictive
Control. IEEE Transactions on Automatic Control, 2019, 64(9), 3602{3615
4. A. L. Dontchev, I. V. Kolmanovsky, M. I. Krastanov, V. M. Veliov, P.
T. Vuong: Approximating optimal nite horizon feedback by model predictive control, submitted.
5. A. L. Dontchev, I. V. Kolmanovsky, D. Liao-McPherson, M. M. Nicotra, V. M. Veliov, Sensitivity-based warmstarting for constrained model predictive control, submitted. |
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