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|Abstract:||In this paper, we present a framework for risk-sensitive model predictive control (MPC) of linear systems affected by stochastic multiplicative uncertainty. Our key innovation is to consider a time-consistent, dynamic risk evaluation of the cumulative cost as the objective function to be minimized. This framework is axiomatically justified in terms of time-consistency of risk assessments, is amenable to dynamic optimization, and is unifying in the sense that it captures a full range of risk preferences from risk neutral (i.e., expectation) to worst case. Within this framework, we propose and analyze an online risk-sensitive MPC algorithm that is provably stabilizing. Furthermore, by exploiting the dual representation of time-consistent, dynamic risk measures, we cast the computation of the MPC control law as a convex optimization problem amenable to real-Time implementation. Simulation results are presented and discussed.|
|Citation:||Singh, S, Chow, Y, Majumdar, A, Pavone, M. (2019). A Framework for Time-Consistent, Risk-Sensitive Model Predictive Control: Theory and Algorithms. IEEE Transactions on Automatic Control, 64 (2905 - 2912. doi:10.1109/TAC.2018.2874704|
|Pages:||2905 - 2912|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||IEEE Transactions on Automatic Control|
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