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Article overview
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Dynamic Tube MPC for Nonlinear Systems | | Brett T. Lopez
; Jean-Jacques E. Slotine
; Jonathan P. How
; | | Date: |
15 Jul 2019 | | Abstract: | Modeling error or external disturbances can severely degrade the performance
of Model Predictive Control (MPC) in real-world scenarios. Robust MPC (RMPC)
addresses this limitation by optimizing over feedback policies but at the
expense of increased computational complexity. Tube MPC is an approximate
solution strategy in which a robust controller, designed offline, keeps the
system in an invariant tube around a desired nominal trajectory, generated
online. Naturally, this decomposition is suboptimal, especially for systems
with changing objectives or operating conditions. In addition, many tube MPC
approaches are unable to capture state-dependent uncertainty due to the
complexity of calculating invariant tubes, resulting in overly-conservative
approximations. This work presents the Dynamic Tube MPC (DTMPC) framework for
nonlinear systems where both the tube geometry and open-loop trajectory are
optimized simultaneously. By using boundary layer sliding control, the tube
geometry can be expressed as a simple relation between control parameters and
uncertainty bound; enabling the tube geometry dynamics to be added to the
nominal MPC optimization with minimal increase in computational complexity. In
addition, DTMPC is able to leverage state-dependent uncertainty to reduce
conservativeness and improve optimization feasibility. DTMPC is demonstrated to
robustly perform obstacle avoidance and modify the tube geometry in response to
obstacle proximity. | | Source: | arXiv, 1907.6553 | | Services: | Forum | Review | PDF | Favorites | |
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