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Model predictive control (MPC) has become more relevant to vehicle dynamics control due to its inherent capacity of treating system constraints. However, online optimization from MPC introduces an extensive computational burden for today’s onboard microprocessors. To alleviate MPC computational load, several methods have been proposed. Among them, online successive system linearization and the resulting linear time-varying model predictive controller (LTVMPC) is one of the most popular options. Nevertheless, such online successive linearization commonly approximates the original (nonlinear) system by a linear one, which inevitably introduces extra modeling errors and therefore reduces MPC performance. Actually, if the controlled system possesses the “differential flatness” property, then it can be exactly linearized and an equivalent linear model will appear. This linear model maintains all the nonlinear features of the original system and can be utilized to design a flatness-based model predictive controller (FMPC). CarSim-Simulink joint simulations demonstrate that the proposed FMPC substantially outperforms a classical LTVMPC in terms of the path-tracking performance for autonomous vehicles.
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Risk-Aware Model Predictive Path Integral Control Using Conditional Value-at-Risk
In this paper, we present a novel Model Predictive Control method for autonomous robot planning and control subject to arbitrary forms of uncertainty. The proposed Risk- Aware Model Predictive Path Integral (RA-MPPI) control utilizes the Conditional Value-at-Risk (CVaR) measure to generate optimal control actions for safety-critical robotic applications. Different from most existing Stochastic MPCs and CVaR optimization methods that linearize the original dynamics and formulate control tasks as convex programs, the proposed method directly uses the original dynamics without restricting the form of the cost functions or the noise. We apply the novel RA-MPPI controller to an autonomous vehicle to perform aggressive driving maneuvers in cluttered environments. Our simulations and experiments show that the proposed RA-MPPI controller can achieve similar lap times with the baseline MPPI controller while encountering significantly fewer collisions. The proposed controller performs online computation at an update frequency of up to 80 Hz, utilizing modern Graphics Processing Units (GPUs) to multi-thread the generation of trajectories as well as the CVaR values.
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- Award ID(s):
- 2219755
- PAR ID:
- 10499424
- Editor(s):
- risk-aware planning; Conditional Value-at-Risk; Model Predictive Control; Model Predictive Path Integral
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE International Conference on Robotics and Automation
- ISSN:
- 2152-4092
- ISBN:
- 979-8-3503-2365-8
- Page Range / eLocation ID:
- 7937 to 7943
- Format(s):
- Medium: X
- Location:
- London, United Kingdom
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA48891.2023.10161100
