Abstract
Optimal control theory Patrick J. can be used to generate aggressive maneuvers for vehicles under a variety of conditions using minimal assumptions. Although optimal control provides a very powerful framework for generating aggressive maneuvers utilizing fully nonlinear vehicle and tire models, its use in practice is hindered by the lack of guarantees of convergence, and by the typically long time to generate a solution, which makes this approach unsuitable for real-time implementation unless the problem obeys certain convexity and/or linearity properties. In this chapter, we investigate the use of statistical interpolation (e.g., kriging) in order to synthesize on-the-fly near-optimal feedback control laws from pre-computed optimal solutions. We apply this methodology to the challenging scenario of generating a minimum-time yaw rotation maneuver of a speeding vehicle in order to change its posture prior to a collision with another vehicle, in an effort to remedy the effects of a head-on collision. It is shown that this approach offers a potentially appealing option for real-time, near-optimal, robust trajectory generation.
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A stationary covariance process has constant mean and variance and the covariance matrix depends only on the distance between the corresponding inputs.
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Acknowledgments
Partial support for the work presented in this chapter has been provided by NSF through award no. CMMI-1234286 and ARO via MURI award no. W911NF-11-1-0046.
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Tsiotras, P., Diaz, R.S. (2014). Real-Time Near-Optimal Feedback Control of Aggressive Vehicle Maneuvers. In: Waschl, H., Kolmanovsky, I., Steinbuch, M., del Re, L. (eds) Optimization and Optimal Control in Automotive Systems. Lecture Notes in Control and Information Sciences, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-319-05371-4_7
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