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Observer-Based Environment Robust Control Barrier Functions for Safety-critical Control with Dynamic Obstacles

Autor: Quan, Ying Shuai, Zhou, Jian, Frisk, Erik, Chung, Chung Choo

This paper proposes a safety-critical controller for dynamic and uncertain environments, leveraging a robust environment control barrier function (ECBF) to enhance the robustness against the measurement and prediction uncertainties associated with mo

Externí odkaz: http://arxiv.org/abs/2403.13288

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K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles

Autor: Kim, Jin Sung, Quan, Ying Shuai, Chung, Chung Choo

This paper proposes Koopman operator-based Stochastic Model Predictive Control (K-SMPC) for enhanced lateral control of autonomous vehicles. The Koopman operator is a linear map representing the nonlinear dynamics in an infinite-dimensional space. Th

Externí odkaz: http://arxiv.org/abs/2310.10214

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Uncertainty Quantification of Autoencoder-based Koopman Operator

Autor: Kim, Jin Sung, Quan, Ying Shuai, Chung, Chung Choo

Publikováno v: 2024 American Control Conference

This paper proposes a method for uncertainty quantification of an autoencoder-based Koopman operator. The main challenge of using the Koopman operator is to design the basis functions for lifting the state. To this end, this paper builds an autoencod

Externí odkaz: http://arxiv.org/abs/2309.09419

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RNN Controller for Lane-Keeping Systems with Robustness and Safety Verification

Autor: Quan, Ying Shuai, Kim, Jin Sung, Chung, Chung Choo

This paper proposes a Recurrent Neural Network (RNN) controller for lane-keeping systems, effectively handling model uncertainties and disturbances. First, quadratic constraints cover the nonlinearities brought by the RNN controller, and the linear f

Externí odkaz: http://arxiv.org/abs/2309.08852

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