1 code implementation • 4 Aug 2023 • Dong Chen, Kaixiang Zhang, Yongqiang Wang, Xunyuan Yin, Zhaojian Li, Dimitar Filev
Connected and autonomous vehicles (CAVs) promise next-gen transportation systems with enhanced safety, energy efficiency, and sustainability.
no code implementations • 29 Jun 2023 • Amin Vahidi-Moghaddam, Kaian Chen, Kaixiang Zhang, Zhaojian Li, Yan Wang, Kai Wu
Despite great successes, model predictive control (MPC) relies on an accurate dynamical model and requires high onboard computational power, impeding its wider adoption in engineering systems, especially for nonlinear real-time systems with limited computation power.
no code implementations • 7 Jun 2023 • Amin Vahidi-Moghaddam, Kaixiang Zhang, Zhaojian Li, Xunyuan Yin, Ziyou Song, Yan Wang
In this work, an extended NE (ENE) framework is developed to systematically adapt the nominal control to both state and preview perturbations.
no code implementations • 2 Jun 2023 • Dongjun Li, Kaixiang Zhang, Haoxuan Dong, Qun Wang, Zhaojian Li, Ziyou Song
In this paper, we employ a data-enabled predictive control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers.
no code implementations • 8 Mar 2023 • Pengyu Chu, Zhaojian Li, Kaixiang Zhang, Dong Chen, Kyle Lammers, Renfu Lu
One key technology to fully enable efficient automated harvesting is accurate and robust apple detection, which is challenging due to complex orchard environments that involve varying lighting conditions and foliage/branch occlusions.
no code implementations • 7 Nov 2022 • Kaixiang Zhang, Yang Zheng, Chao Shang, Zhaojian Li
In this letter, we propose a simple yet effective singular value decomposition (SVD) based strategy to reduce the optimization problem dimension in data-enabled predictive control (DeePC).
no code implementations • 26 Sep 2022 • Kaixiang Zhang, Kaian Chen, Xinfan Lin, Yusheng Zheng, Xunyun Yin, Xiaosong Hu, Ziyou Song, Zhaojian Li
Fast charging of lithium-ion batteries has gained extensive research interests, but most of existing methods are either based on simple rule-based charging profiles or require explicit battery models that are non-trivial to identify accurately.
no code implementations • 22 May 2022 • Kaixiang Zhang, Kaian Chen, Zhaojian Li, Jun Chen, Yang Zheng
Data-driven predictive control of connected and automated vehicles (CAVs) has received increasing attention as it can achieve safe and optimal control without relying on explicit dynamical models.
no code implementations • 20 Dec 2021 • Kaixiang Zhang, Zhaojian Li, Yongqiang Wang, Nan Li
We show that the proposed privacy scheme does not affect the MPC performance and it preserves the privacy of the plant such that the eavesdropper is unable to identify the actual value or even estimate a rough range of the private state and input signals.
no code implementations • 20 Jun 2021 • Nan Li, Kaixiang Zhang, Zhaojian Li, Vaibhav Srivastava, Xiang Yin
In this paper, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that uses a high-fidelity nonlinear model but is subject to communication delays with a local MPC that exploits simplified dynamics (due to limited computation) but has timely feedback.