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A Harmonized Approach: Beyond-the-Limit control for Autonomous Vehicles Balancing Performance and Safety in Unpredictable Environments

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2024年第11期25卷 15827-15840页

作者： Zhao, Shiyue Zhang, Junzhi He, Xiaoxia He, Chengkun Hou, Xiaohui Huang, Heye Han, Jinheng Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Univ Wisconsin Connected & Autonomous Transportat Syst Lab Madison WI 53715 USA

This paper introduces an adaptive beyond-the-limit controller, aimed at striking a balance between high-performance maneuvers, such as transient drift, and ensuring safety in unpredictable environments. Our work is motivated by the necessity for autonomous beyond-the-limit control adaptable to real-world uncertainties, where reinforcement learning (RL) faces simulation-to-reality gap challenges in safety and performance. Our approach introduces a hybrid control mechanism that integrates data-driven performance optimization with a robust safety-centric control policy. By leveraging expert demonstrations and employing Jump-Start RL framework in Frenet coordinates, we greatly improve the learning efficiency of performance optimization. Further, an integrated safety control policy is designed to mitigate hazards through predictive trajectory planning, thus significantly reducing the risk of accidents in unforeseen situations. Meanwhile, the hybrid control mechanism employs adaptive weighting between performance and safety considerations, allowing for fusion control based on real-time environmental assessments. Through simulation experiments and initial real-vehicle testing, we validate the effectiveness of our adaptive hybrid controller. The findings confirm that our controller consistently ensures integrated safety in unpredictable environments, with an acceptable impact on performance.

关键词： Safety Transient analysis Training Task analysis Autonomous vehicles Automobiles Tires beyond-the-limit driving hybrid control RL performance and safety optimization

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Improved Adaptive DBSCAN for data Cleaning in Molten Iron Weighing Process 13

Improved Adaptive DBSCAN for Data Cleaning in Molten Iron We...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Feng, Yu Li, Xiaoli Yu, Xiaowei Wang, Kang Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9798350361674

In the intricate process of steel manufacturing, the precise measurement of molten iron is a pivotal procedure, which directly impacts the quality of steel production. Rail weighbridges are commonly deployed in the steel industry for molten iron measurement. Therefore, the accuracy of rail weighbridges weighing data is extremely important. However, due to process intricacies and equipment nuances, weighbridge weight data often exhibits outliers. This problem can potentially hinder data-centric modeling and predictive tasks. To solve this problem, this paper introduces a data cleaning methodology based on an Improved Adaptive Density-Based Spatial Clustering of Applications with Noise (IA-DBSCAN). The proposed data cleaning method is rooted in an improved adaptive DBSCAN approach, which is particularly effective in identifying clusters of arbitrary shapes and can differentiate noise from actual clusters. The application of the IA-DBSCAN algorithm facilitates the identification and cleaning of concentrated outliers. The algorithm dynamically seeks eps and MinPts parameters to achieve optimal clustering outcomes. Constraints have been incorporated into the improved algorithm, enabling it to achieve data cleaning more rapidly and accurately. After data cleaning, based on the characteristics of the selected dataset in this paper, data imputation using the linear regression method can align with the original data characteristics. Experimental results indicate that the proposed method can effectively identify and clean outliers data in the dataset, supplement missing values based on the original data characteristics, and construct a complete and anomaly-free dataset, establish a robust foundation for modeling and prediction. © 2024 ieee.

关键词： Steelmaking

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learning Robust data-Based LQG controllers From Noisy data

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ieee TRANSACTIONS ON AUTOMATIC control 2024年第12期69卷 8526-8538页

作者： Liu, Wenjie Wang, Gang Sun, Jian Bullo, Francesco Chen, Jie Beijing Inst Technol Sch Automat State Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China UC Santa Barbara Mech Engn Dept Santa Barbara CA 93106 USA UC Santa Barbara Ctr Control Dynam Syst & Computat Santa Barbara CA 93106 USA Tongji Univ State Key Lab Autonomous Intelligent Unmanned Syst Shanghai 201210 Peoples R China

This article addresses the joint state estimation and control problems for unknown linear time-invariant systems subject to both process and measurement noise. The aim is to redesign the linear quadratic Gaussian (LQG) controller-based solely on data. The LQG controller comprises a linear quadratic regulator (LQR) and a steady-state Kalman observer;while the data-based LQR design problem has been previously studied, constructing the Kalman gain and the LQG controller from noisy data presents a novel challenge. In this work, a data-based formulation for computing the steady-state Kalman gain is proposed based on semidefinite programming (SDP) using some noise-free input-state-output data. To compensate for the offline noise, a relaxed SDP is proposed, upon solving which, a robust observer gain is constructed. In addition, a robust LQG controller is designed based on the observer gain and a data-based LQR gain. The proposed controller is proven to achieve robust global exponential stability for the observer and input-to-state stability for the resultant closed-loop systems under standard conditions. Finally, numerical tests are conducted to validate the proposed controllers' correctness and effectiveness.

关键词： noisy data data-driven control semidefinite program semidefinite program semidefinite program linear quadratic Gaus- sian (LQG) linear quadratic Gaus- sian (LQG) linear quadratic Gaus- sian (LQG) linear quadratic Gaus- sian (LQG) linear quadratic Gaus- sian (LQG) noisy data semidefinite program state estima- tion

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Model-driven Feature Engineering for data-driven Battery SOH Model

Model-Driven Feature Engineering for Data-Driven Battery SOH...

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27th Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Alamin, Khaled Pagliari, Daniele Jahier Chen, Yukai Macii, Enrico Vinco, Sara Poncino, Massimo Politecn Torino Dept Control & Comp Engn Turin Italy IMEC Leuven Belgium

ISBN: (纸本)9798350348606;9783981926385

Accurate State of Health (SoH) estimation is indispensable for ensuring battery system safety, reliability, and runtime monitoring. However, as instantaneous runtime measurement of SoH remains impractical when not unfeasible, appropriate models are required for its estimation. Recently, various data-driven models have been proposed, which solve various weaknesses of traditional models. However, the accuracy of data-driven models heavily depends on the quality of the training datasets, which usually contain data that are easy to measure but that are only partially or weakly related to the physical/chemical mechanisms that determine battery aging. In this study, we propose a novel feature engineering approach, which involves augmenting the original dataset with purpose-designed features that better represent the aging phenomena. Our contribution does not consist of a new machine-learning model but rather in the addition of selected features to an existing model. This methodology consistently demonstrates enhanced accuracy across various machine-learning models and battery chemistries, yielding an approximate 25% SoH estimation accuracy improvement. Our work bridges a critical gap in battery research, offering a promising strategy to significantly enhance SoH estimation by optimizing feature selection.

关键词： Battery modeling feature engineering data augmentation data-driven automotive

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data driven Approach for User-owned Renewable Energy Sources Allocation in Community Microgrid 34

Data Driven Approach for User-owned Renewable Energy Sources...

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34th Australasian Universities Power Engineering conference

作者： Alam, Md Morshed Hossain, Jahangir Bin Mofidul, Raihan Univ Technol Sydney Elect & Data Engn Sydney NSW Australia

ISBN: (纸本)9798350377958;9798350377941

Massive integration of photovoltaic (PV) and energy storage systems (ESS) in the energy system increases the utilization of renewable energy and system complexity. Optimal and centralized scheduling and management of these sources can make them more effective and acceptable in the energy community. In this regard, this paper proposes a virtual scheduling of the PV-ESS system based on a deep learning-based data-driven approach. The proposed system divides the time span into two segments based on the availability of PV generation. A deep learning model is applied to predict the day-ahead PV generation and demand during the two-time segments. An optimization algorithm is then applied to schedule the PV and ESS based on the predictive outcomes. For validating the proposed system, a community microgrid framework is considered where multiple PV and ESS systems are connected. In addition, the user-owned PV-ESS systems are controlled by the microgrid. The simulation results demonstrate the proposed system's performance in terms of reliability and sustainability.

关键词： Community microgrid Deep learning Scheduling Optimization Energy storage system

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On-Policy data-driven Linear Quadratic Regulator via Model Reference Adaptive Reinforcement learning 62

On-Policy Data-Driven Linear Quadratic Regulator via Model R...

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62nd ieee conference on Decision and control (CDC)

作者： Borghesi, Marco Bosso, Alessandro Notarstefano, Giuseppe Alma Mater Studiorum Univ Dept Elect Elect & Informat Engn Bologna Italy

ISBN: (纸本)9798350301243

In this paper, we address a data-driven linear quadratic optimal control problem in which the regulator design is performed on-policy by resorting to approaches from reinforcement learning and model reference adaptive control. In particular, a continuous-time identifier of the value function is used to generate online a reference model for the adaptive stabilizer. By introducing a suitably selected dithering signal, the resulting policy is shown to achieve asymptotic convergence to the optimal gain while the controlled plant reaches asymptotically the behavior of the optimal closed-loop system.

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data-driven Finite-Time Three-Dimensional Differential Game Guidance Law Against a Maneuvering Target With Input Constraints

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ieee TRANSACTIONS ON AEROSPACE AND ELECTRONIC systems 2024年第6期60卷 8483-8493页

作者： Liu, Hao Cheng, Ming Wang, Linbo Jiang, Yong Beihang Univ Inst Artificial Intelligence Beijing 100191 Peoples R China Beihang Univ Sch Astronaut Beijing 100191 Peoples R China Beijing Inst Elect Syst Engn Beijing 100854 Peoples R China Changfeng Electromech Technol Design Acad Beijing 100854 Peoples R China

This article addresses the problem of finite-time interception against a high-speed maneuvering target formulated as a two-player zero-sum differential game framework with input constraints. The interceptor seeks to minimize the performance index and the maneuvering target seeks to maximize it. A nonquadratic value function is constructed to incorporate the control constraints into the differential game problem for both sides of the players. A finite-time convergent algorithm is proposed to solve the optimal value function of the differential game from the nonlinear Hamilton-Jacobi-Isaacs equation within finite time. The optimal game strategies are consequently learned using collected data of the engagement states based on the reinforcement learning. Uniform and global finite-time convergent property is confirmed via Lyapunov analysis with the obtained Nash strategies. Simulation results of an interceptor against a high-speed maneuvering target are provided to show the efficacy of the proposed method.

关键词： Games Differential games Aerospace and electronic systems Navigation Mathematical models Vectors Transmission line matrix methods Differential game input constraint optimal guidance reinforcement learning (RL) nonlinear system

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learning for CasADi data-driven Models in Numerical Optimization 6

Learning for CasADi Data-driven Models in Numerical Optimiza...

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6th Annual learning for Dynamics and control conference

作者： Salzmann, Tim Arrizabalaga, Jon Andersson, Joel Pavone, Marco Ryll, Markus Tech Univ Munich Munich Germany Stanford Univ Stanford CA USA Tech Univ Munich Munich Inst Robot & Machine Intelligence Munich Germany

While real-world problems are often challenging to analyze analytically, deep learning excels in modeling complex processes from data. Existing optimization frameworks like CasADi facilitate seamless usage of solvers but face challenges when integrating learned process models into numerical optimizations. To address this gap, we present the learning for CasADi (L4CasADi) framework, enabling the seamless integration of PyTorch-learned models with CasADi for efficient and potentially hardware-accelerated numerical optimization. The applicability of L4CasADi is demonstrated with two tutorial examples: First, we optimize a fish's trajectory in a turbulent river for energy efficiency where the turbulent flow is represented by a PyTorch model. Second, we demonstrate how an implicit Neural Radiance Field environment representation can be easily leveraged for optimal control with L4CasADi. L4CasADi, along with examples and documentation, is available under MIT license at https://***/Tim-Salzmann/l4casadi

关键词： optimization machine learning control systems data-driven control

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Iterative learning control With data-driven-Based Compensation

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ieee TRANSACTIONS ON CYBERNETICS 2022年第8期52卷 7492-7503页

作者： He, Shaoying Chen, Wenbo Li, Dewei Xi, Yugeng Xu, Yunwen Zheng, Pengyuan Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Shanghai Univ Elect Power Coll Automat Engn Shanghai 200090 Peoples R China

The robust iterative learning control (RILC) can deal with the systems with unknown time-varying uncertainty to track a repeated reference signal. However, the existing robust designs consider all the possibilities of uncertainty, which makes the design conservative and causes the controlled process converging to the reference trajectory slowly. To eliminate this weakness, a data-driven method is proposed. The new design intends to employ more information from the past input-output data to compensate for the robust control law and then to improve performance. The proposed control law is proved to guarantee convergence and accelerate the convergence rate. Ultimately, the experiments on a robot manipulator have been conducted to verify the good convergence of the trajectory errors under the control of the proposed method.

关键词： Convergence Uncertainty Trajectory Iterative learning control Manipulators Task analysis data models data-driven method iterative learning control (ILC) robot manipulator robust

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Combined ILC and DOB for Repetitive control with Application to Fast Tool Servo System 20

Combined ILC and DOB for Repetitive Control with Application...

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20th ieee/ASME International conference on Mechatronic and Embedded systems and Applications (MESA)

作者： Jing, Yajie Hou, Beibei Lin, Pengbo Lu, Shuaishuai Yan, Peng Qilu Univ Technol Sch Mech Engn Shandong Acad Sci Jinan Peoples R China Shandong Inst Mech Design & Res Jinan Peoples R China Shandong Univ Sch Mech Engn Key Lab High Efficiency & Clean Mech Manufacture Minist Educ Jinan Peoples R China

ISBN: (纸本)9798331516246;9798331516239

Fast tool servo (FTS) systems using piezoelectric technology show great promise for ultra-precise machining due to their high precision and responsiveness. However, during machining, these systems often struggle with tracking accuracy due to factors like system complexity and external disturbances. To address this, we propose a robust control method specifically for piezo-driven FTS. Our approach aims to improve tracking accuracy and disturbance rejection during machining. We use a robust repetitive control scheme with a disturbance observer (DOB) to minimize the effect of interference on tracking. However, in practice, solving the inverse model of the controlled object can introduce uncertainty. To enhance the disturbance observer's performance, we add iterative learning control (ILC). This helps refine control actions based on past errors, improving overall system performance.

关键词： FTS DOB iterative learning control

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