Article
Engineering, Civil
Tianqi Qie, Weida Wang, Chao Yang, Ying Li, Yuhang Zhang, Wenjie Liu, Changle Xiang
Summary: An improved model predictive control (MPC) trajectory planning method is proposed in this paper, which includes a Kalman filter fusion method for obstacle trajectory prediction and uncertainty estimation, and a tube-based MPC method for reference trajectory planning. The proposed method reduces tracking deviations and is suitable for both static and dynamic scenes, with a decrease of 46.5% in lateral deviation compared to the basic prediction method and a decrease of 77.42% in lateral tracking deviations compared to the nominal MPC method.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Robert Austin Dollar, Ardalan Vahidi
Summary: The study demonstrates that vehicle-to-vehicle connectivity combined with anticipative control can improve lane change decisions by automated vehicles. The new control method enhances energy and time efficiency in road networks, resulting in reduced energy consumption and travel time.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2021)
Article
Automation & Control Systems
Nishant Chowdhri, Laura Ferranti, Felipe Santafe Iribarren, Barys Shyrokau
Summary: This work introduces a Nonlinear Model Predictive Control (NMPC) scheme for evasive maneuvers and preventing rear-end collisions by utilizing steering and braking actions simultaneously. The controller incorporates constraints for optimal tire force and brake torque distribution, and the performance of the proposed NMPC design is compared with two classical MPC designs using simulation. Different single-lane change maneuvers were tested to evaluate controller behavior under various conditions such as lateral wind disturbances and road friction variation.
CONTROL ENGINEERING PRACTICE
(2021)
Article
Computer Science, Artificial Intelligence
Edmond Irani Liu, Matthias Althoff
Summary: In this research, a novel approach for specification-compliant motion planning for automated vehicles is proposed. By combining set-based reachability analysis with automata-based model checking, the approach efficiently verifies and generates driving corridors that comply with specifications. The applicability, effectiveness, and efficiency of the approach are demonstrated using various scenarios.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Engineering, Electrical & Electronic
Shivesh Khaitan, Qin Lin, John M. Dolan
Summary: This paper presents a unified obstacle avoidance framework that addresses uncertainty in ego-vehicle motion and prediction uncertainty of dynamic obstacles, using a two-stage traffic participant trajectory predictor to generate safe yet not overly conservative trajectories. The framework demonstrates effectiveness, safety, and real-time performance in the CARLA simulator, showcasing its potential for safe self-driving under uncertainty.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoyu Mo, Chen Lv
Summary: Recent advances in DTPI enable high-fidelity virtual representation of the physical world for intelligent prediction and decision-making in autonomous vehicles and intelligent transportation systems. This study investigates trajectory-prediction-enabled motion planning using deep neural networks and explores the impacts of historical states and future motions on planning performance.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Engineering, Electrical & Electronic
Zhiyuan Li, Pan Zhao, Chunmao Jiang, Weixin Huang, Huawei Liang
Summary: This paper presents a learning-based model predictive trajectory planning controller for automated driving in unstructured, dynamic environments with obstacle avoidance. The controller integrates trajectory planning and tracking control using model predictive control and improves model accuracy using Gaussian Process (GP) regression. The controller successfully avoids obstacles and generates collision-free and kinematically-feasible local trajectories in complex unstructured scenarios.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Chemistry, Analytical
Grega Jakus, Jaka Sodnik, Nadica Miljkovic
Summary: This study introduces an automated procedure using statistical analysis and machine learning techniques for detecting nausea during automated driving simulation based on electrogastrogram (EGG) analysis. Results show that sample entropy is a robust parameter and machine learning combined with statistical analysis can achieve efficient nausea detection.
Article
Engineering, Civil
Zhong Cao, Shaobing Xu, Huei Peng, Diange Yang, Robert Zidek
Summary: The paper proposes a confidence-aware reinforcement learning (CARL) method to improve autonomous vehicle performance by integrating RL with rule-based driving policies, intervening only in cases where the rule-based method struggles and the RL policy has high confidence. Simulation results demonstrate the superiority of this approach over pure RL policies and baseline rule-based strategies.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Haijie Guan, Boyang Wang, Jianwei Gong, Huiyan Chen
Summary: This paper aims to complete the coordinated motion planning tasks through offline driving behavior primitive (DBP) library generation, online extension and selection of DBPs. The proposed algorithm uses dynamic movement primitives and singular value decomposition to learn driving behavior patterns, and builds a DBP library by fusing driving data and vehicle model. Based on the generated DBP library and primitive association probabilities, the planning method completes the independent DBP extension of each vehicle in the conflict zone and optimally selects the primitives to be executed.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Nathan A. Spielberg, Matthew Brown, J. Christian Gerdes
Summary: NNMPC is a method that constructs a neural network model using vehicle operation data to predict vehicle dynamics and perform model predictive control in complex operating conditions. Experimental results demonstrate the capability of NNMPC to follow trajectory near the limits on high- and low-friction test courses, outperforming physics-based MPC when environmental latent state is considered.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2022)
Article
Engineering, Mechanical
Heungseok Chae, Yonghwan Jeong, Hojun Lee, Jongcherl Park, Kyongsu Yi
Summary: This article discusses the design, implementation, and evaluation of an active lane change control algorithm for autonomous vehicles, taking into account human driving behavior and safety with surrounding vehicles. By analyzing manual driving characteristics, interactions with surrounding vehicles, and safety indices based on kinematic analysis, a lane change decision and control algorithm has been developed. Stochastic predictions and motion planning considering safety indices have been conducted for desired high-speed lane changes on highways. The algorithm has been successfully implemented and evaluated through real-world driving tests, demonstrating safe and comfortable lane changes in autonomous driving.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2021)
Article
Automation & Control Systems
Dae Jung Kim, Yong Woo Jeong, Chung Choo Chung
Summary: This article proposes a lateral vehicle trajectory planning and control algorithm using a model predictive control (MPC) scheme for an automated perpendicular parking system. The proposed method addresses the issues of state-dependent planning and undesirable steering maneuvers, and it is shown to achieve smooth and stable lateral vehicle motion even in tight parking space conditions.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Chemistry, Multidisciplinary
Dong-Sung Pae, Geon-Hee Kim, Tae-Koo Kang, Myo-Taeg Lim
Summary: This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications, proposing autonomous driving path planning through integration of ODG and MPC. The proposed method provides safe control and minimizes vehicle shaking due to the tendency to respond to avoid obstacles quickly.
APPLIED SCIENCES-BASEL
(2021)
Article
Transportation Science & Technology
Zhen Yang, Yiheng Feng, Henry X. Liu
Summary: A cooperative driving framework was proposed for urban arterials, combining centralized and distributed control concepts to optimize signal timing plans and improve traffic flow. By utilizing a hierarchical model design and implementation-ready traffic control solutions, the study demonstrated both mobility and fuel economy benefits of the cooperative driving framework.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Automation & Control Systems
Subhashis Nandy
Summary: This research focuses on the design and stability analysis of nonlinear controllers for an electrically driven marine cycloidal propeller, along with estimating various parameters using the Extended Kalman Filter. The controller is defined using an efficient physics-based model and is able to accurately process multiple control signals. The robustness of the controller is assessed using Monte Carlo simulation, and its performance is evaluated through validation investigations.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Lucas C. Borin, Guilherme Hollweg, Caio R. D. Osorio, Fernanda M. Carnielutti, Ricardo C. L. F. Oliveira, Vinicius F. Montagner
Summary: This work presents a new automated test-driven design procedure for robust and optimized current controllers applied to LCL-filtered grid-tied inverters. The design of control gains is guided by high-fidelity simulations and particle swarm optimization algorithm, considering various normal and abnormal operating conditions. The proposed design ensures superior performance compared with other current control designs.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Wei He, Xiang Wang, Mohammad Masoud Namazi, Wangping Zhou, Josep M. Guerrero
Summary: The main objective of this paper is to develop a reduced-order adaptive state observer for a large class of DC-DC converters with constant power load, in order to estimate their unavailable states and unknown parameter and achieve an output feedback control scheme. The observer is designed using a generalized parameter estimation based observer technique and dynamic regressor extension and mixing method. The comparison study shows that the observer has the advantage of verifying the observability of the systems for exponential convergence without any extra excitation condition.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Te Zhang, Bo Zhu, Lei Zhang, Qingrui Zhang, Tianjiang Hu
Summary: This paper introduces a control technique called time-varying uncertainty and disturbance estimator (TV-UDE) which extends the classic UDE approach to handle more complicated issues. By combining TV-UDE with a nominal dynamic output-feedback controller, robust control for uncertain second-order attitude control systems without velocity measurements is achieved. Numerical simulations and physical experiments on a 2-DOF AERO attitude helicopter platform demonstrate the effectiveness of the proposed design in reducing steady-state errors and avoiding issues caused by high-gain estimation.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Kanishke Gamagedara, Taeyoung Lee, Murray Snyder
Summary: This paper presents the developments of flight hardware and software for a multirotor unmanned aerial vehicle capable of autonomously taking off and landing on a moving vessel in ocean environments. The flight hardware consists of a general-purpose computing module connected to a low-cost inertial measurement unit, real-time kinematics GPS, motor speed controller, and a camera through a custom-made printed circuit board. The flight software is developed in C++ with multi-threading to execute control, estimation, and communication tasks simultaneously. The proposed flight system is verified through autonomous flight experiments on a research vessel in Chesapeake Bay, utilizing real-time kinematics GPS for relative positioning and vision-based autonomous flight for shipboard launch and landing.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Yun Zhu, Kangkang Zhang, Yucai Zhu, Pengfei Jiang, Jinming Zhou
Summary: In this study, a three-term Dynamic Matrix Control (DMC) algorithm using quadratic programming is developed and compared with the traditional two-term DMC algorithm. Simulation studies and real-life tests show that the three-term DMC algorithm outperforms the two-term DMC algorithm in control effectiveness.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Jayu Kim, Taehoon Lee, Cheol-Joong Kim, Kyongsu Yi
Summary: This paper presents a data-based model predictive control method for a semi-active suspension system. The method utilizes a continuous damping controller and a stiffness controller to improve ride comfort and reduce vehicle pitch motion. Gaussian process regression is also used to compensate for model parameter uncertainties. The algorithm has been verified through computer simulations and vehicle tests, demonstrating its effectiveness and robustness.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Kunpeng Zhang, Jikang Gao, Zongqi Xu, Hui Yang, Ming Jiang, Rui Liu
Summary: A improved dynamic programming model is proposed in this paper for joint operation optimization of virtual coupling of heavy-haul trains. By simultaneously optimizing the headway and energy savings, as well as performing locomotive engineering advisory analysis, significant improvements in train performance can be achieved.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Demian Garcia-Violini, Yerai Pena-Sanchez, Nicolas Faedo, Fernando Bianchi, John V. Ringwood
Summary: This study presents a model invalidation methodology for wave energy converters (WECs) that can effectively handle dynamic uncertainty and external noise. The results indicate that neglecting dynamic uncertainty can lead to overestimation of performance, highlighting the importance of accurate dynamic description for estimating control performance.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Shengyang Lu, Yue Jiang, Xiaojun Xu, Hanxiang Qian, Weijie Zhang
Summary: This paper proposes an adaptive heading tracking control strategy based on wheelbase changes for unmanned ground vehicles (UGVs) with variable configuration. The strategy adjusts the wheelbase according to different working conditions to optimize driving performance. The impact of changing wheelbase on sideslip angle and heading angle is analyzed, and a robust-active disturbance rejection control method is developed to achieve desired front-wheel steering angle. A torque distribution method based on tire load rate and real-time load is applied to enhance longitudinal stability.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Domenico Dona, Basilio Lenzo, Paolo Boscariol, Giulio Rosati
Summary: This paper proposes a new method for designing minimum energy trajectories for servo-actuated systems and demonstrates its accuracy and effectiveness through numerical comparisons and experimental validation.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Haolin Wang, Luyao Zhang, Yao Mao, Qiliang Bao
Summary: This paper proposes a method of transforming the core element of ADRC, ESO, into a novel fuzzy self-tuning observer structure to improve the stability of LOS in the electro-optical tracking system. It effectively solves the conflict between disturbance rejection ability and noise attenuation ability in traditional ESO.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Alejandro Toro-Ossaba, Juan C. Tejada, Santiago Rua, Juan David Nunez, Alejandro Pena
Summary: This work presents the development of a myoelectric Model Reference Adaptive Controller (MRAC) with an Adaptive Kalman Filter for controlling a cable driven soft elbow exoskeleton. The proposed MRAC controller is effective in both passive and active control modes, showing good adaptability and control capabilities.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Mehrad Jaloli, Marzia Cescon
Summary: This study presents an advanced multi-agent reinforcement learning (RL) strategy for personalized glucose regulation, which is shown to improve glucose regulation and reduce the risk of severe hyperglycemia compared to traditional therapy.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Yingming Tian, Kenan Du, Jianfeng Qu, Li Feng, Yi Chai
Summary: This paper investigates the control strategy for PMSM with position sensor fault in railway. A learning observer-based control strategy is proposed, which achieves high-precision estimation of electromotive force and accelerates speed response.
CONTROL ENGINEERING PRACTICE
(2024)
