Article
Automation & Control Systems
Peng Li, Di Liu, Simone Baldi
Summary: This paper proposes a variant of AISMC with reduced a priori knowledge of the system uncertainty, utilizing a structure-independent parametrization. The method is analyzed in the Lyapunov stability framework and validated in systems with different structures.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Automation & Control Systems
Peng Li, Di Liu, Simone Baldi
Summary: This study introduces a variant of AISMC with reduced a priori knowledge of system uncertainty, utilizing a structure-independent parametrization based on Euler-Lagrange dynamics. The method is analyzed in the Lyapunov stability framework and validated in systems with different structures.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Engineering, Mechanical
Xin Ma, Jian Xu, Hongbin Fang, Yang Lv, Xiaoxu Zhang
Summary: This paper proposes a new gait coordination-oriented adaptive neural sliding mode control (GC-ANSMC) for lower limb amputees using prostheses, addressing the difficulties in adapting to complex tasks. The approach combines a homotopy algorithm for trajectory generation with a radial basis function neural network for modeling uncertainties, achieving fast motion tracking and global convergence. Applications show that GC-ANSMC outperforms traditional methods in control accuracy, convergence speed, torque control, and gait coordination performance, demonstrating promising potential for adaptive control in nonlinear human-prosthesis dynamics.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2022)
Article
Engineering, Civil
Jaswandi Sawant, Uttam Chaskar, Divyesh Ginoya
Summary: This paper proposes a sliding mode control approach for the CACC system, which improves system performance and stability by estimating uncertainties and disturbances.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Yanxin Nie, Minglu Zhang, Xiaojun Zhang
Summary: A coordinated control strategy for intelligent electric vehicle trajectory tracking and stability is proposed based on hierarchical control theory. The strategy includes an Adaptive Spiral Sliding Mode controller to reduce deviation in trajectory tracking and a tire force optimal distribution method for directional control. Simulation experiments validate the effectiveness of the control strategy in controlling vehicle trajectory deviation while ensuring lateral stability.
APPLIED SCIENCES-BASEL
(2021)
Article
Mathematics, Applied
Yingxin Shou, Bin Xu, Yuyan Guo, Mouhua Sang, Rui Hong
Summary: The paper introduces a coordinated adaptive control for hypersonic reentry vehicles, which ensures flight safety and stable flight attitude through coordinated design, channel moment distribution coordination, and yawing moment compensation. It constructs a learning performance index based on online recorded data to obtain high-precision uncertain approximations and ensures the uniformly ultimately boundedness of system signals using the Lyapunov approach. Simulation results demonstrate that the proposed approach can maintain a stable attitude and adapt to system uncertainty.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2022)
Review
Engineering, Aerospace
Benke Gao, Yan-Jun Liu, Lei Liu
Summary: An adaptive fault-tolerant control method integrated with fast terminal sliding mode control technology and neural network is proposed for the attitude system of a quadrotor unmanned aerial vehicle. The neural network is used to approximate the uncertain terms in the system. Adaptive laws are developed to estimate the unknown fault coefficient and predict the unknown upper bound of the total disturbance. A fast terminal sliding mode control scheme is developed for the attitude system to improve the convergence rate. The effectiveness and superiority of the proposed control method are verified through contrast tests.
AEROSPACE SCIENCE AND TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Gang Chen, Yichen Jiang, Keyi Guo, Liangmo Wang
Summary: This paper proposes a speed tracking method for unmanned driving robot vehicles by adjusting the sliding mode gains using a fuzzy adaptive system. The effectiveness and stability of the proposed method is demonstrated through model establishment and control law derivation.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Automation & Control Systems
Bin Xu, Xia Wang, Zhongke Shi
Summary: This paper investigates the robust adaptive neural control of nonminimum phase hypersonic flight vehicle using composite learning. The output redefinition and transformation of attitude subsystem into internal and input-output subsystem are employed to overcome nonminimum phase behavior. The use of composite learning for updating neural weights ensures stability and boundedness of tracking errors in the closed-loop system.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Guang Yang, Faxing Lu, Junfei Xu
Summary: This paper proposes a control scheme for the lateral motion control of a supercavitating vehicle, taking into account its unique hydrodynamic characteristics and time-delay effects. The control scheme, based on neural networks and adaptive sliding control, aims to control the longitudinal stability and lateral motion of the vehicle in the presence of external disturbances. The proposed scheme includes online estimation of nonlinear disturbances, adaptive adjustment of neural network weights and control parameters, and real-time solution of actuator control efforts. Rigorous theoretical proofs based on the Lyapunov theory demonstrate the globally asymptotic stability of the proposed controller. Numerical simulations validate the effectiveness and robustness of the proposed control scheme.
Article
Computer Science, Information Systems
Ngoc Phi Nguyen, Nguyen Xuan Mung, Ha Le Nhu Ngoc Thanh, Tuan Tu Huynh, Ngoc Tam Lam, Sung Kyung Hong
Summary: In this article, a sliding mode control based on neural networks is proposed for attitude and altitude system of quadcopter under external disturbances. By integrating sliding mode controllers with neural network algorithm and combining disturbance observer, the suggested control method shows better tracking performance and disturbance rejection in numerical simulations, indicating an improved stability of the quadcopter system.
Article
Engineering, Electrical & Electronic
Nguyen Tien Dat, Cao Van Kien, Ho Pham Huy Anh
Summary: This paper proposes an advanced adaptive neural sliding mode controller (ANSMC) for accurate and robust speed control of PMSM driving system. The control technique based on field oriented control (FOC) scheme demonstrates competitive performance compared with standard SMC and FOC-PI control methods. The PMSM speed control using the proposed approach shows superiority over other advanced control techniques.
ELECTRICAL ENGINEERING
(2023)
Article
Automation & Control Systems
Ganghui Shen, Yuanqing Xia, Jinhui Zhang, Bing Cui
Summary: This paper presents a new continuous adaptive super-twisting sliding mode control (ASTSMC) method for the altitude trajectory tracking control problem of reentry vehicle subject to bounded uncertainty. The proposed method, based on conventional super-twisting sliding mode control (STSMC) and adaptive gain technique, improves tracking accuracy and achieves high control performance. By employing fast power rate reaching law and modified fast nonsingular terminal sliding mode (FNTSM) surface, the designed controller achieves faster convergence and stronger robustness than conventional STSMC methods. The finite-time stability of the closed-loop system is proved through Lyapunov theory, and simulation results validate the superiority of the proposed controller.
Article
Computer Science, Information Systems
Xingjian Sun, Lei Zhang, Juping Gu
Summary: In this study, the adaptive sliding mode control (ASMC) strategy is investigated for complex nonlinear systems with matched and unknown nonlinearities and external disturbances. A Gaussian radial basic neural network is used to approximate the nonlinearities and external disturbances. A Takagi-Sugeno (T-S) fuzzy model based integral switching function is introduced to solve the ASMC problem and eliminate the constraint on input gains. The switching control term is represented as a proportional integral (PI) control format to reduce chattering phenomenon and the Lyapunov theory is used to guarantee the stability of the control systems. An experimental simulation is conducted to verify the effectiveness of the proposed sliding mode control (SMC) strategy.
INFORMATION SCIENCES
(2023)
Article
Mathematics, Applied
Salman Ijaz, Chen Fuyang, Mirza Tariq Hamayun, Haris Anwaar
Summary: This paper investigates a nonlinear control approach for high maneuvering fighter aircraft to maintain acceptable flight quality at high angles of attack. Adaptive control strategies are designed for two loops using real-time flight data to estimate aerodynamics coefficients.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Engineering, Civil
Jinheng Han, Junzhi Zhang, Chengkun He, Chen Lv, Chao Li, Yuan Ji, Xiaohui Hou
Summary: In this paper, a novel prescribed time performance recovery fault tolerant control method is proposed to ensure nominal platoon performance under multiple faults. A novel barrier function based prescribed time sliding mode controller is devised to handle platoon consensus errors and convergence time within prescribed constraints under normal conditions. In the presence of leader-follower link faults, a distributed recursive estimator is proposed to estimate the leader's states and recover the previous leader-follower platooning control protocol in a prescribed time. The effectiveness and superiority of the proposed performance recovery fault tolerant control algorithms are validated through numerical simulations and hardware-in-loop experiments.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Jinheng Han, Junzhi Zhang, Chengkun He, Chen Lv, Xiaohui Hou, Yuan Ji
Summary: In this article, a systemic anti-fault safety consensus strategy is proposed to address the problems in platoon and improve its functional safety. The strategy includes fault detection and fault-tolerant control tasks, which detect sensor faults using a distributed finite-time observer and assess the precise fault extent using an adaptive finite-time fault parameter estimation law. An integral discounted cost function is constructed to improve the estimation performance of the adaptive fault parameter estimation law. The effectiveness and feasibility of the strategy are verified through real-time simulations.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Mechanical
Hao Chen, Shanhe Lou, Chen Lv
Summary: This paper proposes a hybrid physics-data-driven framework to improve system modelling by integrating a physical model and an online learning data model. Two hybrid representations and a H infinity-based learning algorithm are introduced. The proposed hybrid method shows better generalization ability and robustness in practical implementations compared to other baseline methods, as demonstrated through simulations and experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Xiaoyu Mo, Jinxiang Wang, Liang Cheng, Tiancheng Ouyang
Summary: Cavitation erosion in gear transmissions, closely related to the damage on tooth surface, is currently under-studied. A computational fluid dynamics model is proposed in this study to predict tooth erosion by combining gear dynamics and two-phase flow simulations. The model considers vibration and cavitation, with gear displacement from a finite element model used as a boundary condition and moving mesh technology adapting the deformed fluid domain. The method is validated by experiments, and twelve different CERIs are used to estimate erosion on meshing surface, with the three most reasonable indicators identified.
TRIBOLOGY INTERNATIONAL
(2023)
Letter
Automation & Control Systems
Xiangkun He, Chen Lv
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Robotics
Xiaoyu Mo, Yang Xing, Haochen Liu, Chen Lv
Summary: Predicting the future motions of neighboring agents is crucial for autonomous vehicles to navigate complex scenarios. Our proposed map-adaptive predictor can predict a variable number of future trajectories based on the number of lanes with candidate centerlines (CCLs). It incorporates three types of predictions, including single CCL-guided future motions, scene-reasoning prediction, and motion-maintaining prediction, through a single graph operation. By utilizing a hierarchical graph representation of the driving scene, our method achieves map-adaptive prediction and outperforms strong baselines in experiments on real-world driving datasets.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Ziran Wang, Chen Lv, Fei-Yue Wang
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Long Chen, Yuchen Li, Chao Huang, Bai Li, Yang Xing, Daxin Tian, Li Li, Zhongxu Hu, Xiaoxiang Na, Zixuan Li, Siyu Teng, Chen Lv, Jinjun Wang, Dongpu Cao, Nanning Zheng, Fei-Yue Wang
Summary: Interest in autonomous driving and intelligent vehicles is growing rapidly due to their convenience, safety, and economic benefits. However, existing surveys are limited in scope and lack systematic summaries and future research directions.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Can Xu, Wanzhong Zhao, Chunyan Wang, Taowen Cui, Chen Lv
Summary: This paper proposes an integrated model and learning combined (IMLC) algorithm to achieve human-like driving for autonomous vehicles. The algorithm includes integrated driving behavior modeling and characteristic learning. The algorithm is validated using highD dataset, and the results show that it has great advantages in position and velocity accuracy.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Hao Chen, Chen Lv
Summary: High-performance path tracking is crucial for autonomous vehicles, and using feedforward-feedback control architectures with adequate margins of stability is suitable for accurate path tracking. Learning-based methods have been proven to be promising for system modelling, but offline-learned data models trained with collection data are limited in their feature space, leading to insufficient generalization. In this study, an online learning network called the recurrent high-order neural network (RHONN) is introduced to effectively characterize vehicle behaviors in a timely and flexible manner.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Transportation Science & Technology
Xiangkun He, Chen Lv
Summary: This article presents a novel constrained multi-objective reinforcement learning technique for personalized decision making in autonomous driving. By introducing a non-linear constraint and a vectorized action-value function, this method is able to learn decision behaviors that align efficiently between user preferences and optimal policies.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Engineering, Civil
Lie Yang, Haohan Yang, Bin-Bin Hu, Yan Wang, Chen Lv
Summary: Accurately identifying driver's emotions is crucial for improving the safety and comfort in intelligent driving system, but individual differences and illumination changes pose challenges to emotion recognition. In this paper, a robust driver emotion recognition method based on feature separation is proposed, which can overcome the interference of individual differences and illumination changes. Experimental results on multiple datasets demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Proceedings Paper
Automation & Control Systems
Yiran Zhang, Zhongxu Hu, Chen Lv
Summary: In highly automated driving vehicles, a human-vehicle interface is still necessary for individualization and emergency intervention. A tactical human-vehicle collaboration framework is proposed, utilizing hand-landmark extraction algorithm and augmented reality visual feedback. Through a vision-based interface, the driver's gesture is projected onto the ground and fed back to the driver through an AR-HUD interface, functioning as a strategic decision or planning suggestion to the vehicle.
2023 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS, AIM
(2023)
