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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications 被引量:4
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作者 Ding Wang Ning Gao +2 位作者 Derong Liu Jinna Li Frank L.Lewis 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第1期18-36,共19页
Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and ... Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence. 展开更多
关键词 Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)
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Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies 被引量:4
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作者 Xiaohua Ge Qing-Long Han +1 位作者 Xian-Ming Zhang Derui Ding 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第2期362-376,共15页
Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical cha... Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results. 展开更多
关键词 Automated vehicles constant time headway spacing constant spacing cooperative adaptive cruise control event-triggered communication vehicle platooning
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Command filtered integrated estimation guidance and control for strapdown missiles with circular field of view
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作者 Wei Wang Jiaqi Liu +2 位作者 Shiyao Lin Baokui Geng Zhongjiao Shi 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第5期211-221,共11页
In this paper,an integrated estimation guidance and control(IEGC)system is designed based on the command filtered backstepping approach for circular field-of-view(FOV)strapdown missiles.The threedimensional integrated... In this paper,an integrated estimation guidance and control(IEGC)system is designed based on the command filtered backstepping approach for circular field-of-view(FOV)strapdown missiles.The threedimensional integrated estimation guidance and control nonlinear model with limited actuator deflection angle is established considering the seeker's FOV constraint.The boundary time-varying integral barrier Lyapunov function(IBLF)is employed in backstepping design to constrain the body line-of-sight(BLOS)in IEGC system to fit a circular FOV.Then,the nonlinear adaptive controller is designed to estimate the changing aerodynamic parameters.The generalized extended state observer(GESO)is designed to estimate the acceleration of the maneuvering targets and the unmatched time-varying disturbances for improving tracking accuracy.Furthermore,the command filters are used to solve the"differential expansion"problem during the backstepping design.The Lyapunov theory is used to prove the stability of the overall closed-loop IEGC system.Finally,the simulation results validate the integrated system's effectiveness,achieving high accuracy strikes against maneuvering targets. 展开更多
关键词 Integrated estimation guidance and control Circular field-of-view Time-varying integral barrier Lyapunov function Command filtered backstepping control Nonlinear adaptive control Extended state observer
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Research on Anti-Fluctuation Control of Winding Tension System Based on Feedforward Compensation
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作者 Yujie Duan Jianguo Liang +4 位作者 Jianglin Liu Haifeng Gao Yinhui Li Jinzhu Zhang Xinyu Wen 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1239-1261,共23页
In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tens... In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tension output,this paper proposed a tension fluctuation rejection strategy based on feedforward compensation.In addition to the bias harmonic curve of the unknown state,the tension fluctuation also contains the influence of bounded noise.A tension fluctuation observer(TFO)is designed to cancel the uncertain periodic signal,in which the frequency generator is used to estimate the critical parameter information.Then,the fluctuation signal is reconstructed by a third-order auxiliary filter.The estimated signal feedforward compensates for the actual tension fluctuation.Furthermore,a time-varying parameters fractional-order PID controller(TPFOPID)is realized to attenuate the bounded noise in the fluctuation.Finally,TPFOPID is enhanced by TFO and applied to control a tension control system considering multi-source disturbances.The stability of the method is analyzed by using the Lyapunov theorem.Finally,numerical simulations verify that the proposed scheme improves the tracking ability and robustness of the system in response to tension fluctuations. 展开更多
关键词 Constant tension control anti-fluctuation strategy tension fluctuation observer time-varying parameters fractional-order PID controller feedforward compensate
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Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment
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作者 Aljuaid Turkea Ayedh M Ainuddin Wahid Abdul Wahab Mohd Yamani Idna Idris 《Computers, Materials & Continua》 SCIE EI 2024年第9期4663-4686,共24页
Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control sy... Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management. 展开更多
关键词 BYOD security access control access control decision-enforcement deep learning neural network techniques TabularDNN MULTILAYER dynamic adaptable FLEXIBILITY bottlenecks performance policy conflict
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A Lightweight, Searchable, and Controllable EMR Sharing Scheme
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作者 Xiaohui Yang Peiyin Zhao 《Computers, Materials & Continua》 SCIE EI 2024年第4期1521-1538,共18页
Electronic medical records (EMR) facilitate the sharing of medical data, but existing sharing schemes suffer fromprivacy leakage and inefficiency. This article proposes a lightweight, searchable, and controllable EMR ... Electronic medical records (EMR) facilitate the sharing of medical data, but existing sharing schemes suffer fromprivacy leakage and inefficiency. This article proposes a lightweight, searchable, and controllable EMR sharingscheme, which employs a large attribute domain and a linear secret sharing structure (LSSS), the computationaloverhead of encryption and decryption reaches a lightweight constant level, and supports keyword search andpolicy hiding, which improves the high efficiency of medical data sharing. The dynamic accumulator technologyis utilized to enable data owners to flexibly authorize or revoke the access rights of data visitors to the datato achieve controllability of the data. Meanwhile, the data is re-encrypted by Intel Software Guard Extensions(SGX) technology to realize resistance to offline dictionary guessing attacks. In addition, blockchain technology isutilized to achieve credible accountability for abnormal behaviors in the sharing process. The experiments reflectthe obvious advantages of the scheme in terms of encryption and decryption computation overhead and storageoverhead, and theoretically prove the security and controllability in the sharing process, providing a feasible solutionfor the safe and efficient sharing of EMR. 展开更多
关键词 LIGHTWEIGHT keyword search large attribute domain controlLABILITY blockchain
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Bifurcation analysis and control study of improved full-speed differential model in connected vehicle environment
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作者 艾文欢 雷正清 +2 位作者 李丹洋 方栋梁 刘大为 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第7期245-266,共22页
In recent years, the traffic congestion problem has become more and more serious, and the research on traffic system control has become a new hot spot. Studying the bifurcation characteristics of traffic flow systems ... In recent years, the traffic congestion problem has become more and more serious, and the research on traffic system control has become a new hot spot. Studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable pivots can alleviate the traffic congestion problem from a new perspective. In this work, the full-speed differential model considering the vehicle network environment is improved in order to adjust the traffic flow from the perspective of bifurcation control, the existence conditions of Hopf bifurcation and saddle-node bifurcation in the model are proved theoretically, and the stability mutation point for the stability of the transportation system is found. For the unstable bifurcation point, a nonlinear system feedback controller is designed by using Chebyshev polynomial approximation and stochastic feedback control method. The advancement, postponement, and elimination of Hopf bifurcation are achieved without changing the system equilibrium point, and the mutation behavior of the transportation system is controlled so as to alleviate the traffic congestion. The changes in the stability of complex traffic systems are explained through the bifurcation analysis, which can better capture the characteristics of the traffic flow. By adjusting the control parameters in the feedback controllers, the influence of the boundary conditions on the stability of the traffic system is adequately described, and the effects of the unstable focuses and saddle points on the system are suppressed to slow down the traffic flow. In addition, the unstable bifurcation points can be eliminated and the Hopf bifurcation can be controlled to advance, delay, and disappear,so as to realize the control of the stability behavior of the traffic system, which can help to alleviate the traffic congestion and describe the actual traffic phenomena as well. 展开更多
关键词 bifurcation analysis vehicle queuing bifurcation control Hopf bifurcation
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Study of the Constraints of Millet Production (Pennisetum glaucum (L.) R. Br.) and the Peasant Perception of Biological Control in the Tahoua Region
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作者 Rabé Mahamane Moctar Hama Oumarou +3 位作者 Issaka Rabo Salissou Abdoulaye Amoustapha Soumaila Bakoye Nouhou Ousmane Baoua Ibrahim 《Agricultural Sciences》 2024年第1期1-14,共14页
Millet (Pennisetum glaucum (L.) R. Br.) is the Sahelian crop par excellence due to its adaptation to the particular production conditions in this region. Unfortunately, in recent years this crop has been threatened by... Millet (Pennisetum glaucum (L.) R. Br.) is the Sahelian crop par excellence due to its adaptation to the particular production conditions in this region. Unfortunately, in recent years this crop has been threatened by very strong parasitic pressure and drought during the production period. The objective of this study is to analyze the main constraints of millet production and the solutions known to producers. A survey was carried out in November 2022 with a sample of 298 producers in five municipalities in the Tahoua region. The main constraints are drought and pressure from crop pests (locust, millet ear miner, floricultural insects) according to 57.9% of respondents. The millet ear miner is the most formidable pest according to 55% of respondents. Thus, the average yield obtained in a year of good production without the leafminer is 194 kg/ha and that obtained in a year of millet ear leafminer is around 27 kg to 43 kg/ha depending on the municipality. The yield obtained this last campaign after the attack of this leafminer varies from 64 to 77 kg/ha depending on the municipalities compared to a potential yield of over 1000 kg/ha. More than half of producers (58.1%) are unaware of the existence of biological control compared to only 12.5% who are aware of this alternative method. Work to popularize this technology is necessary in the five municipalities and the entire region in general. 展开更多
关键词 Biological control Ear Miner MILLET
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Review on uncertainty analysis and information fusion diagnosis of aircraft control system
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作者 ZHOU Keyi LU Ningyun +1 位作者 JIANG Bin MENG Xianfeng 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第5期1245-1263,共19页
In the aircraft control system,sensor networks are used to sample the attitude and environmental data.As a result of the external and internal factors(e.g.,environmental and task complexity,inaccurate sensing and comp... In the aircraft control system,sensor networks are used to sample the attitude and environmental data.As a result of the external and internal factors(e.g.,environmental and task complexity,inaccurate sensing and complex structure),the aircraft control system contains several uncertainties,such as imprecision,incompleteness,redundancy and randomness.The information fusion technology is usually used to solve the uncertainty issue,thus improving the sampled data reliability,which can further effectively increase the performance of the fault diagnosis decision-making in the aircraft control system.In this work,we first analyze the uncertainties in the aircraft control system,and also compare different uncertainty quantitative methods.Since the information fusion can eliminate the effects of the uncertainties,it is widely used in the fault diagnosis.Thus,this paper summarizes the recent work in this aera.Furthermore,we analyze the application of information fusion methods in the fault diagnosis of the aircraft control system.Finally,this work identifies existing problems in the use of information fusion for diagnosis and outlines future trends. 展开更多
关键词 aircraft control system sensor networks information fusion fault diagnosis UNCERTAINTY
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Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking
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作者 Xiang-long Liang Zhi-kai Yao +1 位作者 Yao-wen Ge Jian-yong Yao 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第4期19-28,共10页
This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a larg... This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach. 展开更多
关键词 Adaptive control Reinforcement learning Uncertain mechanical systems Asymptotic tracking
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Deep Reinforcement Learning Based Joint Cooperation Clustering and Downlink Power Control for Cell-Free Massive MIMO
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作者 Du Mingjun Sun Xinghua +2 位作者 Zhang Yue Wang Junyuan Liu Pei 《China Communications》 SCIE CSCD 2024年第11期1-14,共14页
In recent times,various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multipleinput multiple-output(CF-mMIMO)networks.With the emergence of deep reinfo... In recent times,various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multipleinput multiple-output(CF-mMIMO)networks.With the emergence of deep reinforcement learning(DRL),significant progress has been made in the field of network optimization as DRL holds great promise for improving network performance and efficiency.In this work,our focus delves into the intricate challenge of joint cooperation clustering and downlink power control within CF-mMIMO networks.Leveraging the potent deep deterministic policy gradient(DDPG)algorithm,our objective is to maximize the proportional fairness(PF)for user rates,thereby aiming to achieve optimal network performance and resource utilization.Moreover,we harness the concept of“divide and conquer”strategy,introducing two innovative methods termed alternating DDPG(A-DDPG)and hierarchical DDPG(H-DDPG).These approaches aim to decompose the intricate joint optimization problem into more manageable sub-problems,thereby facilitating a more efficient resolution process.Our findings unequivo-cally showcase the superior efficacy of our proposed DDPG approach over the baseline schemes in both clustering and downlink power control.Furthermore,the A-DDPG and H-DDPG obtain higher performance gain than DDPG with lower computational complexity. 展开更多
关键词 cell-free massive MIMO CLUSTERING deep reinforcement learning power control
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Decentralized Optimal Control and Stabilization of Interconnected Systems With Asymmetric Information
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作者 Na Wang Xiao Liang +1 位作者 Hongdan Li Xiao Lu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第3期698-707,共10页
The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information.Compared with previous work,a closed-loop optimal solution to the control p... The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information.Compared with previous work,a closed-loop optimal solution to the control problem and sufficient and necessary conditions for the stabilization problem of the interconnected systems are given for the first time.The main challenge lies in three aspects:Firstly,the asymmetric information results in coupling between control and estimation and failure of the separation principle.Secondly,two extra unknown variables are generated by asymmetric information(different information filtration)when solving forward-backward stochastic difference equations.Thirdly,the existence of additive noise makes the study of mean-square boundedness an obstacle.The adopted technique is proving and assuming the linear form of controllers and establishing the equivalence between the two systems with and without additive noise.A dual-motor parallel drive system is presented to demonstrate the validity of the proposed algorithm. 展开更多
关键词 Asymmetric information decentralized control forwardbackward stochastic difference equations interconnected system stalibization
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Experimental Study on Wire Melting Control Ability of Twin-Body Plasma Arc
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作者 Ruiying Zhang Fan Jiang Long Xue 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第2期184-194,共11页
The twin-body plasma arc has the decoupling control ability of heat transfer and mass transfer,which is beneficial to shape and property control in wire arc additive manufacturing.In this paper,with the wire feeding s... The twin-body plasma arc has the decoupling control ability of heat transfer and mass transfer,which is beneficial to shape and property control in wire arc additive manufacturing.In this paper,with the wire feeding speed as a characteristic quantity,the wire melting control ability of twin-body plasma arc was studied by adjusting the current separation ratio(under the condition of a constant total current),the wire current/main current and the position of the wire in the arc axial direction.The results showed that under the premise that the total current remains unchanged(100 A),as the current separation ratio increased,the middle and minimum melting amounts increased approximately synchronously under the effect of anode effect power,the first melting mass range remained constant;the maximum melting amount increased twice as fast as the middle melting amount under the effect of the wire feeding speed,and the second melting mass range was expanded.When the wire current increased,the anode effect power and the plasma arc power were both factors causing the increase in the wire melting amount;however,when the main current increased,the plasma arc power was the only factor causing the increase in the wire melting amount.The average wire melting increment caused by the anode effect power was approximately 2.7 times that caused by the plasma arc power.The minimum melting amount was not affected by the wire-torch distance under any current separation ratio tested.When the current separation ratio increased and reached a threshold,the middle melting amount remained constant with increasing wire-torch distance.When the current separation ratio continued to increase and reached the next threshold,the maximum melting amount remained constant with the increasing wire-torch distance.The effect of the wire-torch distance on the wire melting amount reduced with the increase in the current separation ratio.Through this study,the decoupling mechanism and ability of this innovative arc heat source is more clearly. 展开更多
关键词 Twin-body plasma arc Melting control ability Melting amount Current separation ratio
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Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks
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作者 Yongjiang Zhao Haoyi Zhong Chang Cyoon Lim 《Computers, Materials & Continua》 SCIE EI 2024年第4期449-471,共23页
This paper examines the difficulties of managing distributed power systems,notably due to the increasing use of renewable energy sources,and focuses on voltage control challenges exacerbated by their variable nature i... This paper examines the difficulties of managing distributed power systems,notably due to the increasing use of renewable energy sources,and focuses on voltage control challenges exacerbated by their variable nature in modern power grids.To tackle the unique challenges of voltage control in distributed renewable energy networks,researchers are increasingly turning towards multi-agent reinforcement learning(MARL).However,MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase.This unpredictability can lead to unsafe control measures.To mitigate these safety concerns in MARL-based voltage control,our study introduces a novel approach:Safety-ConstrainedMulti-Agent Reinforcement Learning(SC-MARL).This approach incorporates a specialized safety constraint module specifically designed for voltage control within the MARL framework.This module ensures that the MARL agents carry out voltage control actions safely.The experiments demonstrate that,in the 33-buses,141-buses,and 322-buses power systems,employing SC-MARL for voltage control resulted in a reduction of the Voltage Out of Control Rate(%V.out)from0.43,0.24,and 2.95 to 0,0.01,and 0.03,respectively.Additionally,the Reactive Power Loss(Q loss)decreased from 0.095,0.547,and 0.017 to 0.062,0.452,and 0.016 in the corresponding systems. 展开更多
关键词 Power quality control multi-agent reinforcement learning safety-constrained MARL
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A grouping strategy for reinforcement learning-based collective yawcontrol of wind farms
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作者 Chao Li Luoqin Liu Xiyun Lu 《Theoretical & Applied Mechanics Letters》 CAS CSCD 2024年第1期1-5,共5页
Reinforcement learning(RL)algorithms are expected to become the next generation of wind farm control methods.However,as wind farms continue to grow in size,the computational complexity of collective wind farm control ... Reinforcement learning(RL)algorithms are expected to become the next generation of wind farm control methods.However,as wind farms continue to grow in size,the computational complexity of collective wind farm control will exponentially increase with the growth of action and state spaces,limiting its potential in practical applications.In this Letter,we employ a RL-based wind farm control approach with multi-agent deep deterministic policy gradient to optimize the yaw manoeuvre of grouped wind turbines in wind farms.To reduce the computational complexity,the turbines in the wind farm are grouped according to the strength of the wake interaction.Meanwhile,to improve the control efficiency,each subgroup is treated as a whole and controlled by a single agent.Optimized results show that the proposed method can not only increase the power production of the wind farm but also significantly improve the control efficiency. 展开更多
关键词 Reinforcement learning Wake steering Wind-farm flow control Production maximization
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Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm
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作者 Bin Tang Zhengyi Yang +3 位作者 Haobin Jiang Ziyan Lin Zhanxiang Xu Zitian Hu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第1期114-129,共16页
Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase... Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase and higher mass center.To improve the performance mentioned above comprehensively,the control strategy based on improved artificial potential field(APF)algorithm is proposed.In the paper,time to lane crossing(TLC)is introduced into the potential field function to enhance the accuracy of path tracking,meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source.The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory.In addition,adaptive inertial weight particle swarm optimization algorithm(AIWPSO)is applied to optimize the gain of each potential field function.The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably.Finally,the proposed control strategy is verified by the HiL test.It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving. 展开更多
关键词 Lane keeping control Commercial vehicles Lateral stability Artificial potential field AIWPSO
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Adaptive Nonlinear PD Controller of Two-Wheeled Self-Balancing Robot with External Force
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作者 Van-Truong Nguyen Dai-Nhan Duong +3 位作者 Dinh-Hieu Phan Thanh-Lam Bui Xiem HoangVan Phan Xuan Tan 《Computers, Materials & Continua》 SCIE EI 2024年第11期2337-2356,共20页
This paper proposes an adaptive nonlinear proportional-derivative(ANPD)controller for a two-wheeled self-balancing robot(TWSB)modeled by the Lagrange equation with external forces.The proposed control scheme is design... This paper proposes an adaptive nonlinear proportional-derivative(ANPD)controller for a two-wheeled self-balancing robot(TWSB)modeled by the Lagrange equation with external forces.The proposed control scheme is designed based on the combination of a nonlinear proportional-derivative(NPD)controller and a genetic algorithm,in which the proportional-derivative(PD)parameters are updated online based on the tracking error and the preset error threshold.In addition,the genetic algorithm is employed to adaptively select initial controller parameters,contributing to system stability and improved control accuracy.The proposed controller is basic in design yet simple to implement.The ANPD controller has the advantage of being computationally lightweight and providing high robustness against external forces.The stability of the closed-loop system is rigorously analyzed and verified using Lyapunov theory,providing theoretical assurance of its robustness.Simulations and experimental results show that the TWSB robot with the proposed ANPD controller achieves quick balance and tracks target values with very small errors,demonstrating the effectiveness and performance of the proposed controller.The proposed ANPD controller demonstrates significant improvements in balancing and tracking performance for two-wheeled self-balancing robots,which has great applicability in the field of robot control systems.This represents a promising solution for applications requiring precise and stable motion control under varying external conditions. 展开更多
关键词 Two-wheeled self-balancing robot nonlinear PD control external force genetic algorithm
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Impact of Migrant Populations on Tuberculosis Rates in Saudi Arabia: Assessing How Migration Patterns Affect TB Incidence and Control Measures: A Narrative Review
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作者 Neda Ali Al Bati 《Journal of Tuberculosis Research》 2024年第3期165-181,共17页
This research focuses on the effects of migration on the TB infection rate and its prevention in Saudi Arabia, which has a large number of expatriates from TB-affected countries. Despite, based on the current global s... This research focuses on the effects of migration on the TB infection rate and its prevention in Saudi Arabia, which has a large number of expatriates from TB-affected countries. Despite, based on the current global statistics of TB occurrence, it is evident that the national incidence of TB has reduced from 10.55 per 100,000 in 2015 to 8.36 per 100,000 in 2019;despite this, there are still some difficulties because migrants bring new strains of Mycobacterium tuberculosis. Hindrances, including language barriers and perceived immigration status, hinder patients from seeking medical attention or doctors from diagnosing diseases. Each patient and each cultural group need special attention to public health, enhancing living circumstances, and health care support. Community participation, inclusion of TB control programs into functional healthcare facilities, and the functioning of TB programs need to be stressed to address TB issues. Considering the focus on social, economic, and cultural approaches, the country can make severe advancements in TB control and population protection. This holistic analysis is critical for a long-term effective strategy to combat TB in the Kingdom. 展开更多
关键词 Kingdom Saudi Arabia (KSA) Tuberculosis (TB) Prevalence MIGRANTS control Measures
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Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach
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作者 Luigi D’Alfonso Francesco Giannini +3 位作者 Giuseppe Franzè Giuseppe Fedele Francesco Pupo Giancarlo Fortino 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第1期141-156,共16页
In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory... In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors. 展开更多
关键词 Distributed model predictive control distributed reinforcement learning routing decisions urban road networks
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Hierarchical Controller Synthesis Under Linear Temporal Logic Specifications Using Dynamic Quantization
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作者 Wei Ren Zhuo-Rui Pan +1 位作者 Weiguo Xia Xi-Ming Sun 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第10期2082-2098,共17页
Linear temporal logic(LTL)is an intuitive and expressive language to specify complex control tasks,and how to design an efficient control strategy for LTL specification is still a challenge.In this paper,we implement ... Linear temporal logic(LTL)is an intuitive and expressive language to specify complex control tasks,and how to design an efficient control strategy for LTL specification is still a challenge.In this paper,we implement the dynamic quantization technique to propose a novel hierarchical control strategy for nonlinear control systems under LTL specifications.Based on the regions of interest involved in the LTL formula,an accepting path is derived first to provide a high-level solution for the controller synthesis problem.Second,we develop a dynamic quantization based approach to verify the realization of the accepting path.The realization verification results in the necessity of the controller design and a sequence of quantization regions for the controller design.Third,the techniques of dynamic quantization and abstraction-based control are combined together to establish the local-to-global control strategy.Both abstraction construction and controller design are local and dynamic,thereby resulting in the potential reduction of the computational complexity.Since each quantization region can be considered locally and individually,the proposed hierarchical mechanism is more efficient and can solve much larger problems than many existing methods.Finally,the proposed control strategy is illustrated via two examples from the path planning and tracking problems of mobile robots. 展开更多
关键词 Abstraction-based control design dynamic quantization formal methods linear temporal logic(LTL)
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