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Data-Driven Predictive Control for Continuous-Time Nonlinear Systems:A Nonzero-Sum Game Approach 认领 引用
1
作者 Juan Liu Hao Zhang +1 位作者 Yifan Xie Frank Allgöwer 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期495-497,共3页
Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over p... Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over predictive control input sequences,deriving multiple optimal predictive control input sequences from its solution. 展开更多
关键词 predictive control nonzero sum game observation loss predictive control input sequencesderiving continuous time nonlinear systems optimal predictive control input sequences reinforcement learning
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Adaptive-length data-driven predictive control for post-operation of space robot non-cooperative target capture with disturbances 认领 引用 被引量：1
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作者 Peiji WANG Bicheng CAI +2 位作者 Chengfei YUE Yong ZHAO Weiren WU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期485-498,共14页
This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi... This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering. 展开更多
关键词 Combined control Data-driven predictive control Post operation Predictive control systems Space non-cooperative target capture
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Human-Robot Interaction-Based Model Predictive Control for Exoskeleton Robots Driven by Series Elastic Actuators 认领 引用
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作者 Changxian Xu Keping Liu Zhongbo Sun 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期486-488,共3页
Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charg... Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charge(RIC)mode facilitates the ER driven by SEA to provide the required assistance and support for the subject. 展开更多
关键词 human robot interaction model predictive assistance support series elastic actuator model predictive control series elastic actuator sea exoskeleton robot robot charge mode
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A Scale Separation Hybrid Predictive Model and Its Application to Predict Summer Monthly Precipitation in Northeast China 认领 引用
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作者 Lei YU Aihui WANG Changzheng LIU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第3期504-528,共25页
Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying clima... Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90. 展开更多
关键词 Northeast China precipitation scale separation approach statistical predictive model recurrent neural network predictive model
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Hybrid AI-IoT Framework with Digital Twin Integration for Predictive Urban Infrastructure Management in Smart Cities 认领 引用 被引量：1
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作者 Abdullah Alourani Mehtab Alam +2 位作者 Ashraf Ali Ihtiram Raza Khan Chandra Kanta Samal 《Computers, Materials & Continua》 SCIE EI 2026年第1期462-493,共32页
The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often... The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities. 展开更多
关键词 Smart cities digital twin AI-IOT framework predictive infrastructure management edge computing reinforcement learning optimization methods federated learning urban systems modeling smart governance
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Predictive tool for evident histological liver injury in chronic hepatitis B patients:Development and validation 认领 引用 被引量：1
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作者 Zhong-Shang Dai Xin Cao +1 位作者 Yong-Fang Jiang Bo He 《World Journal of Hepatology》 2026年第2期194-208,共15页
BACKGROUND Chronic hepatitis B(CHB)is a leading cause of liver-related mortality,progressing to fibrosis,cirrhosis,and hepatocellular carcinoma.Existing noninvasive tools(e.g.,aspartate aminotransferase to platelet ra... BACKGROUND Chronic hepatitis B(CHB)is a leading cause of liver-related mortality,progressing to fibrosis,cirrhosis,and hepatocellular carcinoma.Existing noninvasive tools(e.g.,aspartate aminotransferase to platelet ratio index,fibrosis-4 index,liver stiffness measurement)and invasive liver biopsy have limitations in assessing evident histological liver injury(EHLI),highlighting the need for novel predictive models.AIM To develop and validate a predictive model for EHLI in CHB patients using a cohort from Hunan Province,China,to facilitate early risk identification and optimize resource allocation.METHODS This observational real-world study enrolled 223 CHB patients(August 2020 to March 2022)from the Second Xiangya Hospital,divided into development(n=159)and validation(n=64)cohorts(7:3 ratio).EHLI was defined as Ishak fibrosis stage≥3 and/or histologic activity index≥9.Variables were screened via univariable logistic regression and least absolute shrinkage and selection operator regression,and a multivariable logistic regression model and nomogram were constructed.Performance was evaluated using area under the curve(AUC),calibration plots,Hosmer-Lemeshow test,and decision curve analysis(DCA).Gene expression profiles were analyzed to identify immune-related pathways.RESULTS L59,platelet count(PLT),alanine transaminase(ALT),and aspartate transaminase(AST)were identified as independent predictors of EHLI.The model showed high discriminative ability,with AUC of 0.921[95%confidence interval(CI):0.880-0.963]in the development cohort and 0.959(95%CI:0.910-1.0)in the validation cohort,demonstrating a 20%-32%relative improvement in AUC over conventional noninvasive scores.Calibration plots demonstrated good agreement between predicted and observed EHLI,and DCA confirmed clinical utility(threshold probabilities:20%-80%).Transcriptomic analysis identified 210 differentially expressed genes,with hub genes(e.g.,COL1A2)and transforming growth factor-β/Smad pathway involvement linked to liver injury.CONCLUSION A novel nomogram incorporating L59,PLT,ALT,and AST robustly predicts EHLI in CHB patients.This model,using routinely measured variables,aids clinical decision-making and optimizes resource allocation. 展开更多
关键词 Chronic hepatitis B Evident histological liver injury Predictive model Performance Clinical applicability
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Distributed Iterative Learning Model Predictive Control for Unmanned Surface Vehicles 认领 引用
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作者 Xinyuan Huang Lihao Ma Huiping Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1512-1514,共3页
Dear Editor,This letter proposes a distributed iterative learning model predictive control(LMPC)strategy for coordinated trajectory tracking of multiple unmanned surface vehicles(USVs).By learning from previously feas... Dear Editor,This letter proposes a distributed iterative learning model predictive control(LMPC)strategy for coordinated trajectory tracking of multiple unmanned surface vehicles(USVs).By learning from previously feasible control and state trajectories,each USV iteratively refines its input sequence to improve the accuracy of trajectory tracking and formation control.To tackle challenges such as system coupling,limited onboard computational resources,and communication constraints,the method integrates the alternating direction method of multipliers(ADMM)with iterative learning.The effectiveness and advantages of the proposed approach are demonstrated through comparison results. 展开更多
关键词 Distributed Control learning previously feasible control state trajectorieseach distributed iterative learning model predictive control lmpc strategy unmanned surface vehicles usvs coordinated trajectory tracking Iterative Learning Control Model Predictive Control Unmanned Surface Vehicles
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Development and validation of machine learningbased in-hospital mortality predictive models for acute aortic syndrome in emergency departments 认领 引用
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作者 Yuanwei Fu Yilan Yang +6 位作者 Hua Zhang Daidai Wang Qiangrong Zhai Lanfang Du Nijiati Muyesai YanxiaGao Qingbian Ma 《World Journal of Emergency Medicine》 SCIE CAS CSCD 2026年第1期43-49,共7页
BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suita... BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation. 展开更多
关键词 Emergency department Acute aortic syndrome Mortality Predictive model Machine learning Algorithms
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Model predictive control based on LSTM neural network for maglev vehicle’suspension system 认领 引用
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作者 Mengjuan Liu Han Wu +3 位作者 Xin Liang Jiali Liu Xiaohui Zeng Kaixuan Hu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期504-517,共14页
To improve the suspension performance of high-speed maglev vehicles under complex external disturbance,a composite model predictive control(MPC)algorithm based on a neural network is proposed.Firstly,the nonlinear dyn... To improve the suspension performance of high-speed maglev vehicles under complex external disturbance,a composite model predictive control(MPC)algorithm based on a neural network is proposed.Firstly,the nonlinear dynamic response prediction model is constructed utilizing the long short-term memory(LSTM)neural network,and this model is trained by machine learning.Subsequently,a rolling optimization controller of the MPC algorithm is designed according to the vehicle suspension system’s prediction model and the suspension target.To compensate for the error of the prediction model resulting from changes in the control algorithm,a composite MPC algorithm is devised by combining both the proportionalintegral-derivative(PID)algorithm and the MPC algorithm.This composite approach enables the suspension system to switch the selection of control algorithms in the suspension system according to the prediction error.Finally,the effectiveness of the composite MPC algorithm is verified by simulation and experiment.The results show that the prediction model based on the LSTM neural network can effectively predict the future dynamic response of the vehicle.Moreover,the proposed MPC algorithm can effectively suppress the suspension gap fluctuation in the high-speed maglev vehicle,thereby fostering improved stability in the suspension system. 展开更多
关键词 Maglev vehicle Suspension system Model predictive control Long short-term memory neural network
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Networked Predictive Control:A Survey 认领 引用
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作者 Zhong-Hua Pang Tong Mu +3 位作者 Yi Yu Haibin Guo Guo-Ping Liu Qing-Long Han 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期3-20,共18页
Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induc... Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induced delays,packet dropouts,and packet disorders.Despite significant advancements,the increasing complexity and dynamism of network environments,along with the growing complexity of systems,pose new challenges for NPC.These challenges include difficulties in system modeling,cyber attacks,component faults,limited network bandwidth,and the necessity for distributed collaboration.This survey aims to provide a comprehensive review of NPC strategies.It begins with a summary of the primary challenges faced by NCSs,followed by an introduction to the control structure and core concepts of NPC.The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control,fault-tolerant control,distributed coordinated control,and event-triggered control.Moreover,it reviews notable works that have implemented these schemes.Finally,the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts. 展开更多
关键词 Communication constraints cyber attacks networked control systems networked multi-agent systems networked predictive control
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Dynamic Neural-Model-Based Predictive Control for Autonomous Wheel-Legged Robot System 认领 引用
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作者 Jiehao Li Junzheng Wang +2 位作者 Hongbo Gao Xiwen Luo C.L.Philip Chen 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第1期83-97,共15页
Mobile wheel-legged robots exhibiting mobility,stability and reliability have garnered heightened research attention in demanding real-world scenarios,especially in material transport,emergency response and space expl... Mobile wheel-legged robots exhibiting mobility,stability and reliability have garnered heightened research attention in demanding real-world scenarios,especially in material transport,emergency response and space exploration.The kinematics model merely delineates the geometric relationship of the controlled objective,disregarding force feedback.This study investigates model predictive trajectory tracking control utilising the robot dynamic model(DRMPC)in the context of unpredictable interactions.The predictive tracking controller for the wheel-legged robot is introduced in the context of position tracking.A dynamic approximator is employed to address the uncertain interactions in the tracking process.Ultimately,cosimulation and empirical tests are conducted to demonstrate the efficacy of the devised control methodology,which achieves high precision and dependable robustness.This work can elucidate the technical and practical oversight of autonomous movement in complicated environments and enhance the manoeuverability and flexibility. 展开更多
关键词 intelligent control predictive control robotics
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Constrained RLV guidance via pseudospectral model predictive convex programming and predefined-height sliding mode approach 认领 引用
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作者 Zhe LIU Naigang CUI +1 位作者 Feng ZHANG Jialun PU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期466-482,共17页
Online trajectory generation and tracking for the Terminal Area Energy Management(TAEM)phase of a Reusable Launch Vehicle(RLV)is one of the core technologies for achieving a soft landing.The processing of complex non-... Online trajectory generation and tracking for the Terminal Area Energy Management(TAEM)phase of a Reusable Launch Vehicle(RLV)is one of the core technologies for achieving a soft landing.The processing of complex non-convex path constraints significantly reduces the real-time performance of guidance methods.In addition,the terminal full-element state constraints are difficult to satisfy due to the coupling of longitudinal and lateral motion of RLV.To address these issues,a high-precision constrained guidance method for RLV is proposed in this paper.The analytical sensitivity relationships among the terminal states,non-convex path constraints,and control profile are rapidly constructed via multi-interval pseudospectral discretization.The repeated recursive calculation of sensitivity matrix is avoided by linearizing the non-convex constraints at state output points and expanding the sensitivity matrix sequentially,which reduces the time consumption of constraint processing.Furthermore,a model-based prediction-correction process is introduced to eliminate deviation iteratively and constraints are handled using homotopy to improve convergence.Meanwhile,a robust parallel guidance method is presented to overcome numerical instability issues.The guidance commands obtained by trajectory online generation are prioritized executed,while the tracking commands are calculated in parallel to enhance the guidance feasibility.Instead of tracking a fixed reference trajectory,a predefined height-convergent sliding mode surface is designed and tracked online,which can guarantee that the RLV states converge to the desired values at a preset height,even under various uncertainties.Finally,Monte Carlo simulations are conducted to demonstrate the effectiveness and robustness of the proposed method. 展开更多
关键词 Convex optimization Model predictive Parallel guidance Pseudospectral discretization Reusable launch vehicle
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Model predictive control:past,present,and future 认领 引用
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作者 Nan Li Hong Chen +2 位作者 Meng Li Shuyou Yu Yanjun Huang 《Control Theory and Technology》 EI CSCD 2026年第2期173-193,共21页
Model predictive control(MPC)has evolved from an industry-originated heuristic to a rigorously grounded and widely adopted control framework.This survey provides a structured account of MPC's trajectory across thr... Model predictive control(MPC)has evolved from an industry-originated heuristic to a rigorously grounded and widely adopted control framework.This survey provides a structured account of MPC's trajectory across three interrelated dimensions:theoretical foundations,practical deployments,and future outlook.We first trace the emergence of core principles especially around stability and robustness and highlight pivotal contributions that have shaped linear,nonlinear,robust,stochastic,and adaptive MPC variants.The second part reviews computational advances,data-driven innovations,and widespread deployments,with a particular emphasis on automotive applications.Finally,we articulate a forward-looking vision of MPC as a general and unified paradigm for embodied intelligence.Throughout,we underscore contributions from international and Chinese research communities and point to emerging research directions at the intersection of control theory,machine learning,and intelligent systems. 展开更多
关键词 Model predictive control Autonomous systems Embodied intelligence
Tuning nonlinear model predictive control via Bayesian optimization:a comparative performance analysis 认领 引用
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作者 Maria Alice de F.Marques Juarez dos Santos Azevedo +1 位作者 Julio Elias Normey-Rico Marcus V.Americano da Costa 《Control Theory and Technology》 EI CSCD 2026年第3期374-387,共14页
This paper presents a new approach to tuning the cost function weights of a nonlinear model predictive controller(NMPC)using offline Bayesian optimization(BO).We propose a recursive weight selection method that integr... This paper presents a new approach to tuning the cost function weights of a nonlinear model predictive controller(NMPC)using offline Bayesian optimization(BO).We propose a recursive weight selection method that integrates BO directly into the NMPC simulation loop and targets an economic cost function.This approach identifies weights that optimize an economic cost function,ensuring that controller performance is aligned with the operational economics of the process.A case study involving an interconnected tank system with nonlinear level and temperature dynamics illustrates the method.Two operating conditions are tested:an undisturbed scenario and a disturbed one,incorporating sensor noise and model–plant mismatch.In both scenarios,the BO-tuned NMPC outperforms Traditional and Satisficing-based weight strategies,yielding smoother control actions.In the disturbed case,cost reductions of up to 4.5%are achieved.The results confirm that offline BO-based tuning offers a viable and robust alternative to manual or online adaptive strategies,especially in systems where online retraining is impractical.Sensitivity tests further highlight the importance of informed search interval selection to ensure convergence and performance. 展开更多
关键词 Nonlinear predictive control Bayesian optimization Tuning parameters Cost function
Research on Coordinated Operation Strategies for Wind Power Hybrid Energy Storage Systems Based on Model Predictive Control 认领 引用
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作者 Jiguang Wu Qing Zhi +4 位作者 Jin Guan Ruopeng Zhang Lixia Wu Shuhui Zhang Caifeng Wen 《Energy Engineering》 EI 2026年第7期424-444,共21页
This paper proposes a hybrid energy storage control method that coordinates the minimum output of the wind-storage system and the SOC self-recovery capability,applied to stand-alone energy storage stations.Under the p... This paper proposes a hybrid energy storage control method that coordinates the minimum output of the wind-storage system and the SOC self-recovery capability,applied to stand-alone energy storage stations.Under the premise of meeting the wind power smoothing requirements,model predictive control(MPC)is employed to rapidly regulate the SOC and output of the energy storage system during the smoothing process,thereby enhancing its sustained and stable operation capability,and decomposing the original wind power into a direct grid-connected component and a hybrid energy storage smoothing component.Subsequently,the Northern Goshawk AlgorithmImproved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(NGO-ICEEMDAN)method is employed to decompose and reconstruct the hybrid energy storage power obtained from MPC rolling optimization by determining the optimal combination of white noise amplitude weight Nstd and the number of noise additions(NA),and to allocate the reconstructed power between the supercapacitor and the battery.Finally,simulation verification is conducted using actual 10o MW wind power data from a site in Inner Mongolia.The results demonstrate that the proposed strategy can coordinate the relationship among the minimum output of the hybrid energy storage system(HESS),SOC balancing,and grid-connected power fluctuations.The NGO-ICEEMDAN method enables more precise power allocation,thereby improving the rationality and efficiency of energy management in wind power hybrid energy storage systems. 展开更多
关键词 Standalone energy storage power station hybrid energy storage system model predictive control empirical mode decomposition multi-objective optimization
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Relative Motion Based Predictive Adaptive Control:A Case Study of AUV 3D Trajectory Tracking 认领 引用
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作者 Daxiong Ji Xinwei Wang Yuanchang Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期492-494,共3页
Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the... Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the-art predictive adaptive controller(PAC)is proposed with a distinct dual closed-loop structure. 展开更多
关键词 adaptive controller pac autonomous underwater vehicle auv three predictive adaptive control relative motion D trajectory tracking hydrodynamics closed loop structure complex hydrodynamicsa
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Enforcing spatio-temporal collaborations in distributed predictive control under temporally relaxed STL specifications 认领 引用
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作者 Hongbo Yang Yuanyuan Zou Shaoyuan Li 《Control Theory and Technology》 EI CSCD 2026年第2期203-213,共11页
This paper introduces spatio-temporal collaboration(STC),a novel formalism for coordinating multi-agent systems under signal temporal logic(STL).STC defines critical inter-agent dependencies that may be violated by th... This paper introduces spatio-temporal collaboration(STC),a novel formalism for coordinating multi-agent systems under signal temporal logic(STL).STC defines critical inter-agent dependencies that may be violated by the cascading delays resulting from temporal relaxation,a common method for resolving local task conflicts.To address this issue,we first analyze the propagation of task delays through dependent tasks.A time interval refinement strategy is then proposed to maintain the required collaborations.This strategy is integrated into a distributed predictive control algorithm,ensuring simultaneous satisfaction of both STL specifications and STC relations while preserving recursive feasibility and closed-loop stability.Validation via a case study demonstrates the effectiveness of proposed strategy in preventing collaboration failures. 展开更多
关键词 Collaborative control Signal temporal logic Multi-agent system Temporal relaxation Distributed predictive control
Improved Whale Optimization-Based Neural Network Predictive Control for Industrial Refrigeration Systems 认领 引用
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作者 Qi Li Menghan Yang +1 位作者 Shifa Cui Kun Han 《Journal of Harbin Institute of Technology(New Series)》 CAS 2026年第3期1-14,共14页
The inherent nonlinearity and time-delay characteristics of industrial refrigeration processes complicate parameter tuning for conventional PID control,adversely affecting its precision.This makes the control of such ... The inherent nonlinearity and time-delay characteristics of industrial refrigeration processes complicate parameter tuning for conventional PID control,adversely affecting its precision.This makes the control of such systems a significant and challenging research problem.To address this,a novel Neural Network Predictive Control(NNPC)algorithm is proposed,which integrates an Improved Deep Belief Network(IDBN)with an Improved Whale Optimization Algorithm(IWOA).First,the IDBN acts as a high-precision nonlinear prediction model,significantly improving multi-step prediction accuracy.Second,the IWOA is employed to optimize the predictive controller,featuring three major improvements:an improved population initialization,a modified convergence factor update mechanism,and an added disturbance strategy,which collectively accelerate convergence and enhance global search capability.Finally,simulation results demonstrate that the proposed NNPC algorithm achieves superior set-point tracking performance and strong robustness against external disturbances. 展开更多
关键词 industrial refrigeration systems model predictive control time⁃delay whale optimization algorithm deep belief network
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Nonlinear model predictive control strategy for vehicle path tracking with steering actuator’s dynamic characteristics 认领 引用
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作者 Bin Wang Zhiguo Zhao +1 位作者 Xiwen Yuan Yuyi Wang 《Control Theory and Technology》 EI CSCD 2026年第3期359-373,共15页
The dynamic characteristics of the steering actuator,including response time-delay and calibration deviation,are crucial to a vehicle’s path tracking performance.This study proposes a novel vehicle path tracking cont... The dynamic characteristics of the steering actuator,including response time-delay and calibration deviation,are crucial to a vehicle’s path tracking performance.This study proposes a novel vehicle path tracking control strategy by combining nonlinear model predictive control(NMPC),time-delay model control,and calibration deviation compensation control.In the first stage,the path tracking NMPC strategy is designed without considering the steering actuator’s dynamic characteristics.In the second stage,by combining polynomial fitting with linear matrix inequality(LMI)techniques,a nonlinear time-delay model control algorithm is designed to address the response time-delay.In the third stage,based on the inverse model of the calibration deviation,a nonlinear compensation algorithm is designed for steering actuator’s calibration deviation.Finally,simulation and real vehicle experiment results are provided to illustrate the effectiveness of the proposed vehicle path tracking control strategy. 展开更多
关键词 Nonlinear model predictive control(NMPC) Path tracking Nonlinear time-delay model control Calibration deviation compensation control
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