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Accelerated Value Iteration-Based Safe Q-Learning for Data-Driven Optimal Tracking Control

IEEE Transactions on Cybernetics 2025年 PP卷 PP页

作者： Zhao, Mingming Wang, Ding Song, Shijie Qiao, Junfei Beijing University of Technology School of Information Science and Technology The Beijing Key Laboratory of Computational Intelligence and Intelligent System The Beijing Laboratory of Smart Environmental Protection The Beijing Institute of Artificial Intelligence Beijing100124 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China

In this article, an accelerated value iteration-based safe Q-learning (SQL) algorithm is developed to design the tracking controller for unknown nonlinear systems. First, an augmented Q-function, consisting of a quadratic utility function and an adjustable positive-definite control barrier function (CBF), is devised to ensure both the optimality and safety of the tracking controller. The quadratic utility function, associated with optimality, guarantees that the tracking controller can eliminate the ultimate tracking error, regardless of the reference trajectory. The adjustable positive-definite CBF, pertaining to safety, ensures that the tracking error converges faster toward zero while remaining within the safe set at all times. Second, an accelerated iterative learning mechanism, comprising policy evaluation (PE) and policy improvement (PI), is employed to discover the safe optimal tracking control policy. Integrating the difference between two iterative Q-functions into the current PE process can expedite the convergence rate of the SQL algorithm. A policy optimization technique based on Nesterov Momentum method is utilized to accelerate the PI process of the SQL algorithm. When faced with a large amount of offline data, the two-stage accelerated learning effectively reduces computational pressure. Furthermore, convergence of the Q-function sequence and safety of the optimal tracking policy are theoretically analyzed. Finally, by using neural networks and the action-critic structure, two simulation examples are performed to verify the availability of accelerated SQL methods. © 2013 IEEE.

关键词： Proportional control systems

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Wastewater treatment control method based on a rule adaptive recurrent fuzzy neural network

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International Journal of intelligent computing and Cybernetics 2017年第2期10卷 94-110页

作者： Junfei Qiao Gaitang Han Honggui Han Wei Chai Faculty of Information Technology Beijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of TechnologyBeijingChina

Purpose-The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment ***/methodology/approach-A control strategy based on rule adaptive recurrent neural network(RARFNN)is proposed in this paper to control the dissolved oxygen(DO)concentration and nitrate nitrogen(SNo)*** structure of the RARFNN is self-organized by a rule adaptive algorithm,and the rule adaptive algorithm considers the overall information processing ability of neural ***,a stability analysis method is given to prove the convergence of the proposed ***-By application in the control problem of wastewater treatment process(WWTP),results show that the proposed control method achieves better performance compared to other ***/value-The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic *** RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant ***,the rule adaptive mechanism considers the overall information processing ability judgment of the neural network,which can ensure that the neural network contains the information of the biochemical reactions.

关键词： Information processing ability Recurrent fuzzy neural network Rule adaptive Wastewater treatment

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RaSTFormer: region-aware spatiotemporal transformer for visual homogenization recognition in short videos

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Neural computing and Applications 2024年第18期36卷 10713-10732页

作者： Zhang, Shuying Zhang, Jing Zhang, Hui Zhuo, Li Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China

With the surge in network traffic, the homogenization of short video content is becoming increasingly prominent, resulting in low-quality entertainment due to proliferation and infringement. Therefore, recognizing visual homogeneity in short videos is of great significance. Considering the extremely similar dynamic evolution in specific regions from intra and inter-frames, introducing region-aware attention is attractive to achieve homogenization recognition. Therefore, we propose a region-aware spatiotemporal Transformer (RaSTFormer) for visual homogenization recognition, including: (1) a region-aware Transformer encoder is designed to extract multi-region frame-level features from short videos;(2) a multi-layer spatiotemporal Transformer decoder is used to aggregate multi-region frame-level features, generating shot-level spatiotemporal features;and (3) measuring symmetric shot-level chamfer similarity to recognize visual homogeneous content. Specially, we established a real-world video homogenization dataset, BJUT-HCD, and conducted extensive experiments. The proposed RaSTFormer achieved the highest mean average precision (mAP) of 98.13% and a top-1 accuracy of 99.37%, outperforming SOTA methods. The results show that our method achieves competitive performance in visual homogenization recognition in short videos. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Homogenization method

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Evolution-guided Q-learning for tracking control of unknown dynamic systems

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Neurocomputing 2025年 640卷

作者： Yuan, Zeqiang Wang, Ding Wang, Jiangyu Zhao, Mingming Qiao, Junfei School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing100124 China Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing100124 China

In this article, a novel evolution-guided accelerated Q-learning (EGAQL) algorithm is proposed to solve optimal tracking control problems for unknown nonlinear systems. Traditional Q-learning methods in tracking control, which rely on gradient-based strategies, often struggle with limited accuracy in policy improvement and tracking performance. To address these challenges, a novel evolutionary algorithm is applied to Q-learning, enabling dynamic adjustment of search intensity. By integrating evolutionary computation into the policy improvement process, EGAQL eliminates the dependence on gradient information and derives more precise solutions. Furthermore, to improve the accuracy of the reference control policy, evolutionary algorithms are also employed to optimize the model neural network. The algorithm is proven to guarantee convergence and monotonic improvement. Finally, a simulation experiment validates the effectiveness of the proposed method. © 2025 Elsevier B.V.

关键词： Adaptive critic designs Evolutionary computation Optimal tracking control Q-learning Swarm optimization

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Ensemble Self-Organizing Recursive Neural Network for Modeling Furnace Temperature in Municipal Solid Waste Incineration

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Applied Soft computing 2025年

作者： Tao Yu Haixu Ding Junfei Qiao School of Information Science and Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing 100124 China Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing 100124 China Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing 100124 China

The modeling of furnace temperature (FT) is the foundation of optimizing and controlling in municipal solid waste incineration (MSWI) process. However, owing to the high nonlinearity and dynamicity, complex reaction mechanisms, and strong coupling phenomena of MSWI process, accurately modeling the FT remains a significant challenge. In this paper, an ensemble self-organizing recursive neural network with information fusion gain algorithm (ESORNN-IFG) is proposed for FT modeling in MSWI process. First, the AdaBoost algorithm is introduced to combine multiple base learners by weighting them to enhance the accuracy and robustness of the strong learner. Second, an IFG index is introduced to appraise the contribution of hidden neurons and their interrelationships. Third, a self-organizing strategy combined with the IFG index is designed for adjusting the structure of the base learners during model training. Finally, the merits and effectiveness of the proposed ESORNN-IFG is confirmed by comparison with other existing approaches after testing the experimental results on several benchmark problems and the practical application of FT modeling in MSWI process.

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Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration

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Chinese Journal of Chemical Engineering 2023年第4期56卷 46-57页

作者： Haoshan Duan Xi Meng Jian Tang Junfei Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing 100124China

Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission *** this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)***,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex ***,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the ***,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application ***,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI *** the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

关键词： Municipal solid waste incineration NO_(x)concentration prediction Modular neural network Model

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Dynamic multi-objective intelligent optimal control toward wastewater treatment processes

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Science China(Technological Sciences) 2022年第3期65卷 569-580页

作者： XIE YingBo WANG Ding QIAO JunFei Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of TechnologyBeijing 100124China Beijing Institute of Artificial Intelligence Beijing University of TechnologyBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing University of TechnologyBeijing 100124China

Wastewater treatment plays a crucial role in alleviating water shortages and protecting the environment from *** to the strong time variabilities and complex nonlinearities within wastewater treatment systems,devising an efficient optimal controller to reduce energy consumption while ensuring effluent quality is still a bottleneck that needs to be *** this paper,in order to comprehensively consider different needs of the wastewater treatment process(WTTP),a two-objective model is to consider a scope,in which minimizing energy consumption and guaranteeing effluent quality are both considered to improve wastewater treatment *** efficiently solve the model functions,a grid-based dynamic multi-objective evolutionary decomposition algorithm,namely GD-MOEA/D,is designed.A GD-MOEA/D-based intelligent optimal controller(GD-MOEA/D-IOC)is devised to achieve tracking control of the main operating variables of the ***,the benchmark simulation model No.1(BSM1)is applied to verify the validity of the proposed *** experimental results demonstrate that the constructed models can catch the dynamics of WWTP ***,GD-MOEA/D has better optimization ability in solving the designed ***-MOEA/D-IOC can achieve a significant improvement in terms of reducing energy consumption and improving effluent ***,the designed multi-objective intelligent optimal control method for WWTP has great potential to be applied to practical engineering since it can easily achieve a highly intelligent control in WTTP.

关键词： wastewater treatment processes evolutionary algorithms(EAs) multi-objective optimization performance functions

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A Hierarchical Scheme for Video-Based Person Re-identification Using Lightweight PCANet and Handcrafted LOMO Features

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Chinese Journal of Electronics 2021年第2期30卷 289-295页

作者： LI Youjiao ZHUO Li LI Jiafeng ZHANG Jing Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Faculty of Information Technology College of Micro-electronics Beijing University of Technology College of Computer Science and Technology Shandong University of Technology

A two-level hierarchical scheme for video-based person re-identification(re-id) is presented, with the aim of learning a pedestrian appearance model through more complete walking cycle extraction. Specifically,given a video with consecutive frames, the objective of the first level is to detect the key frame with lightweight Convolutional neural network(CNN) of PCANet to reflect the summary of the video content. At the second level, on the basis of the detected key frame, the pedestrian walking cycle is extracted from the long video sequence. Moreover,local features of Local maximal occurrence(LOMO) of the walking cycle are extracted to represent the pedestrian's appearance information. In contrast to the existing walking-cycle-based person re-id approaches, the proposed scheme relaxes the limit on step number for a walking cycle, thus making it flexible and less affected by noisy frames. Experiments are conducted on two benchmark datasets: PRID 2011 and i LIDS-VID. The experimental results demonstrate that our proposed scheme outperforms the six state-of-art video-based re-id methods, and is more robust to the severe video noises and variations in pose,lighting, and camera viewpoint.

关键词： video surveillance image sequences two-level hierarchical scheme video content local features image representation video-based re-id methods pedestrian walking cycle video signal processing convolutional neural nets local maximal occurrence noisy frames lightweight PCANet complete walking cycle extraction pedestrian appearance information walking-cycle-based person re-id approaches LOMO features lightweight convolutional neural network learning (artificial intelligence) pedestrian appearance model consecutive frames video noises video-based person re-identification iLIDS-VID benchmark dataset LOMO PRID 2011 benchmark dataset video sequence image matching detected key frame object detection

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A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models

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PeerJ Computer Science 2025年 11卷 e2646-e2646页

作者： Zga, Adel Zitouni, Farouq Harous, Saad Sallam, Karam Almazyad, Abdulaziz S. Xiong, Guojiang Mohamed, Ali Wagdy Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang China Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza Egypt Applied Science Research Center Applied Science Private University Amman Jordan

This study conducts a comparative analysis of the performance of ten novel and wellperforming metaheuristic algorithms for parameter estimation of solar photovoltaic models. This optimization problem involves accurately identifying parameters that reflect the complex and nonlinear behaviours of photovoltaic cells affected by changing environmental conditions and material inconsistencies. This estimation is challenging due to computational complexity and the risk of optimization errors, which can hinder reliable performance predictions. The algorithms evaluated include the Crayfish Optimization Algorithm, the Golf Optimization Algorithm, the Coati Optimization Algorithm, the Crested Porcupine Optimizer, the Growth Optimizer, the Artificial Protozoa Optimizer, the Secretary Bird Optimization Algorithm, the Mother Optimization Algorithm, the Election Optimizer Algorithm, and the Technical and Vocational Education and Training-Based Optimizer. These algorithms are applied to solve four well-established photovoltaic models: the singlediode model, the double-diode model, the triple-diode model, and different photovoltaic module models. The study focuses on key performance metrics such as execution time, number of function evaluations, and solution optimality. The results reveal significant differences in the efficiency and accuracy of the algorithms, with some algorithms demonstrating superior performance in specific models. The Friedman test was utilized to rank the performance of the various algorithms, revealing the Growth Optimizer as the top performer across all the considered models. This optimizer achieved a root mean square error of 9.8602187789E−04 for the single-diode model, 9.8248487610E−04 for both the double-diode and triplediode models and 1.2307306856E−02 for the photovoltaic module model. This consistent success indicates that the Growth Optimizer is a strong contender for future enhancements aimed at further boosting its efficiency and effectiveness. Its

关键词： Mean square error

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Prediction of PM2.5 Concentration Based on Recurrent Fuzzy Neural Network 36

Prediction of PM2.5 Concentration Based on Recurrent Fuzzy N...

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第36届中国控制会议

作者： Shanshan Zhou Wenjing Li Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781538629185

The prediction of PM2.5 is difficult because the variation of PM2.5 concentration is a nonlinear dynamic ***,a recurrent fuzzy neural network prediction method is proposed to predict the PM2.5 concentration in this ***,the partial least squares(PLS) algorithm is used to select key input variables as a preprocessing ***,a recurrent fuzzy neural network model is established and the gradient descent algorithm with an adaptive learning rate is used to train the neural *** results show that the recurrent neural network has better prediction performance and higher interpretability than fuzzy neural network(FNN) and radial-basis function(RBF) feed forward neural network.

关键词： PM2.5 prediction recurrent fuzzy neural network PLS adaptive learning rate

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