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IEEE Transactions on Green Communications and Networking 2024年第2期9卷 1-1页

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Key Lab of Industrial Computer Control Engineering of Hebei Province China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China

This paper deals with a cooperation communication problem (relay selection and power control) for mobile underwater acoustic communication networks. To achieve satisfactory transmission capacity, we propose a reinforcement-learning-based cooperation communication scheme to efficiently resist the highly dynamic communication links and strongly unknown time-varying channel states caused by the mobility of Autonomous Underwater Vehicles (AUVs). Firstly, a particular Markov decision process is developed to model the dynamic relay selection process of mobile AUV in the unknown scenario. In the developed model, an experimental statistical-based partition mechanism is proposed to cope with the greatly increasing dimension of the state space caused by the mobility of AUV, reducing the search optimization difficulty. Secondly, a dual-thread reinforcement learning structure with actual and virtual learning threads is proposed to efficiently track the superior relay action. In the actual learning thread, the proposed improved probability greedy policy enables the AUV to strengthen the exploration for the reward information of potential superior relays on the current state. Meanwhile, in the virtual learning thread, the proposed upper-confidence-bound-index-based uncertainty estimation method can estimate the action-reward level of historical states. Consequently, the combination of actual and virtual learning threads can efficiently obtain satisfactory Q value information, thereby making superior relay decision-making in a short time. Thirdly, a power control mechanism is proposed to reuse the current observed action-reward information and transform the multiple unknown parameter nonlinear joint power optimization problem into a convex optimization problem, thereby enhancing network transmission capacity. Finally, simulation results verify the effectiveness of the proposed scheme. IEEE

关键词： Convex optimization

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Anti-saturation fault-tolerant adaptive torsional vibration control with fixed-time prescribed performance for rolling mill main drive system

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Journal of Iron and Steel Research International 2024年第3期31卷 660-669页

作者： Shuang Liu Chen Du Cong Zhang Zhen-hua Bai Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao 066004HebeiChina National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Yanshan UniversityQinhuangdao 066004HebeiChina

An anti-saturation fault-tolerant adaptive torsional vibration control method with fixed-time prescribed performance for the rolling mill main drive system(RMMDS)was investigated,which is affected by control input saturation,actuator faults,sensor measurement errors,and parameter ***,we gave a continuously differentiable saturation function to approximate the control input saturation characteristic of the RMMDS,translating the saturation characteristic into the matched uncertainty and unknown time-varying gain in the ***,an RMMDS mathematical model with unmatched uncertainty and unknown time-varying gain was developed,taking into account the presence of control input saturation,actuator faults,sensor measurement errors,and parameter *** on the established mathematical model,an error transformation model of the roll speed tracking was constructed by the equivalent error transformation *** to the error transformation model,a barrier Lyapunov function and a novel adaptive controller were studied to ensure that the roll speed tracking error always evolves inside a fixed-time asymmetric ***,numerical simulations were performed in Matlab.Simulink to verify the effectiveness and superiority of the proposed control method in suppressing the RMMDS torsional vibration.

关键词： Fixed-time prescribed performance control input saturation Actuator fault Unknown time-varying gain Adaptive control Asymmetric constraint

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Design and Analysis of Polynomial Positive Fuzzy System controllers Based on Markov Transitions

Design and Analysis of Polynomial Positive Fuzzy System Cont...

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2024 China Automation Congress, CAC 2024

作者： Chang, Sen Li, Xiaomiao Liu, Fucai Bao, Zhiyong Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

ISBN: (纸本)9798350368604

This study explores polynomial Markov positive fuzzy systems, formulating controllers for these systems by examining the prerequisites for positivity and stochastic stability. Initially, polynomials are incorporated into the Markov positive fuzzy system to represent nonlinear elements better and enhance its capacity to model nonlinear systems. Concurrently, the study discusses the non-convex scenarios encountered in the stability analysis of the closed-loop system once the positive fuzzy system controller is integrated and confirms the stability of the Markov polynomial positive fuzzy system. Ultimately, the effectiveness of the proposed approaches is demonstrated through a numerical simulation example. © 2024 IEEE.

关键词： Fuzzy systems

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A Distributed Algorithm for Large-Scale Multi-Agent MINLPs 63

A Distributed Algorithm for Large-Scale Multi-Agent MINLPs

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Dong, Sheng Xia, Weiguo Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering China

ISBN: (纸本)9798350316339

In this paper, we focus on the optimization of large-scale multi-agent systems, where agents collab.ratively optimize the sum of local objective functions through their own continuous and/or discrete decision variables, subject to global coupling constraints and local constraints. The resulting Mixed-Integer Nonlinear Programmings (MINLPs) are NP-hard, non-convex, and large-scale. Therefore, this paper aims to design distributed algorithms to find feasible suboptimal solutions with a guaranteed bound. To this end, considering dual decomposition as an effective method to decompose large-scale constraint-coupled optimization problems, we first show, based on the convexification effects of large-scale MINLPs, that the primal solutions from the dual are near-optimal under certain conditions. This expands recent results in Mixed-Integer Linear Programmings (MILPs) to the nonlinear case but requires additional efforts on the proof. Utilizing this result to tighten the coupling constraints, we develop a fully distributed algorithm for the tightened problem, based on dual decomposition and consensus protocols. The algorithm is guaranteed to provide feasible solutions for the original MINLP. Moreover, asymptotic suboptimality bounds are established for the obtained solution. Finally, the efficacy of the method is verified through numerical simulations. © 2024 IEEE.

关键词： Mixed-integer linear programming

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An Improved Seagull Optimization Algorithm for Flexible Job-shop Scheduling Problem 13

An Improved Seagull Optimization Algorithm for Flexible Job-...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Ma, Kai Yao, Shijie Guo, Shiliang Yang, Bin Li, Shuai Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China

ISBN: (纸本)9798350315196

Job-shop scheduling problem is the core link and key technology to realize smart factory and develop smart manufacturing technology. Among them, the flexible job-shop scheduling problem (FJSP) which is more consistent with the actual production and processing mode has been focused. This paper proposes an improved seagull optimization algorithm(ISOA) for solving the FJSP. The ISOA uses chaos initialization and reverse learning to optimize initial population, and introduces Q-learning to change the fitting parameters to balance global search and local search. In addition, we propose a local search model that is adaptive iterative process to enhance the algorithm's optimization search capability. The results of the experiment proved that the algorithm is superior in terms of optimization speed and scheduling results. © 2023 IEEE.

关键词： Iterative methods

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Relaxed Stability Analysis and Event-Triggered controller Design for Discrete-Time Positive T-S Fuzzy Networked control Systems 49

Relaxed Stability Analysis and Event-Triggered Controller De...

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49th Annual Conference of the IEEE industrial Electronics Society, IECON 2023

作者： Wang, Xiaoxiao Li, Xiaomiao Bao, Zhiyong Liu, Fucai Du, Yuehao Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China

ISBN: (纸本)9798350331820

This paper deals with the relaxation stability analysis and event-triggered controller design of discrete-time positive Takagi-Sugeno (T-S) fuzzy networked control systems (FNCSs). The constraints of positive conditions in NCSs bring significant challenges to stability analysis and controller design. A 1-norm event-triggered scheme is proposed for the positive system, which can effectively reduce the frequency of network communication. Since the event-triggered T-S fuzzy controller (ETTSFC) can only observe the latest transmission state after the triggering action, the ETTSFC selects the premise variables different from the T-S fuzzy model, improving design and implementation flexibility. Using the linear copositive Lyapunov function (LCLF), the design criteria of the event-triggered controller to ensure the positivity and stability of the closed-loop system are derived. To relax the sum-of-squares (SOS) based conservative condition, we adopt the Taylor series membership functions (TSMFs) and introduce TSMFs and their approximation error into the stability conditions. Finally, an example is given to illustrate the effectiveness of the design. © 2023 IEEE.

关键词： Lyapunov functions

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An Improved NSGAII Algorithm for Flexible Job-shop Scheduling Problem Based on A New Decoding Mechanism 13

An Improved NSGAII Algorithm for Flexible Job-shop Schedulin...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Li, Man Ma, Kai Guo, Shiliang Li, Shuai Yang, Bin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China

ISBN: (纸本)9798350315196

The flexible job-shop scheduling problem (FJSP) is a famous combinatorial optimization problem. FJSP is widely used in process manufacturing industry, so it is of great significance to study FJSP to improve production efficiency. In this paper, a non-dominated sorting genetic algorithm II based on a new decoding mechanism (NSGAII-NDM) is proposed. Firstly, the opposite-based learning strategy is introduced on the basis of NSGAII, and the sufficiency of global search is improved by generating the opposite value. Secondly, an improved adaptive crossover and mutation probability strategy is added, which can dynamically adjust the inherent crossover and mutation probability with the number of iterations and improve the convergence speed of the algorithm. Furthermore, based on fully active scheduling strategy, a new decoding mechanism is proposed, which can get a better scheduling scheme by arranging the machines and starting time of each process more reasonably. Finally, the improved algorithm is tested in a standard test case, which verifies the superiority of NSGAII-NDM in solving the flexible job-shop scheduling problem. © 2023 IEEE.

关键词： Production efficiency

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Fault Tolerant control Based on GA-DRE for a Manipulator with Actuator Fault 43

Fault Tolerant Control Based on GA-DRE for a Manipulator wit...

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43rd Chinese control Conference, CCC 2024

作者： Qiu, Jiongzhi Fang, Yiming Zhao, Donliang Liu, Le Shao, Nuan Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Hebei University of Environmental Engineering Department of Environmental Engineering Qinhuangdao China

ISBN: (纸本)9789887581581

To solve the trajectory tracking control problem of a rigid manipulator system in the case of unexpected actuator failure, considering the uncertainty of system parameters and bounded disturbance, an adaptive fault-tolerant tracking control strategy is proposed in this paper. Firstly, an adaptive strategy is designed to estimate and compensate the effects of actuator failure and system uncertainty, and the dynamic regression matrix is extended and mixed to improve the accuracy of parameter estimation without persistency of excitation (PE), which will enhance the trajectory tracking performance. Then, in the framework of sliding mode fault-tolerant control, combined with standard Gradient Adaptive and Dynamic Regressor Extension (GA-DRE) parameter estimation technology, a composite adaptive fault-tolerant controller is designed. Finally, theoretical analysis and numerical simulation results show that the proposed method can perform accurate parameter estimation in a finite time under the premise of matching the new conditions based on DRE additional terms, and the trajectory tracking errors of the expected positions and velocities of each joint of the manipulator can asymptotically converge to zero. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Sliding mode control

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Preclinical Assessment of Upper Limb Tremor in Parkinson's Disease with Deep Learning and Wearable Technology

Preclinical Assessment of Upper Limb Tremor in Parkinson's D...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lin, Fang Wang, Zhelong Qiu, Sen Zhao, Hongyu School of Control Science and Engineering Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Liaoning Dalian China

ISBN: (纸本)9798350337020

Tremors are typically experienced by patients at the beginning of Parkinson's disease (PD). Clinicians evaluate clinical symptoms based on scale and experience, but mild tremors do not have significant characteristics and are difficult to observe with the naked eye. Implementing an intelligent and objective method to identify PD patients with early tremor symptoms and healthy controls (HC) is necessary. This study used wearable sensors to collect 9-axis inertial signals and 2-channel sEMG signals at the wrists of 13 HC and 24 PD patients from Dalian Municipal Central Hospital. Based on a Long Short-Term Memory Network (LSTM), an attention mechanism, and a Fully Convolutional Network (FCN), we develop a model to classify and recognize data from PD patients. Compared the proposed method with several classification methods, the results showed that the proposed method achieved higher classification accuracy (91.78%), precision (100%), recall (87.50%), and F1-score (93.33%) of PD class than Support Vector Machine, FCN, and LSTM. The computing time of the proposed method is approximately 1 second. The proposed method identifies early PD patients by pre-clinical assessment of mild upper limb tremors, which is valuable for early treatment and rehabilitation. © 2023 IEEE.

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Mechanistic data-driven combined modelling and predictive control method for coal-fired power unit 5

Mechanistic data-driven combined modelling and predictive co...

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5th International Conference on industrial Artificial Intelligence, IAI 2023

作者： Hu, Jingyu Han, Zhongyang Zhao, Jun Wang, Wei Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Liaoning 116024 China

ISBN: (纸本)9798350325294

With the increase of the proportion of new energy sources connected to the grid, challenges are also emerged for modeling and control of the conventional coal-fired power units to increase their operational flexibility of deep peak-regulation. Traditional data-driven modelling (DDM) requires a large amount of training data containing a variety of operating conditions, which are hard to be fully satisfied in engineering practice. This paper investigates the mechanics and dynamic characteristics of boiler-turbine systems in the coal-fired power unit, and proposes a mechanistic data-driven combined modelling (M-DDM) method. It not only requires less amount of data comparing with the DDM based method, but also covers a wide range of operating conditions by considering the dynamic characteristics of the coal-fired power unit. A model predictive control (MPC) based method was subsequently designed as the control vehicle. In order to improve the computing efficiency and reduce the probability of model mismatch considering the involved ordinary differential equations (ODEs) and nonlinearities. reinforcement learning is embedded into the MPC. Finally, simulations are conducted for demonstrating the superior performance of the proposed methods comparing with other commonly deployed modeling and control methods. © 2023 IEEE.

关键词： Model predictive control

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