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Stabilizing extremum seeking control applied to model-free bioprocess productivity optimization

IFAC-PapersOnLine 2024年第14期58卷 712-717页

作者： Laurent Dewasme Alain Vande Wouwer Systems Estimation Control and Optimization (SECO) University of Mons 7000 Mons Belgium

This study investigates an application of stabilizing extremum seeking (STAB-ESC) to model-free bioprocess productivity optimization. To this end, microbial growth in a chemostat is considered as a typical example. In contrast with the classical ESC formulation, STAB-ESC confines the measurable cost function in the argument of a periodic and bounded control function, which confers very interesting features to the control scheme in terms of sparse parameter tuning, fast convergence and robustness to measurement noise. These points are highlighted in the present study through an analysis of the Lie-bracket average dynamics and a performance comparison with a Newton-based recursive ES.

关键词： Data-driven control stabilization optimization averaging process control

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Research on Traction optimization control Strategy for 10,000-Ton Heavy Haul Trains 19th

Research on Traction Optimization Control Strategy for 10,00...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Zhang, Kunpeng Tang, Junjie Yang, Hui School of Electrical and Automation Engineering East China Jiaotong University Nanchang330013 China Jiangxi Provincial Key Laboratory of Advanced Control and Optimization Nanchang330013 China State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure Jiangxi Nanchang330013 China

ISBN: (纸本)9789819613823

Aiming at the problems of difficult control of electric traction equipment for heavy haul trains and low accuracy of control models, a distributed fuzzy sliding mode optimization control strategy for heavy haul trains based on a predictive compensation model is proposed. Firstly, based on the complex road condition information of heavy haul railways, a longitudinal dynamics model of heavy haul trains is established. Then, a long short-term memory neural network (LSTM) optimized by the Northern Goshawk optimization (NGO) algorithm is used to predict and compensate the uncertain terms in the model. At the same time, a variable universe fuzzy algorithm (VUF) is used to improve the fast terminal sliding mode controller (FTSM). Finally, the improved controller is combined with the air braking strategy to jointly control the corrected model. The simulation results based on the heavy haul train operation simulation platform and real operation data show that the proposed method can effectively improve the handling stability and safety of 10,000-ton heavy haul trains. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Traction control

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Hierarchical controller Synthesis Under Linear Temporal Logic Specifications Using Dynamic Quantization

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2082-2098页

作者： Wei Ren Zhuo-Rui Pan Weiguo Xia Xi-Ming Sun the Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology the School of Control Science and Engineering Dalian University of TechnologyDalian 116024China

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 *** this paper,we implement the dynamic quantization technique to propose a novel hierarchical control strategy for nonlinear control systems under LTL *** 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 ***,we develop a dynamic quantization based approach to verify the realization of the accepting *** realization verification results in the necessity of the controller design and a sequence of quantization regions for the controller ***,the techniques of dynamic quantization and abstraction-based control are combined together to establish the local-to-global control *** abstraction construction and controller design are local and dynamic,thereby resulting in the potential reduction of the computational *** 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 ***,the proposed control strategy is illustrated via two examples from the path planning and tracking problems of mobile robots.

关键词： Abstraction-based control design dynamic quantization formal methods linear temporal logic(LTL)

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Data-Driven Modeling of PFR Kiln 9

Data-Driven Modeling of PFR Kiln

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9th International Conference on control, Decision and Information Technologies, CoDIT 2023

作者： Garcia-Tenorio, Camilo Wouwer, Alain Vande Systems Optimization Control and Optimization University of Mons Mons7000 Belgium

ISBN: (纸本)9798350311402

Modeling the different dynamics in parallel flow regenerative kilns for the production of quicklime is one of the steps towards obtaining the digital twins of the kilns. This paper shows a data-driven modeling approach based on the dynamic mode decomposition algorithm leveraged with a maximum likelihood method that gives linear and discrete-time models of the system. This allows capturing the transient dynamics of the process for the design of subsequent observers of the unknown dynamics, for the implementation of anomaly detection algorithms and eventual control and optimization of the manufacturing process. © 2023 IEEE.

关键词： Anomaly detection

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A Policy Iteration Algorithm for N-player General-Sum Linear Quadratic Dynamic Games 63

A Policy Iteration Algorithm for N-player General-Sum Linear...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Guan, Yuxiang Salizzoni, Giulio Kamgarpour, Maryam Summers, Tyler H. University of Texas at Dallas Control Optimization and Networks Lab United States Systems Control and Multi-Agent Optimization Research Lab Epfl Switzerland

ISBN: (纸本)9798350316339

We present a policy iteration algorithm for the infinite-horizon N -player general-sum deterministic linear quadratic dynamic games and compare it to policy gradient methods. We demonstrate that the proposed policy iteration algorithm is distinct from the Gauss-Newton policy gradient method in the N -player game setting, in contrast to the single-player setting where under suitable choice of step size they are equivalent. We illustrate in numerical experiments that the convergence rate of the proposed policy iteration algorithm significantly surpasses that of the Gauss-Newton policy gradient method and other policy gradient variations. Furthermore, our numerical results indicate that, compared to policy gradient methods, the convergence performance of the proposed policy iteration algorithm is less sensitive to the initial policy and changes in the number of players. © 2024 IEEE.

关键词： Numerical methods

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Aeroengine thrust estimation and embedded verification based on improved temporal convolutional network

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Chinese Journal of Aeronautics 2024年第1期37卷 106-117页

作者： Wanzhi MENG Zhuorui PAN Sixin WEN Pan QIN Ximing SUN Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of TechnologyDalian 116024China School of Control Science and Engineering Dalian University of TechnologyDalian 116024China

Thrust estimation is a significant part of aeroengine thrust control *** traditional estimation methods are either low in accuracy or large in *** further improve the estimation effect,a thrust estimator based on Multi-layer Residual Temporal Convolutional Network(M-RTCN)is *** solve the problem of dead Rectified Linear Unit(ReLU),the proposed method uses the Gaussian Error Linear Unit(GELU)activation function instead of ReLU in residual *** the overall architecture of the multi-layer convolutional network is adjusted by using residual connections,so that the network thrust estimation effect and memory consumption are further ***,the comparison with seven other methods shows that the proposed method has the advantages of higher estimation accuracy and faster convergence ***,six neural network models are deployed in the embedded controller of the micro-turbojet *** Hardware-in-the-Loop(HIL)testing results demonstrate the superiority of M-RTCN in terms of estimation accuracy,memory occupation and running ***,an ignition verification is conducted to confirm the expected thrust estimation and real-time performance.

关键词： Thrust estimation Temporal convolutional network Embedded deployment Hardware-in-the-loop testing Ignition verification

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An improved high-fidelity adaptive model for integrated inlet-engine-nozzle based on mechanismdata fusion

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Chinese Journal of Aeronautics 2024年第8期37卷 190-207页

作者： Chen WANG Ziyang YU Xian DU Ximing SUN School of Control Science and Engineering Dalian University of TechnologyDalian 116024China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of EducationDalian University of TechnologyDalian 116024China

Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion *** component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model,failing to meet the accuracy requirements of supersonic *** paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental ***,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is *** study reveals the inlet model’s efficacy in reflecting installed performance,flow matching,and mitigating pressure distortion,while the nozzle model accurately predicts flow coefficients and thrust coefficients,and identifies various operational *** model’s output closely aligns with typical experimental *** combining offline optimization and online adaptive correction,the mechanismdata fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight ***,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location *** approach significantly curbs performance deviations in supersonic *** example,at Ma=2.0,the system error impressively drops from 34.17%to merely 6.54%,while errors for other flight conditions consistently stay below the 2.95%*** findings underscore the clear superiority of the proposed method.

关键词： Aero-propulsion system Integrated inlet-enginenozzle Component-level model On-board adaptive model Mechanism-data fusion Extreme learning machine

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Deep reinforcement learning with reward design for quantum control

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1087-1101页

作者： Yu, Haixu Zhao, Xudong Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China

Deep reinforcement learning (DRL) has been recognized as a powerful tool in quantum physics, where DRL's reward design is nontrivial but crucial for quantum control tasks. To address the problem of over-reliance on human empirical knowledge to design DRL's rewards, we propose a DRL with a novel reward paradigm designed by the learning process information (DRL-LPI), where the learning process information (LPI) comprises the state information and the experiences. In DRL-LPI, the state information after being classified by a fidelity threshold, and the experiences are first stored simultaneously in the respective sequences, and this process is repeated until a similar-segment ends. Then, the stored state information is converted to the real value and used to design the reward value by applying a self-Amplitude function. Next, the designed reward values are integrated with the stored experiences to compose transitions for DRL's training. Through comparisons to five representative reward schemes, the proposed DRL-LPI is evaluated on two typical quantum control tasks, i.e., the spin-(1/2) quantum state control and many-coupled qubits state control, and the experimental results show the superior learning efficiency and control performance of the proposed *** results show that DRL-LPI exhibits the ability to learn the control strategy with few control actions compared to stochastic gradient descent (SGD) and genetic algorithm (GA). Impact Statement-Over the past few years, quantum machine learning has received growing attention. In particular, reinforcement learning (RL) and quantum physics have gradually intersected, and one representative aspect is that some impressive results have been achieved regarding the application of RL algorithms in quantum system tasks. Despite some advances, the full potential of RL remains massively unexplored in quantum physics. A major limitation is howto reward the learning *** previousworks adopted hand-designed methods, wh

关键词： Reinforcement learning

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Isolating Signatures of Cyberattacks under Stressed Grid Conditions 50

Isolating Signatures of Cyberattacks under Stressed Grid Con...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Ghosh, Sanchita Naqvi, Syed Ahsan Raza Nandanoori, Sai Pushpak Kundu, Soumya Optimization & Control Group Pacific Northwest National Laboratory United States

ISBN: (纸本)9781665464543

In a controlled cyber-physical network, such as a power grid, any malicious data injection in the sensor measurements can lead to widespread impact due to the actions of the closed-loop controllers. While fast identification of the attack signatures is imperative for reliable operations, it is challenging to do so in a large dynamical network with tightly coupled nodes. A particularly challenging scenario arises when the cyberattacks are strategically launched during a grid stress condition, caused by non-malicious physical disturbances. In this work, we propose an algorithmic framework - based on Koopman mode (KM) decomposition - for online identification and visualization of the cyberattack signatures in streaming time-series measurements from a power network. The KMs are capable of capturing the spatial embedding of both natural and anomalous modes of oscillations in the sensor measurements and thus revealing the specific influences of cyberattacks, even under existing non-malicious grid stress events. Most importantly, it enables us to quantitatively compare the outcomes of different potential cyberattacks injected by an attacker. The performance of the proposed algorithmic framework is illustrated on the IEEE 68-bus test system using synthetic attack scenarios. Such knowledge regarding the detection of various cyberattacks will enable us to devise appropriate diagnostic scheme while considering varied constraints arising from different attacks. © 2024 IEEE.

关键词： Cybersecurity

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Physically informed hierarchical learning based soft sensing for aero-engine health management unit

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Chinese Journal of Aeronautics 2025年第3期38卷 374-385页

作者： Aina WANG Pan QIN Yunbo YUAN Guang ZHAO Ximing SUN School of Control Science and Engineering Dalian University of TechnologyDalian 116024China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian 116024China School of Energy and Power Engineering Dalian University of TechnologyDalian 116024China

Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management *** existing Physics-Informed Neural Networks(PINNs)have made ***,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs *** this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is ***,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are *** is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU ***,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss ***,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed ***,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’***,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft ***,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.

关键词： Hierarchical learning strategy Monitoring:Partial differen tial equations with unmeasurable driving terms Physically informed hierarchical learning followed by recurrent-prediction term Soft sensing

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