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Adaptive Safety-Certified Reinforcement Learning for Constrained Optimal Control of Autonomous Robots With Uncertainties

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IEEE Internet of Things Journal 2025年

作者： Zhang, Fei Yang, Guang-Hong Northeastern University College of Information Science and Engineering Liaoning Shenyang110819 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110819 China

This paper investigates a constrained optimal control problem for safety-critical robots with parametric uncertainties. A novel adaptive safety-certified reinforcement learning (RL) algorithm is proposed, leveraging control barrier functions (CBFs) to enable safe learning of the optimal policy during the online exploration phase. Specifically, a high-order robust adaptive CBF is presented to minimally adjust RL-derived control actions by incorporating a prescribed-time adaptation law to handle the unknown system parameters. This way directly enforces forward invariance, allowing the shrunken safe set to near the standard set within a user-prescribed time. Moreover, a novel adaptive critic learning frame is presented by introducing filtered auxiliary signals that integrate both instantaneous and historical data, which relaxes the strict persistent excitation (PE) condition required in the existing RL methods to a weaker, easily verifiable finite excitation (FE) condition. Later, a prescribed-time learning rule is developed to accelerate the convergence of weights. The key advantage of the proposed way is the decoupling of safety and RL convergence, enabling each component to be managed separately, thereby offering stronger safety certifications compared to the existing RL schemes even under uncertain dynamics. The effectiveness and superiority of the proposed scheme are proven via simulations for surveillance and regulation tasks of autonomous robots. © 2014 IEEE.

关键词： Adversarial machine learning

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Resilient Control Under Denial-of-service and Uncertainty: An Adaptive Dynamic Programming Approach

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IEEE Transactions on Automatic Control 2025年第6期70卷 4085-4092页

作者： Gao, Weinan Jiang, Zhong-Ping Chai, Tianyou Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China New York University Department of Electrical and Computer Engineering Tandon School of Engineering BrooklynNY11201 United States

In this paper, a new framework for the resilient control of continuous-time linear systems under denial-of-service (DoS) attacks and system uncertainty is presented. Integrating techniques from reinforcement learning and output regulation theory, it is shown that resilient optimal controllers can be learned directly from real-time state and input data collected from the systems subjected to attacks. Sufficient conditions are given under which the closed-loop system remains stable given any upper bound of DoS attack duration. Simulation results are used to demonstrate the efficacy of the proposed learning-based framework for resilient control under DoS attacks and model uncertainty. © 1963-2012 IEEE.

关键词： Denial-of-service attack

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A Lagrangian Mesh-Free Multi-Phase Model for Simulating Complex Dynamics of Bubbly Flows

MetaResource

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MetaResource 2025年第1期2卷 11-32页

作者： Ying Tian Xingyan Guo Qiang Zhang Zhengchang Shen Xiangwei Dong Chunzhi Li Kun Cao College of Mechanical and Electronic Engineering Shandong University of Science and Technology Qingdao China BGRIMM TECHNOLOGY GROUP Beijing China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Zhongtianhechuang Energy Co. Ltd. Ordos China

Smoothed particle hydrodynamics (SPH) is a Lagrangian, mesh-free numerical method renowned for its ability to handle fluid dynamics problems with large interface deformations and multiphase flow coupling. This study introduces an SPH-based multiphase flow model for simulating bubbly flows involving various immiscible fluids. The model uses a volume-based density discretization equation, ensuring numerical accuracy near interfaces, independent of density ratios, thus maintaining accuracy and stability even at high density ratios. By integrating the Continuous Surface Force method for surface tension into the multiphase SPH model, the study simulates interfacial behavior between phases. The model accurately predicts Laplace pressure differences across interfaces, maintaining interface stability at density ratios up to 100.0. Simulations of single and double bubble ascents elucidate the influence of the Bond number on bubble shape, rising distance, and velocity. As the Bond number increases, bubbles flatten and develop tails, affecting their integrity. The study also simulates multiple bubbles ascents in water, showcasing the model's ability to capture complex interfacial behaviors in bubbly flows, including deformation, adsorption, coalescence, and tearing.

关键词： Deformable models Accuracy Deformation Force Hydrodynamics Mathematical models Stability analysis Surface tension Numerical models Numerical stability

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ZnS:Cu-based fluorescence and surface plasmon resonance triple channel sensor for simultaneous detection of ultraviolet intensity, humidity and temperature

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Fu, Rao Chen, Xiaobing Li, Haihui Tang, Huina Qu, Yuhan Yin, Zhiyuan Zhang, Qi Cheng, Tonglei Zhou, Xue Yan, Xin Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

With the increasing demand for more powerful sensors, rapid development had been achieved. In this paper, a fluorescence(FL) and surface plasmon resonance(SPR) triple channel sensor was designed based on wavelength division multiplexing(WDM). First, ZnS:Cu powder was mixed inside polydimethylsiloxane(PDMS) to detect ultraviolet intensity(UVI) and temperature as channel 1. Second, we coated the thin core fiber(TCF) with 40 nm Ag film and carboxymethyl cellulose (CMC) to detect relative humidity(RH) as channel 2. Third, another part of TCF was coated with 40 nm Ag, 4 nm ZnO film and PDMS also to detect UVI and temperature as channel 3. The detection range and compensation accuracy of UVI, RH and T are 3.74-26.18 mW/cm2, 1.016;20%-100% RH, 1.022 and 5-41 °C, 1.017. All the channels could be compensated to isolate single variation during simultaneous change. Compared with previous work, the first introduction of fluorescence signal into SPR sensor in this paper achieves three channels integration inside one single fiber structure, and all three parameters could be monitored simultaneously. The advantages of proposed sensor such as high accuracy, stability and multi-parameter simultaneous sensing could be quite useful in future environmental monitoring field. © 1963-2012 IEEE.

关键词： Polydimethylsiloxane

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Distributed Time-Varying Constrained Convex Optimization: Finite-/Fixed-Time Convergence

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IEEE Transactions on Control of Network Systems 2025年

作者： Guo, Ge Zhou, Zeng-Di Zhang, Renyongkang Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University School of Control Engineering Qinhuangdao066004 China Northeastern University College of Information Science and Engineering Shenyang110819 China

This paper investigates a distributed time-varying optimization problem with inequality constraints, aiming to find finite- and fixed-time convergent solutions free from initialization. A nonsmooth optimization algorithm for state consensus achieving within a finite or fixed time is presented, by designing a projection-based log-barrier penalty cost function to meet the constraints and introducing integral sliding mode subsystems to guarantee zero- gradient-sum. With the use of the projection idea, the penalized functions are always well-defined (i.e., satisfying the logarithmic definition) for any system states, which avoids initializing of certain parameters. An adaptive gain scheme without any extra global information is presented. The time-varying zero- gradient-sum method here is feasible for cost functions with nonidentical Hessian matrixes, and applicable to finite or fixed time optimal consensus tracking. The effectiveness and superiority of our algorithms are verified with numerical simulations. © 2014 IEEE.

关键词： Cost functions

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Domain transfer spatiotemporal graph network for predicting key indicators in multimode chemical processes

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Chemical engineering Science 2025年 314卷

作者： Zhu, Jialiang Yang, Chao Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

In modern chemical industries, soft sensors related to key indicators are crucial in production processes, particularly for multimode chemical processes. However, challenges arise from scarce labeled data and constant process shifts, which complicate the development of these sensors. Additionally, complex process variable interactions hinder prediction interpretability. This work develops a domain transfer spatiotemporal graph network (DTSGN) regression model, tailored for cross-domain modeling in multimode chemical processes. The DTSGN model first employs graph convolution to capture spatial dependencies and gating mechanism to track temporal relationships. A Bernoulli-distributed perturbation graph enhances model robustness. By embedding constraints into conditional distribution discrepancy, the parameters for DTSGN are calibrated under the fine-tuning paradigm for new mode. Therefore, the model extends its applicability to address process shifts in multimode chemical processes. A graph explainer finally ensures interpretability by aligning learned variable graph with process knowledge, and two multimode cases evidence the proposed model's efficacy and superiority. © 2025 Elsevier Ltd

关键词： Chemical industry

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Data-Driven Attack Detection and Resilient Control for Heterogeneous Nonlinear Nonaffine MASs

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IEEE Transactions on Network Science and engineering 2025年

作者： Yang, Chen Che, Wei-Wei Qingdao University School of Automation Shandong Key Laboratory of Industrial Control Technology Qingdao266071 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang China

The data-driven attack detection and resilient control problems for heterogeneous nonlinear nonaffine multi-agent systems (MASs) in the presence of false data injection (FDI) attacks are investigated in this paper. The heterogeneous nonlinear nonaffine MASs are firstly translated to an equivalent linear data model by using the dynamic linearization method. Then, based on the obtained linear data model, a novel attack detector, by using the nonlinear autoregressive with exogenous input neural network (NARXNN), is designed to detect whether MASs are under FDI attacks. Further, a data-driven resilient switched control scheme with the attack compensation mechanism is designed to achieve the leaderless consensus control objective. Finally, a simulation is given to illustrate the efficiency of the proposed method by comparisons. © 2013 IEEE.

关键词： Authorization

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Improved Spacing Policy-Based Predefined-Time Cooperative Control of Vehicles

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Qian Guo, Ge Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China

This paper studies the problem of cooperative lateral-longitudinal control for a collection of vehicles. To eliminate the limitation of zero initial spacing errors, a novel spacing policy is introduced. Then, a fast vehicular cooperative control method is proposed based on the new spacing policy, which can ensure internal stability, string stability and traffic flow stability. The algorithm is derived via the use of sliding mode control with a predefined-time terminal sliding mode surface. The efficiency and feasibility of the algorithm are demonstrated by numerical simulations. © 1967-2012 IEEE.

关键词： Autonomous vehicles

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Noniterative Wet Gas Flow Metering in Annular Mist Flow by Combining Vortex Meter and Liquid Film Parameters

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Li, Jinxia Lin, Ji Huang, Yi Ding, Hongbing Sun, Hongjun Civil Aviation University of China College of Electronic Information and Automation Tianjin300300 China Tianjin University Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin300072 China

Wet gas metering in annular mist flow is important for it is typically used in fiscal metering, and the traditional iteration-based methods are limited due to their sensitivity to initial values and time-consuming in the iteration process. To improve the real-time performance and reliability, this study tries to develop noniterative wet gas metering method based on the vortex meter-conductance dual-modality system. To adapt to different application scenarios, two models are proposed, named nonlinear regression model and neural network model, respectively. The tests are conducted on a DN 15 vertical pipeline with a gas flow rate range of 12–24 m3/h and liquid volume fraction within 1.30‰. To obtain the flow parameters of liquid film thickness, disturbance wave frequency, and velocity simultaneously, the dual-ring liquid film sensor is designed and optimized. The input features are selected by the importance ranking of the correlation heatmap, and the optimized hyperparameters and K-fold cross-validation ensure the generalization of the trained neural network. Finally, the predicted performances are evaluated and compared in detail. It indicates that mean film thickness could be regarded as the unique scale parameter of vortex meter overreading (OR) and OR = 1 + 2.64δm/Dgives a predicted error of ±1.5% for gas flow rate. Liquid flow could be directly predicted by combining different liquid film flow parameters, and Usl= 535.62 f0.214DW D 2.034δ 0.655m V2.090DW gives a predicted full-scale error of ±5.0% for liquid flow rate. The neural network model gives satisfactory prediction both for gas and liquid flow rates, gas flow accuracy within ±1.0% error bands, and liquid flow within ±1.5% full-scale error bands. Finally, comparisons and practical suggestions for the application are given for the two proposed noniterative wet gas metering models. © 1963-2012 IEEE.

关键词： Flow of gases

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Reinforcement Learning-Based Optimal Control for Continuous-Time Quantized Systems via Adaptive Dynamic Programming

Reinforcement Learning-Based Optimal Control for Continuous-...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Omar Qasem Weinan Gao Omar R. Daoud Electrical and Computer Engineering Department School of Engineering and Computing American International University Al-Jahra Kuwait State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Lianning China

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Ahstract- This paper presents a comprehensive investigation into the stability analysis and design conditions for quantizers to ensure the closed-loop stability of continuous-time linear quantized systems, under the conditions derived by the small-gain theorem. The study begins by deriving explicit bounds on the quantizer parameters required for maintaining system stability. Building on this foundation, an optimal controller is designed using the linear quadratic regulator (LQR) framework, providing an efficient data-driven control strategy. To further enhance the system's performance, an adaptive dynamic programming (ADP) algorithm, referred to as the hybrid iteration (HI) method, is developed. This algorithm effectively learns the optimal control policy by leveraging the trajectories of the quantized states and inputs, thereby addressing the challenges posed by quantization constraints. The proposed HI approach combines the advantages of adaptive learning and optimization, making it well-suited for continuous-time systems with limited information. The simulation results confirm that the ADP approach with the provided conditions not only stabilizes the quantized system but also achieves optimal control performance under the specified quantization conditions. This study offers valuable insights and a robust methodological framework for addressing stability and control challenges, with insights to be expanded to continuous-time nonlinear quantized systems, with potential applications in various engineering domains, such as networked systems, robotics and autonomous systems.

关键词： Quantization (signal) Adaptive systems Regulators Simulation Heuristic algorithms Optimal control Stability analysis Dynamic programming Trajectory Thermal stability

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