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Variable Reconstruction for Evolutionary Expensive Large-Scale Multiobjective Optimization and Its Application on Aerodynamic Design

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ieee-CAA JOURNAL OF AUTOMATICA SINICA 2025年第4期12卷 719-733页

作者： Lin, Jianqing He, Cheng Tian, Ye Pan, Linqiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

Expensive multiobjective optimization problems (EMOPs) are complex optimization problems exacted from real-world applications, where each objective function evaluation (FE) involves expensive computations or physical experiments. Many surrogate-assisted evolutionary algorithms (SAEAs) have been designed to solve EMOPs. Nevertheless, EMOPs with large-scale decision variables remain challenging for existing SAEAs, leading to difficulties in maintaining convergence and diversity. To address this deficiency, we proposed a variable reconstruction-based SAEA (VREA) to balance convergence enhancement and diversity maintenance. Generally, a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables. Thus, the population can be rapidly pushed towards the Pareto set (PS) by optimizing low-dimensional weight variables with the assistance of surrogate models. Population diversity is improved due to the cluster-based variable reconstruction strategy. An adaptive search step size strategy is proposed to balance exploration and exploitation further. Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task. Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.

关键词： Optimization computational modeling Iron Convergence Linear programming Predictive models Particle swarm optimization Kernel Costs Clustering algorithms Aerodynamic design large-scale optimization multiobjective evolutionary algorithm surrogate model variable reconstruction

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Multi-Modal Battel Royale Optimizer Based Optimal control of Industrial Robots

Multi-Modal Battel Royale Optimizer Based Optimal Control of...

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2024 International Conference on Intelligent Algorithms for computational intelligence Systems, IACIS 2024

作者： Yang, Meixia Yin, Qiaoling Guangzhou Songtian Polytechnic College Guangzhou China

ISBN: (纸本)9798350360660

Many industrial robots are given through manufacturer with the controller which can't changed through user. This kind of arrangement is generally determined to nearest control structure, that is not probable for processing the arbitrary control laws. To implement the novel algorithms, that is on contrary essential for assigning the open control structure, that allowed the program the control scheme. In this research, developed the Multi-Modal Battel Royale Optimizer (MM-BRO) algorithm for industrial robot optimization control. To find the appropriate neighbors the Coulomb's law is used as the multi modal solution. That represents that solution with high beneficial location have higher chance of success. The MM-BRO algorithm enhances the performance of industrial robot optimization control. The developed MM-BRO algorithm reached 4.36 on K_p′, 0.23 on K_D′, 7.76 on Kp, 21.67 on KI and 0.06 on KD which is effective while compared to various optimization algorithms like Remora Optimization Algorithm (ROA) and Reptile Search Algorithm (RSA). © 2024 ieee.

关键词： Industrial robots

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Boost Converter Modeling and control based on its Role within a Microgrid 16

Boost Converter Modeling and Control based on its Role withi...

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16th ieee International symposium on Applied computational intelligence and Informatics, SACI 2022

作者： Rat, Cezara-Liliana Prostean, Octavian Filip, Ioan Ichim-Burlacu, Catalin Politehnica University of Timisoara Department of Electrical Engineering and Industrial Informatics Timisoara Romania Politehnica University of Timisoara Department of Automation and Applied Informatics Timisoara Romania

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

The aim of this paper is to present an alternative method of converter modeling. This method is based on the classical state-space modeling method and it consists of using certain simplifications throughout the modeling process which are based on the configuration in which the converter is to be used within the energy conversion system or microgrid. This method was designed with the purpose of simplifying the conventional process of obtaining a linear model through state-space modeling. The reason for it was the fact that most converter state-space models are nonlinear and require linearization around an operating point in order to be used for control structure design. The method presented in this paper is more likely to provide a linear model than the classical method. © 2022 ieee.

关键词： Boost converter

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A Hybrid Design of Fault Detection for Nonlinear Systems Based on Dynamic Optimization

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ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5244-5254页

作者： Ran, Guangtao Chen, Hongtian Li, Chuanjiang Ma, Guangfu Jiang, Bin Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G IH9 Canada Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Jiangsu Key Lab Internet Things & Control Technol Nanjing 211106 Peoples R China

To ensure the safety of an automation system, fault detection (FD) has become an active research topic. With the development of artificial intelligence, model-free FD strategies have been widely investigated over the past 20 years. In this work, a hybrid FD design approach that combines data-driven and model-based is developed for nonlinear dynamic systems whose information is not known beforehand. With the aid of a Takagi-Sugeno (T-S) fuzzy model, the nonlinear system can be identified through a group of least-squares-based optimization. The associated modeling errors are taken into account when designing residual generators. In addition, statistical learning is adopted to obtain an upper bound of modeling errors, based on which an optimization problem is formulated to determine a reliable FD threshold. In the online FD decision, an event-triggered strategy is also involved in saving computational costs and network resources. The effectiveness and feasibility of the proposed hybrid FD method are illustrated through two simulation studies on nonlinear systems.

关键词： Optimization Observers Nonlinear dynamical systems Generators automation Learning systems Fault detection Fault detection (FD) hybrid designs nonlinear dynamic systems optimization learning Takagi-Sugeno (T-S) fuzzy models

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Real-Time Neural MPC: Deep Learning Model Predictive control for Quadrotors and Agile Robotic Platforms

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ieee ROBOTICS AND automation LETTERS 2023年第4期8卷 2397-2404页

作者： Salzmann, Tim Kaufmann, Elia Arrizabalaga, Jon Pavone, Marco Scaramuzza, Davide Ryll, Markus Tech Univ Munich D-85521 Munich Germany Univ Zurich CH-8050 Zurich Switzerland Stanford Univ Stanford CA 94305 USA NVIDIA Res Stanford CA 94305 USA Tech Univ Munich D-85521 Munich Germany Munich Inst Robot & Machine Intelligence MIRMI D-80992 Munich Germany

Model Predictive control (MPC) has become a popular framework in embedded control for high-performance autonomous systems. However, to achieve good control performance using MPC, an accurate dynamics model is key. To maintain real-time operation, the dynamics models used on embedded systems have been limited to simple first-principle models, which substantially limits their representative power. In contrast to such simple models, machine learning approaches, specifically neural networks, have been shown to accurately model even complex dynamic effects, but their large computational complexity hindered combination with fast real-time iteration loops. With this work, we present Real-time Neural MPC, a framework to efficiently integrate large, complex neural network architectures as dynamics models within a model-predictive control pipeline. Our experiments, performed in simulation and the real world onboard a highly agile quadrotor platform, demonstrate the capabilities of the described system to run learned models with, previously infeasible, large modeling capacity using gradient-based online optimization MPC. Compared to prior implementations of neural networks in online optimization MPC we can leverage models of over 4000 times larger parametric capacity in a 50 Hz real-time window on an embedded platform. Further, we show the feasibility of our framework on real-world problems by reducing the positional tracking error by up to 82% when compared to state-of-the-art MPC approaches without neural network dynamics.

关键词： Machine learning for robot control model learning for control aerial systems Mechanics and control

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Generating Cardiovascular Data by Integration of Physiological Information

Generating Cardiovascular Data by Integration of Physiologic...

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2025 ieee symposium on computational intelligence in Health and Medicine, CIHM 2025

作者： Kummert, Johannes Schulz, Alexander Habigt, Moriz Stemmler, Maike Kohler, Christina Abel, Dirk Rossaint, Rolf Hammer, Barbara Machine Learning Group Bielefeld University Bielefeld Germany Anaesthesiology Clinic RWTH Aachen University Aachen Germany Institute of Automatic Control RWTH Aachen University Aachen Germany

ISBN: (纸本)9798331508333

Medical data is a valuable resource that is under privacy constraints and often requires a substantial effort and cost to obtain. This is the case for physiological cardiovascular data that is among others utilized to develop assistive heart devices. In the present work, we aim to improve a GAN based generative approach from the literature by integrating physiological knowledge in form of pressure-value curves together with a dynamic time warping cost function into the model. We demonstrate that our adapted model generates more realistic cardiovascular signals as measured by targeted physiological markers and showcase that such generated signals can be used to improve a downstream task on real data. © 2025 ieee.

关键词： Heart

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Road Disease Damage Detection Based on Improved CenterNet 39

Road Disease Damage Detection Based on Improved CenterNet

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39th Youth Academic Annual Conference of Chinese-Association-of-automation (YAC)

作者： Wei, Yuanzheng Zhou, Yingjiang Lu, Ming Han, Shihao Xie, Minghui Gao, Lu Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Nanjing Peoples R China Beijing Inst Control Engn Beijing Peoples R China

ISBN: (纸本)9798350390780;9798350379228

Road damage detection is an important step in ensuring road safety and achieving timely repair of road damage, which is of great significance in reducing road maintenance costs. Deep learning technology has developed rapidly this year, and using computer vision technology instead of manual detection can achieve automated detection of road losses. However, there are still a series of problems such as low detection efficiency and poor image quality. In response to the above issues, in this article, we first established a road dataset, including 2575 Chinese road images and 3589 road damage annotations. Then, we proposed an improved model based on CenterNet, using the Asymptotic Feature Pyramid structure. Using this method, we fused two adjacent low-level features, and gradually fused the high-level features. In the feature fusion process, adaptive spatial fusion is utilized to avoid multi-target information conflict, and then the CLA cross-layer attention mechanism is utilized to improve the performance of small-target detection in the model and reduce the computational amount of the model. Finally, it is experimentally demonstrated that our improvement improves the detection accuracy and the computational cost is within a manageable range.

关键词： Deep Learning Object Detection CenterNet Ensemble models

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Low-Cost Automated Visual Screw Inspection System

Low-Cost Automated Visual Screw Inspection System

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2023 ieee symposium Series on computational intelligence, SSCI 2023

作者： Li, Yiran Li, Jiayi Yang, Xiaoying Li, Cheng'ao Xiong, Xihan Fang, Yutong Ding, Shusheng Cui, Tianxiang School of Computer Science University of Nottingham Ningbo China Ningbo China School of Aerospace Engineering University of Nottingham Ningbo China Ningbo China Imperial College London Deparment of Computing London United Kingdom Ningbo Open University Teaching Research Center Ningbo China Business School Ningbo University Ningbo China

ISBN: (纸本)9781665430654

Despite the significant achievements in the development of automation technologies, the application of autonomous robots to improve the production efficiency of small-scale indus-tries has been largely ignored. While there has been excellent progress in industrial image processing systems implementation, most of the work has focused on a unique aspect of specific objects rather than introducing a general inspection system. Thus, this paper discusses the critical industrial topic of quality control, which develops rapidly through the use of autonomous systems. Given the high cost of implementing automated systems, this paper presents an affordable low-budget solution for the visual inspection system. This method of inspecting screw dimensions consists of four visual inspection parts and a special mechanical supporting structure. The designed system was able to check the overall screw dimensions, including screw head diameter, screw head driven type, screw length, screw thread length, and screw head thickness. It could also separate the qualified screws from the unqualified ones after the inspection process. The accuracy of most inspection cases is 100%, meaning the error ranges within 0.1mm, which meets all the non-negotiable requirements and most of the target requirements. The visual inspection parts can be further enhanced by building a template matching library that includes different angles of the screw head or by using Hough Transform to identify the defect types of the screw thread. © 2023 ieee.

关键词： Automated quality control Computer-assisted visual inspection Industrial computer vision Low-cost

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Finite-time Sliding Mode control Method for a Class of Air Intake Testing Devices 4

Finite-time Sliding Mode Control Method for a Class of Air I...

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4th International symposium on Artificial intelligence and Intelligent Manufacturing, AIIM 2024

作者： Li, Yunli Pu, Ming Zhang, Gexiang Dan, Zhihong Zhang, Song School of Automation Chengdu University of Information Technology Chengdu China Science and Technology on Altitude Simulation Laboratory AECC Sichuan Gas Turbine Establishment Mianyang China

ISBN: (纸本)9798331541729

This paper proposes a novel finite-time sliding mode control method for the enhancement of the velocity and precision in the control of temperature and pressure within a class of air intake testing devices. Initially, a simulation model of the device without heat transfer characteristics is constructed in MATLAB, followed by the design of a sliding mode control strategy for the device. Then an auxiliary function characterized by a normal distribution is introduced, facilitating the formulation of a novel finite-time reaching law and an integral sliding mode surface, and culminating in the derivation of a finite-time sliding mode controller. Employing the Lyapunov stability theorem, it is demonstrated that the system state attains its equilibrium point within a finite time. Finally, three different reaching laws are used with the proposed sliding mode surface to construct the three controllers. Simulation experiments are conducted in the simulation model using the aforementioned three sliding mode controllers. The simulation results indicate that the sliding mode controller under the influence of the reaching laws presented in this paper significantly enhances the servo tracking performance of temperature and pressure. © 2024 ieee.

关键词： MATLAB

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A Knowledge Acquisition Framework for Autonomous Decision Making in Service Robots

A Knowledge Acquisition Framework for Autonomous Decision Ma...

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2023 ieee symposium Series on computational intelligence, SSCI 2023

作者： Wu, Hao Zhao, Zhixian Ma, Qing Tian, Guohui School of Control Science and Engineering Shandong University Shandong Province China

ISBN: (纸本)9781665430654

Service robots are expected to autonomously perform a wide range of service tasks to satisfy users' needs, but are limited in practice by their weak decision-making capabilities. This work introduces a Knowledge Acquisition Framework (KAFS) to help robots make autonomous decisions through this knowledge. This framework is divided into two parts: service knowledge acquisition and scene knowledge construction and uses a variety of intelligent methods to easily and accurately acquire a large amount of service and scene knowledge. We demonstrate the knowledge acquired by KAFS and validate the effectiveness of KAFS on robot service tasks. © 2023 ieee.

关键词： decision making knowledge acquisition service robot

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