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Model Predictive control based Motion Cueing Algorithm for Driving Simulator

Journal of systems Science and systems Engineering 2024年第5期33卷 607-626页

作者： Ayesha Hameed Ali Soltani Sharif Abadi Andrzej Ordys Institute of Automatic Control and Robotics Warsaw University of TechnologyWarsawPoland Institute of Electronic Systems Warsaw University of TechnologyWarsawPoland

Thanks to the emerging integration of algorithms and simulators, recent Driving Simulators (DS) find enormous potential in applications like advanced driver-assistance devices, analysis of driver’s behaviours, research and development of new vehicles and even for entertainment purposes. Driving simulators have been developed to reduce the cost of field studies, allow more flexible control over circumstances and measurements, and safely present hazardous conditions. The major challenge in a driving simulator is to reproduce realistic motions within hardware constraints. Motion Cueing Algorithm (MCA) guarantees a realistic motion perception in the simulator. However, the complex nature of the human perception system makes MCA implementation challenging. The present research aims to improve the performance of driving simulators by proposing and implementing the MCA algorithm as a control problem. The approach is realized using an actual vehicle model integrated with a detailed model of the human vestibular system, which accurately reproduces the driver’s perception. These perception motion signals are compared with simulated ones. A 2-DOF stabilized platform model is used to test the results from the two proposed control strategies, Proportional Integrator and Derivative (PID) and Model Predictive control (MPC).

关键词： Driving simulator motion cueing algorithm control theory model predictive control PID

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Hybrid Strategy of Partitioned and Monolithic Methods for Solving Strongly Coupled Analysis of Inverse and Direct Piezoelectric and Circuit Coupling

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1371-1386页

作者： Daisuke Ishihara Syunnosuke Nozaki Tomoya Niho Naoto Takayama Department of Intelligent and Control Systems Kyushu Institute of TechnologyFukuokaJapan

The inverse and direct piezoelectric and circuit coupling are widely observed in advanced electro-mechanical systems such as piezoelectric energy *** strongly coupled analysis methods based on direct numerical modeling for this phenomenon can be classified into partitioned or monolithic *** formulation has its advantages and disadvantages,and the choice depends on the characteristics of each coupled *** study proposes a new option:a coupled analysis strategy that combines the best features of the existing formulations,namely,the hybrid partitioned-monolithic *** analysis of inverse piezoelectricity and the monolithic analysis of direct piezoelectric and circuit interaction are strongly coupled using a partitioned iterative hierarchical *** a typical benchmark problem of a piezoelectric energy harvester,this research compares the results from the proposed method to those from the conventional strongly coupled partitioned iterative method,discussing the accuracy,stability,and computational *** proposed hybrid concept is effective for coupled multi-physics problems,including various coupling conditions.

关键词： Structure-piezoelectric-circuit interaction energy harvesting partitioned method monolithic method hybrid method

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Characteristic model-based control of aerial manipulator for improved interaction stability

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Science China(Technological Sciences) 2025年第4期68卷 166-178页

作者： Bingkai XIU Zhan LI Yipeng YANG Huijun GAO Research Institute of Intelligent Control and Systems Harbin Institute of TechnologyHarbin150001China State Key Laboratory of Robotics and Systems(HIT) Harbin Institute of TechnologyHarbin150001China

Maintaining contact stability is crucial when the aerial manipulator interacts with the surrounding environment. In this paper, a novel output feedback framework based on a characteristic model is proposed to improve the contact stability of the aerial manipulator. First, only position measurements of the aerial manipulator are introduced to design the practical finite-time command filter-based force observer. Second, an attitude control architecture including characteristic modeling and controller design is presented. In the modeling part, input-output data is utilized to build the characteristic model with fewer parameters and a simpler structure than the traditional dynamic model. Different from conventional control methods, fewer feedback values,namely only angle information, are required for designing the controller in the controller part. In addition, the convergence of force estimation and the stability of the attitude control system are proved by the Lyapunov analysis. Numerical simulation comparisons are conducted to validate the effectiveness of the attitude controller and force observer. The comparative results demonstrate that the tracking error of x and θ channels decreases at least 10.62% and 10.53% under disturbances and the force estimation precision increases at least 45.19% in the different environmental stiffness. Finally, physical flight experiments are conducted to validate the effectiveness of the proposed framework by a self-built aerial manipulator platform.

关键词： aerial manipulator interaction stability contact force observer characteristic model

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TOGA-Based Fuzzy Grey Cognitive Map for Spacecraft Debris Avoidance

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IEEE Transactions on Fuzzy systems 2025年第6期33卷 1793-1802页

作者： Qin, Chenhui Liu, Yuanshi Wang, Tong Qiu, Jianbin Li, Min Harbin Institute of Technology Research Institute of Intelligent Control and Systems Harbin150080 China Beijing Institute of Control Engineering Beijing100094 China

The optimization of fuzzy grey cognitive map (FGCM) can enhance the decision quality of the system in managing uncertainties and incomplete information. Addressing this issue requires a method that effectively balances the competing demands of the speed and precision in the optimization process. Therefore, a trade-off genetic algorithm (TOGA) is proposed to refine the FGCM optimization process in this paper. Firstly, a modified fitness function with a penalty term is designed to improve the convergence rate, which can drive the FGCM population to obtain the optimal solution during the evolutionary process. Secondly, an adaptive genetic mechanism based on horizontal comparison and longitudinal assessment, is designed to strike a balance between accelerating convergence and avoiding the dilemma of falling into local optima. Finally, in the simulation section, the effectiveness of the proposed method is validated by optimizing FGCM using synthetic datasets and applying to spacecraft debris threat avoidance scenario. © 1993-2012 IEEE.

关键词： Space debris

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Algorithm Selection Method Based on Coupling Strength for Partitioned Analysis of Structure-Piezoelectric-Circuit Coupling

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1237-1258页

作者： Daisuke Ishihara Naoto Takayama Department of Intelligent and Control Systems Kyushu Institute of TechnologyIizuka820-8502Japan

In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis ofstructure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct piezoelectriccoupling and direct piezoelectric and circuit coupling. In the proposed method, implicit and explicit formulationsare used for strong and weak coupling, respectively. Three feasible partitioned algorithms are generated, namely(1) a strongly coupled algorithm that uses a fully implicit formulation for both types of coupling, (2) a weaklycoupled algorithm that uses a fully explicit formulation for both types of coupling, and (3) a partially stronglycoupled and partially weakly coupled algorithm that uses an implicit formulation and an explicit formulation forthe two types of coupling, *** examples using a piezoelectric energy harvester,which is a typicalstructure-piezoelectric-circuit coupling problem, demonstrate that the proposed method selects the most costeffectivealgorithm.

关键词： Multiphysics coupling strength partitioned algorithm structure-piezoelectric-circuit coupling strongly coupled algorithm weakly coupled algorithm

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A multistage intrusion detection method for alleviating class overlapping problem

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Neural Computing and Applications 2025年第6期37卷 4853-4867页

作者： Pang, He Jin, Fusheng Chen, Mengnan Jiang, Yutong Yuan, Ye Beijing Aerospace Automatic Control Institute Beijing100854 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

Intrusion detection system (IDS) can identify abnormal network traffic and attacks, which is an important means of network security defense. However, some intrusion data are often disguised as normal data for transmission, which increases the difficulty of intrusion data classification. In addition, the existing packet-based or flow-based data feature extraction methods result in low feature dimensions, causing the problem of class overlapping between different categories with the same features. To clarify, overlapping samples are those that overlap between erroneous samples and correct samples. Nonoverlapping samples are those in the test set that do not match the characteristics of the already identified overlapping samples and are therefore considered nonoverlapping samples. Therefore, the detection effect of some attacks with high concealment is poor. In order to solve the above problems, this paper proposes a multistage intrusion detection method: an existing intrusion detection model with higher classification performance (OBLR) is used to predict the data in the first stage. In the second stage, for the overlapping data in the confusing data, the method learns the distribution of each feature group according to the randomly divided "intermediary set," and realizes the prediction of overlapping samples through the prior distribution knowledge, and achieves efficient classification of overlapping samples without increasing the computational burden of the model. For nonoverlapping data in the confusing data, KPCA (kernel principal component analysis) dimension elevation is used in the third stage to capture more detailed difference information between samples, and GMM (Gaussian mixed model) is combined with the "representative samples" proposed in this paper to assist classifier classification. At the same time, all the base classifiers are integrated through LTR (learning to rank) to improve the classification effect of the model for nonoverlapping data in the

关键词： Network intrusion

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SELECTION STRATEGIES FOR CENTRALIZED ACTIVE STRUCTURAL ACOUSTIC control OF A CASING BASED ON SYSTEM IDENTIFICATION DATA 30

SELECTION STRATEGIES FOR CENTRALIZED ACTIVE STRUCTURAL ACOUS...

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30th International Congress on Sound and Vibration, ICSV 2024

作者： Hafez, Amir Pawelczyk, Marek Wrona, Stanislaw Institute of Automatic Control Department of Measurements and Control Systems Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9789090390581

Active Structural Acoustic control (ASAC) is a technique designed to minimize the acoustic radiation emitting from operating machines or devices. It employs an encapsulated structure, sensors to capture the noise, and actuators to control surface vibrations. This paper analyses two selection strategies to optimize a centralized ASAC system. The main purpose is to reduce the computational complexity by limiting the amount of secondary path FIR models utilized in the system. The strategies are thoroughly examined and discussed in section 2, complemented by illustrative figures to provide visual clarity. A numerical model was created in MATLAB program to carry out simulations. Each simulation was performed with a different strategy, where the amount and order of the secondary path models differed. The numerical model uses the Normalized Multiple Error Filtered-X Least Mean Square (NMEFxLMS) as the control algorithm for the simulations presented in this paper. An optimization margin was deduced from the simulations conducted. Subsequently, the computational complexity was determined for both strategies based on deducted optimization margins. © 2024 Proceedings of the International Congress on Sound and Vibration. All rights reserved.

关键词： Acoustic variables control

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Social Self-Attention Generative Adversarial Networks for Human Trajectory Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1805-1815页

作者： Yang, Changzhi Pan, Huihui Sun, Weichao Gao, Huijun Harbin Institute of Technology Research Institute of Intelligent Control and Systems Harbin150001 China

Predicting accurate human future trajectories is of critical importance for self-driving vehicles if they are to navigate complex scenarios. Trajectories of humans are not only dependent on the humans themselves, but also the interactions with surrounding agents. Previous works mainly model interactions among agents by using a diversity of polymerization methods that integrate various learned agent states hit or miss. In this article, we propose social self-attention generative adversarial networks (Social SAGAN), which generate socially acceptable multimodal trajectory predictions. Social SAGAN incorporates a generator that predicts future trajectories of pedestrians, a discriminator that classifies trajectory predictions as real or fake, and a social self-attention mechanism that selectively refines the most interactive information and helps the overall model to capture what to pay attention to. Through extensive experiments, we demonstrate that our model achieves competitive prediction accuracy and computational complexity compared with previous state-of-the-art methods on all trajectory forecasting benchmarks. © 2020 IEEE.

关键词： Semantics

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A Review of Segment Anything Model and Its Applications

A Review of Segment Anything Model and Its Applications

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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Ma, Zengchen Sun, Yanwen Zhai, Shixun Lei, Guocheng Zhang, Kaige North Automatic Control Technology Institute Taiyuan China

ISBN: (纸本)9798350384185

Segment anything model (SAM) is the first foundation model in computer vision, following the footsteps of natural language *** on SAM, researchers have conducted a suite of works on a variety of applications in the past year, including image segmentation, object detection, image editing, video object tracking, *** paper conducts a comprehensive review on the works derived from SAM and makes a comparison concerning computational efficiency, model size, and the quality of the processing results.A bunch of evidenceable results indicates that the visual foundation model is able to advance vision task performance, and it is worth further exploring the application mode from the edge ***, future developing trend on visual foundation model is discussed and concluded. © 2024 IEEE.

关键词： Image segmentation

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A data-driven approach for modeling and predicting the thrust force of a tunnel boring machine

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2023年第9期24卷 801-816页

作者： Lintao WANG Fengzhang ZHU Jie LI Wei SUN School of Mechanical Engineering Dalian University of TechnologyDalian 116024China North Automatic Control Technology Institute Taiyuan 030006China

Thrust prediction of a tunnel boring machine(TBM)is crucial for the life span of disc cutters,cost forecasting,and its design *** factors affect the thrust of a *** rock pressure on the shield,advance speed,and cutter water pressure will all have a certain *** addition,geological conditions and other random factors will also influence the thrust and greatly increase the difficulty of modeling it,seriously affecting the efficiency of tunnel *** overcome these challenges,this paper establishes a thrust prediction model for the TBM based on the combination of on-site quality record data and surrogate model ***,the thrust composition and influencing factors are analyzed and the thrust is modeled using a surrogate model based on field *** main factor screening based on the Morris method,the accuracy of the surrogate model is greatly *** Kriging model with the highest accuracy is selected to model the thrust and predict the thrust of the unexcavated *** results show that the thrust model has better thrust prediction by selecting similar conditions for modeling and reasonably increasing modeling *** thrust prediction method of TBM based on the combination of field data and surrogate model can accurately predict the dynamic thrust of the load and can also accurately estimate its statistical characteristics and effectively improve the excavation plan.

关键词： Tunnel boring machine(TBM) Thrust prediction Surrogate model Morris method

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