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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Size Matters: Exploring the Impact of Scaling on Quadruped Robot Dynamics 25th

Size Matters: Exploring the Impact of Scaling on Quadruped R...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Rahvar, Faraz Yilmaz, Abdurrahman Department of Mechatronics Engineering Istanbul Technical University Istanbul Turkey Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9783031720611

Quadruped robots are indispensable for specialized tasks, particularly in disaster scenarios like earthquakes, where their mobility surpasses that of fixed robots. However, altering their dimensions significantly impacts their mechanical requirements and control systems. This study investigates and compares the principal mechanical parameters affecting the control systems of quadruped robots during scaling, aiming to understand how scalability influences their design across different sizes. Using a standard quasi-direct driven commercial quadruped robot1(Unitree A 1) model featuring dual coaxial motor design and serial link architecture actuators, simulations are conducted at four different sizes, each expanding the robot size by 30%. These simulations analyze static and dynamic forces as the robot walks on a flat surface at a constant speed. To fortify our findings, simulations employ three trajectory scenarios, in each scenario, four robots are scaled in length, increasing by a coefficient of 0.3 from robot 1 to robot 4. The first and second scenarios feature different trajectory lengths, while the third scenario increases the trajectory height while maintaining the length of the second scenario. Specifically, we examine the required torque, energy consumption of the robot motors, reaction forces between the robot feet and terrain, and the mechanical cost of transport. These outcomes encompass critical components of the robot from a mechanical analysis perspective, including joints, providing valuable references for various actuator architecture designs. Our findings reveal a logical pattern in the increase of torques, power consumptions, and reaction forces as the robot scales. These insights lay the groundwork for developing a scalable control architecture for legged robots. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multipurpose robots

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Adaptive Path-following control of Underactuated Surface Vessels Subject to Unknown Disturbances With Uniform Bounded Lateral Motion

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International Journal of control, automation and Systems 2025年 1-11页

作者： Cao, Duc Sang Tran, Quoc Van Department of Electric Actuators & Automation Control Control Automation in Production and Improvement of Technology Institute (CAPITI) Hanoi Viet Nam Department of Mechatronics the School of Mechanical Engineering Hanoi University of Science and Technology (HUST) Hanoi Viet Nam

This article presents a unified framework for path-following and trajectory tracking of underactuated surface vessels (USVs) in the presence of unknown disturbances. To handle the underactuation of the vessel, the path-following of a hand point in the longitudinal axis ahead of the vessel’s mass center is considered. Two robust adaptive path-following control laws are subsequently proposed in the vessel-fixed coordinates to drive the vessel (specifically, the hand point) to the desired path asymptotically. The first controller employs a time-varying discontinuous signum term to attenuate unknown disturbances. By utilization of normalization technique, the second tracking controller is continuous, thus eliminating chattering effect in the control forces commonly occurred in sliding mode controllers. Global uniform asymptotic stability of the path tracking is established based on Lyapunov theory and Barbalat’s lemma. As another technical contribution of this article, the stability of the non-actuated lateral motion of the vessel is shown rigorously based on the square-integrability of the path-following error vector. © ICROS, KIEE and Springer 2025.

关键词： Internal dynamics path-following robust adaptive control stability analysis underactuated vehicles

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A Lyapunov-Based Framework for Trajectory Planning of Wheeled Vehicle Using Imitation Learning

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IEEE Transactions on automation Science and engineering 2025年 22卷 12118-12133页

作者： Lai, Jialun Wu, Zongze Ren, Zhigang Chen, Ci Tan, Qi Xie, Shengli Guangdong University of Technology School of Automation Guangdong Guangzhou510006 China Shenzhen University College of Mechatronics and Control Engineering Guangdong Shenzhen518052 China Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

Trajectory planning with a learning-based approach has emerged as a crucial element in autonomous unmanned systems and has attracted substantial interest from both academia and industry. However, unresolved issues persist concerning data efficiency, safety, convergence, and generalization within the control pipeline. To address this gap, this work presents a trajectory planning method that combines the differential flatness of wheeled vehicle with global convergence property. Our proposed framework transforms the trajectory planning problem, integrating kinematic constraints into a motion planning paradigm. This transformation significantly reduces the state space associated with trajectory planning. Initially, Gaussian mixture regression (GMR) is employed to learn the nonlinear mapping from flat input, leveraging a limited number of demonstrations solved by the optimal control method. Subsequently, we design an asymmetric quadratic Lyapunov function that incorporates both random barrier information and the potential convergence property of the demonstration trajectories. Based on the optimized parameterized Lyapunov function incorporating the convergence and safety criteria, the analytical supplementary control is subsequently obtained by solving a quadratic programming problem to compensate for the prediction errors of GMR, which make the framework complete. Both numerical and real-world experiments are performed to validate the effectiveness of our framework. © 2004-2012 IEEE.

关键词： Motion planning

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Feedback-Based Quantum Algorithm for Constrained Optimization Problems 15th

Feedback-Based Quantum Algorithm for Constrained Optimizati...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Abdul Rahman, Salahuddin Karabacak, Özkan Wisniewski, Rafal Automation and Control Section Department of Electronic Systems Aalborg University Aalborg Denmark Department of Mechatronics Engineering Kadir Has University Istanbul Turkey

ISBN: (纸本)9783031856990

The feedback-based algorithm for quantum optimization (FALQON) has recently been proposed to find ground states of Hamiltonians and solve quadratic unconstrained binary optimization problems. This paper efficiently generalizes FALQON to tackle quadratic constrained binary optimization (QCBO) problems. For this purpose, we introduce a new operator that encodes the problem’s solution as its ground state. Using control theory, we design a quantum control system such that the state converges to the ground state of this operator. When applied to the QCBO problem, we show that our proposed algorithm saves computational resources by reducing the depth of the quantum circuit and can perform better than FALQON. The effectiveness of our proposed algorithm is further illustrated through numerical simulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Constrained optimization

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Reinforcement Learning Based Optimization of Autonomous Aircraft Performance 11

Reinforcement Learning Based Optimization of Autonomous Airc...

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11th International Conference on mechatronics and Robotics engineering, ICMRE 2025

作者： Sari, Metin Caliskan, Fikret Istanbul Technical University Department of Mechatronics Engineering Istanbul Turkey Turkish Aerospace Industries Ankara Turkey Istanbul Technical University Department of Control and Automation Istanbul Turkey

ISBN: (纸本)9798331509293

This research introduces an advanced flight control system for optimizing autonomous aircraft performance, leveraging deep reinforcement learning (DRL) to address the complexities of nonlinear flight dynamics. Using a six-degree-of-freedom (6-DoF) rigid aircraft flight dynamics model, we develop a Deep Deterministic Policy Gradient (DDPG) controller tailored for waypoint navigation and attitude stabilization tasks. A custom reward framework and extensive hyperparameter tuning enable effective training within a high-fidelity MATLAB/Simulink environment, achieving high rewards and precise control. Although computationally intensive, the simulations demonstrate robust performance across diverse flight conditions, with potential for real-world applications and future extensions to multi-agent scenarios. © 2025 IEEE.

关键词： Deep reinforcement learning

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Balancing Precision and Speed: Introducing The Performance Efficiency Evaluation Ratio (PEER) in Visual Odometry

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IEEE Access 2025年 13卷 89874-89904页

作者： Atilgan, Cem Mercimek, Muharrem Kırklareli University Department of Mechatronics Engineering Kırklareli Turkey Yildiz Technical University Department of Control and Automation Engineering Istanbul Turkey

Visual odometry is extensively utilized in robotics, autonomous navigation, and augmented reality to estimate motion by analyzing sequential images. Provides high precision without relying on external sensors such as GPS. However, real-time visual odometry systems must strike a balance between computational correctness and speed, a critical yet often overlooked trade-off in existing evaluation methods. Traditional metrics such as ATE, RPE, RMSE, FPS, and RTF typically assess precision or efficiency in isolation and lack adaptability to real-time constraints. To address this limitation, we propose the Performance Efficiency Evaluation Ratio (PEER), a novel, adaptive, and lightweight metric that jointly evaluates algorithm performance based on both fidelity and computation time. PEER incorporates a tunable weighting parameter to prioritize performance, speed, or a balanced trade-off, and employs normalization techniques to ensure comparability across different algorithms and systems. We evaluated PEER using the KITTI dataset with various feature extraction algorithms (SIFT, ORB, BRISK, KAZE, AKAZE) and matching algorithms (BF, FLANN). The findings indicate that PEER not only distinguishes performance effectively across various scenarios but also shows strong alignment with established MCDM methods such as TOPSIS and VIKOR, all while maintaining lower computational complexity and greater suitability for real-time deployment. Overall, PEER provides a robust and flexible framework for optimizing visual odometry systems. © 2013 IEEE.

关键词： Augmented reality

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Development of a Learning-Based control System for Robot-Assisted Grinding of Rubber Roller

Development of a Learning-Based Control System for Robot-Ass...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Fabian Reiling Maximilian Bause Stefan Gröger Christian Henke Steven Koppert Ansgar Trächtler Scientific Automation Fraunhofer IEM Paderborn Germany Intelligent Automotive Industry Capgemini Invent Hamburg Germany Control Engineering and Mechatronics Heinz Nixdorf Institute Paderborn Germany

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Grinding rubber rollers to a specific geometry presents significant challenges due to the material's non-linear behavior. Currently, this process has not been successfully automated, primarily because the control concepts implemented thus far do not achieve the desired accuracy for economical operation. Consequently, these rollers are manually ground to the required geometry on a grinding wheel. Previous work has led to the development of a system that automates the grinding of rollers using a robot. The objective of this paper is to demonstrate, through modeling, how a learning-based control system can be developed for this process to reduce production time and facilitate an economical operation. To this end, we propose two control concepts: one that directly predicts the required control variable using a learning-based controller, and another that employs an AI-corrected control mechanism, which utilizes the control variable provided by a proportional controller to correct the error between the control variable and the control difference. Finally, we conduct a comparative simulation of the performance of these concepts against a conventional controller.

关键词： Geometry Hands Manufacturing processes Process control Wheels Production control systems Parametric statistics Rubber Mobile robots

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Reinforcement Learning Based Optimization of Autonomous Aircraft Performance

Reinforcement Learning Based Optimization of Autonomous Airc...

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mechatronics and Robotics engineering (ICMRE), International Conference on

作者： Metin Sari Fikret Caliskan Department of Mechatronics Engineering Istanbul Technical University Istanbul Turkey Turkish Aerospace Industries Ankara Turkey Department of Control and Automation Istanbul Technical University Istanbul Turkey

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

关键词： Training Atmospheric modeling Computational modeling Robot sensing systems Mathematical models Aircraft navigation Aircraft MATLAB Optimization Aerospace control

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Enhancing the performance of CaTiO3-GDC heterostructure electrolytes in LT-CFCs

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Ceramics International 2025年第15期51卷 20131-20142页

作者： Li, Dongchen Cui, Yingying Hong, Xiaofang Shah, M.A.K. Yousaf Mushtaq, Naveed Rauf, Sajid Arshad, Naila Irshad, Sultan Alodhayb, Abdullah N. Yousaf, Muhammad Lu, Yuzheng Department of Electrical and Automation Shandong Labor Vocational and Technical College Jinan250300 China Jiangsu Provincial Key Laboratory of Solar Energy Science and Technology/Energy Storage Joint Research Center School of Energy and Environment Southeast University No.2 Si Pai Lou Nanjing210096 China College of Mechatronics and Control Engineering Shenzhen University Guangdong Province 518000 China School of Physics Electronics and Intelligent Manufacturing Huaihua University Hunan Huaihua418008 China College of Electronics and Information Engineering Shenzhen University Guangdong Province 518000 China Department of Physics and Astronomy College of Science King Saud University Riyadh11451 Saudi Arabia College of Chemistry and Environmental Engineering Shenzhen University Guangdong Province Shenzhen518055 China School of Electronic Engineering Nanjing Xiaozhuang University Nanjing211171 China

Designing ceramic fuel cells (CFCs) with fast ionic conduction and superior electrochemical performance at low temperatures is critical for advancing hydrogen applications. Here, we developed a heterostructure electrolyte by combining the ionic conductor Gd0.1Ce0.9O2 (GDC) with the semiconductor material CaTiO3 (CTO) to shape into the composite heterostructure (semiconductor ionic heterostructure) CTO-GDC. The designed heterostructure with different fermi levels established the depletion zone due to the redistribution of charges at the interface, leading to enhancement of the ionic conduction;meanwhile, the established built-in electric field suppresses the electronic conduction speed of the ion's transportation at the interface of the constructed heterostructure (CTO-GDC). The formation of heterostructure introduces a number of oxygen vacancies, as confirmed via x-ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR), benefiting the transportation of ions in the composite heterostructure. The prepared composite heterostructure shows the highest power output of 810 mW/cm2 at a low operational temperature of 520 °C. The CTO-GDC shows high ionic conductivity of 0.17 S cm−1 at 520 °C and low activation energy of 0.785 eV at 520-420 °C. The composite containing a 50 % molar ratio of GDC and CTO demonstrated exceptional performance by successfully inhibiting electron transit, improving ionic conductivity, and achieving outstanding power output. The enhanced ionic conductivity with low activation energy of composite heterostructure suggests that designing a heterostructure seems an efficient approach to fuel cell application. © 2025

关键词： Paramagnetic resonance

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