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Multivariable Nonlinear control based on Exact Feedback Linearization with Integral Action for a PMSM

engineering Letters 2024年第12期32卷 2270-2277页

作者： Velarde-Gomez, Sergio Giraldo, Eduardo Universidad Tecnológica de Pereira Research Group in Automatic Control Pereira Colombia Department of Electrical Engineering Universidad Tecnológica de Pereira Research group in Automatic Control Pereira Colombia

This paper introduces a novel approach for linear quadratic optimal control design for a nonlinear control structure based on a feedback linearization method and applied over permanent magnet synchronous motor (PMSM). The design of the proposed approach is developed by considering rotational speed, direct and quadrature currents as state variables. To achieve accurate tracking of the desired rotational speed, an additional nonlinear controller with integral action is implemented, also employing the exact feedback linearization method, and since the reference for the quadrature current is selected as zero only regulation is required. Consequently, a multivariable nonlinear controller is achieved using exact feedback linearization, offering robustness against external disturbances like torque load. The proposed method is assessed through simulations on a PMSM motor, and the speed reference tracking performance is analyzed with and without integral action. An additional comparison is carried out by incorporating a pole placement technique into the controller design. The proposed exact feedback linearization approach with integral action and linear quadratic control, demonstrates superior performance over other methods in terms of disturbance rejection, control effort and speed tracking. © 2024, International Association of Engineers. All rights reserved.

关键词： Disturbance rejection

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Adaptive Neural Network Identification for Robust Multivariable Systems

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IAENG International Journal of Applied Mathematics 2024年第1期54卷 68-76页

作者： Osorio-Arteaga, Felipe Giraldo, Eduardo Department of Electrical Technology Universidad Tecnológica de Pereira Research Group in Automatic Control Pereira Colombia Department of Electrical Engineering Universidad Tecnológica de Pereira Research group in Automatic Control Pereira Colombia

This paper proposes a robust identification and control-based on a neural network method for a Twin Rotor Multivariable System (TRMS) using a recursive adaptive training algorithm. The algorithm is based on a recursive least squares approach with an additional steepest descent stage. An Adaline neural network is used for modeling the system, and a robust structure is selected based on a linear auto-regressive structure with exogenous inputs (ARX) related to the estimation error. The identification is performed online and the system is controlled under a polynomial structure by pole placement with a dead-beat strategy. The method is evaluated in terms of estimation and tracking error in the presence of external additive disturbances, parametric disturbances, and sinusoidal reference signals. The Root-Mean Square Error (RMSE) is used to evaluate the estimation performance and the Integral-Time Absolute Error (ITAE) is used to evaluate the tracking performance. As a result, a novel robust controller based on a neural network is designed where the best results are obtained for a training recursive least squares algorithm with an additional steepest descent stage. © 2024, International Association of Engineers. All rights reserved.

关键词： Multivariable systems

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Learning-based robot assembly method for peg insertion tasks on inclined hole using time-series force information

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Biomimetic Intelligence & robotics 2025年第1期5卷 116-123页

作者： Zhifei Shen Zhiyong Jiang Jingwang Zhang Jun Wu Qiuguo Zhu Polytechnic Institute Zhejiang UniversityHangzhou 310015China Robotics Engineering Center the 21st Research Institute of China Electronics Technology Group CorporationShanghai 200233China Department of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

This paper presents a novel method for learning force-aware robot assembly skills,specifically targeting the peg insertion task on inclined *** the peg insertion task involving inclined holes,we employ one-dimensional convolutional networks(1DCNN)and gated recurrent units(GRU)to extract features from the time-series force information during the assembly process,thereby identifying different contact states between the peg and the *** to the identification of contact states,corresponding pose adjustments are executed,and overall smooth interaction is ensured through admittance *** assembly process is dynamically adjusted using a state machine to fine-tune admittance control parameters and seamlessly switch the assembly *** the utilization of dual-arm clamping,we conduct key unlocking experiments on bases inclined at varying *** results demonstrate that the proposed method significantly improves the accuracy and success rate of state recognition compared to previous methods.

关键词： Manipulator assembly Peg insertion Force information Contact state Dual-arm

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Nonlinear Dynamic Inversion-based Motion Planning of a Floating Satellite Platform

Nonlinear Dynamic Inversion-based Motion Planning of a Float...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Banerjee, Avijit Tevetzidis, Ilias Satpute, Sumeet Gajanan Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents a nonlinear dynamic inversion-based motion plan for a levitating robotic satellite emulation platform. The frictionless motion of such a levitating platform has dynamic equivalency with a satellite’s realistic movement in space, thus evolving as an efficient demonstration tool as a hardware-in-loop satellite motion simulator. A brief design description of the emulation platform unified with various sensor and actuator units is presented, and an associated nonlinear mathematical model representing its coupled dynamic motion is described. The proposed control design architecture manipulates the associated nonlinear dynamics, which appear due to the coupling of translational and rotational movement of the robotic platform, thereby establishing an efficient trajectory tracking capability for various operating regions without the additional requirement of gain scheduling. In order to realize the control requirement through a set of ON-OFF thruster-based actuation units, which closely resembles the reaction control system of realistic satellite design, an optimal thrust allocation logic is proposed that accounts for various physical limitations of thrusters such as thrusters as its magnitude constraint and minimum on time. The efficacy of the proposed modelling and control scheme is experimentally validated in different realistic motion planning scenarios, such as point-to-point manoeuvres with terminal objectives and a superior path-following capability. The experimental studies are supported with related results. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Motion planning

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Quadtree-based Free-space Cell-decomposition for Pathplanning with RRT* Implementation

Quadtree-based Free-space Cell-decomposition for Pathplannin...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Otte, Noah Satpute, Sumeet Gajanan Banerjee, Avijit Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper introduces a novel, computationally efficient, random-search based path-planning algorithm specifically designed to enhance the dexterous accessibility of autonomous lunar rovers operating on highly cluttered obstacle-dense planetary surfaces. The proposed pathplanning framework employs a Quadtree-based cell decomposition to enhance the performance of sampling-based path-planning algorithms, specifically those in the category of Rapidlyexploring Random Trees (RRT) and its optimized variant, RRT*. In the process of generating the path through an occupancy grid map, the obstacle-free cells are utilized as discrete sampling points for the RRT* algorithm. A unique non-uniform sampling approach is employed to favour larger cells with a higher degree of safety, thereby enabling seamless navigation through the obstacle-dense map. Additionally, the non-uniform sampling approach enhances the overall computational efficiency of the framework, making it suitable for fast, lightweight onboard implementation. The efficacy of the proposed planning framework is demonstrated through numerous simulation scenarios. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Decision trees

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Coverage Path Planning of Agricultural Spraying Drones with Graph Convolutional Network 12

Coverage Path Planning of Agricultural Spraying Drones with ...

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12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

作者： Khaloujini, Amirmasood Talaeizadeh, Amin Taheri, Alireza Alastya, Aria Advanced Research Lab for Control and Agricultural Robotics Sharif University of Technology Social and Cognitive Robotics Lab. Department of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798331529734

Optimal route planning for autonomous robots is a crucial element of contemporary agriculture, especially for activities like spraying and pollination. This paper presents a Graph Convolutional Network (GCN) model especially developed to address the Coverage Path Planning (CPP). The aim of this paper is to convert the CPP problem into a Traveling Salesman Problem (TSP), so that by solving the TSP using the proposed neural network, an optimal route can be extracted for each agricultural field in the shortest possible time and with sufficient accuracy, ensuring full coverage of the field's area. The proposed model is a GCN, augmented with a transformer layer and a Multi-Layer Perceptron (MLP), was trained using datasets produced by a Genetic Algorithm (GA) Method. The model obtained a network error of 0.12 while solving TSP cases with 20 points. It showed robust generalization skills, as evidenced by validation and test errors of 0.1261 and 0.1264, respectively. Significantly, the GCN model reduces the solution time to approximately one-tenth compared to conventional approaches, a benefit that increases as the number of points increases. The effectiveness of the model in producing ideal coverage routes highlights its potential for wider use in optimization and coverage path planning, especially in dynamic and extensive agricultural environments. © 2024 IEEE.

关键词： Traveling salesman problem

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360-Degree Sensor Integration to Optimize Reinforcement Learning for Autonomous Vehicles 14

360-Degree Sensor Integration to Optimize Reinforcement Lear...

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14th Asian control Conference, ASCC 2024

作者： Mardhatillah, Irina Nazaruddin, Yul Yunazwin Widyotriatmo, Augie Institut Teknologi Bandung Instrumentation Control and Automation Research Group Department of Engineering Physics Bandung Indonesia

ISBN: (纸本)9789887581598

The prevalent use of front-view and top-view cam-eras in reinforcement learning (RL)-based autonomous vehicle agents introduces limitations in observing the surroundings. To address these challenges, we propose the integration of 360-degree sensors on the agent, ensuring comprehensive observation cover-age. Our approach involves strategically placing four cameras on the vehicle's sides, supplemented by Light Detection and Ranging (LiDAR) technology to eliminate blind spots. Deep reinforcement learning (deep RL) is employed to develop a driving policy controlling the agent's velocity and steering based on the sensor information. Two sensor configurations are explored, namely the common top-view camera and our 360-degree sensor integration. Simulation results demonstrate excellent performance for the agent equipped with our proposed sensor integration, particu-larly in complex traffic scenarios. The evaluation matrix reveals improved route completion rates, reduced collisions, and lower average collision impulse, underscoring the effectiveness of our proposed sensor configuration. © 2024 Asian control Association.

关键词： Deep reinforcement learning

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Machine learning-driven optimization of plasma-catalytic dry reforming of methane

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Journal of Energy Chemistry 2024年第9期96卷 153-163页

作者： Yuxiang Cai Danhua Mei Yanzhen Chen Annemie Bogaerts Xin Tu Department of Electrical Engineering and Electronics University of LiverpoolLiverpool L693GJUK Research Group PLASMANT Department of ChemistryUniversity of AntwerpUniversiteitsplein 1BE-2610 Wilrijk-AntwerpBelgium College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjing 211816JiangsuChina

This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental *** address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML *** ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM *** model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM *** found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/***,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM *** results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction *** hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

关键词： Plasma catalysis Machine learning Process optimization Dry reforming of methane Syngas production

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Modeling a fuzzy expert system for predicting customers’ behavioral intention for restaurant business through parallel prediction of perceived values and satisfactions

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International Journal of Information Technology (Singapore) 2025年第2期17卷 1007-1014页

作者： Akhtaruzzaman, M. Shahryer, Fahim Halder, Sachitra Tanvin, Jamal Uddin Department of Computer Science and Engineering Military Institute of Science and Technology (MIST) Mirpur Cantonment Dhaka 1216 Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh

This paper focuses on the modeling of a recommendation process for the restaurant industry. It is a challenge job for a restaurant owner to ascertain the intentions of customers to evaluate their services, further improve their profit margin. So, the objective of this study is to design a fuzzy-based prediction model of customers’ intentions for a fine dining experience. To facilitate a comparative analysis and to identify the optimal approach, various membership functions (MF) and de-fuzzification methods are employed along with the analysis of the correlational behavior among various input subsets. Results have shown that CoA method with triangular MF are well enough to implement the model. The fuzzy-correlation analysis presents various levels of membership strengths for a same recommendation score in some cases. Moreover, the evaluation of the proposed model based on a custom dataset shows prominent results with as 0.938. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Customer intension Customer satisfaction FIS Perceived value Recommendation

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Real-Time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changes

Real-Time Motion Generation and Data Augmentation for Graspi...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Yamamoto, Kenjiro Ito, Hiroshi Ichiwara, Hideyuki Mori, Hiroki Ogata, Tetsuya Connective Automation Innovation Center R&d Group Hitachi Ltd. Robotics Research Department Japan Institute for Ai and Robotics Future Robotics Organization Japan Waseda University Faculty of Science and Engineering Japan

ISBN: (纸本)9798350312072

While deep learning enables real robots to perform complex tasks had been difficult to implement in the past, the challenge is the enormous amount of trial-and-error and motion teaching in a real environment. The manipulation of moving objects, due to their dynamic properties, requires learning a wide range of factors such as the object's position, movement speed, and grasping timing. We propose a data augmentation method for enabling a robot to grasp moving objects with different speeds and grasping timings at low cost. Specifically, the robot is taught to grasp an object moving at low speed using teleoperation, and multiple data with different speeds and grasping timings are generated by down-sampling and padding the robot sensor data in the time-series direction. By learning multiple sensor data in a time series, the robot can generate motions while adjusting the grasping timing for unlearned movement speeds and sudden speed changes. We have shown using a real robot that this data augmentation method facilitates learning the relationship between object position and velocity and enables the robot to perform robust grasping motions for unlearned positions and objects with dynamically changing positions and velocities. © 2024 IEEE.

关键词： Time series

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