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Baseline Results for Selected Nonlinear system Identification Benchmarks

IFAC-PapersOnLine 2024年第15期58卷 474-479页

作者： Max Champneys Gerben I. Beintema R. Tóth Maarten Schoukens Timothy J. Rogers Dynamics Research Group University of Sheffield Sheffield UK Control System group Eindhoven University of Technology Eindhoven the Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting improvements or extensions to existing methods. It is natural, therefore, to consider how one might choose between these competing models. Benchmark datasets provide one clear way to approach this question. However, to make meaningful inference based on benchmark performance it is important to understand how well a new method performs comparatively to results available with well-established methods. This paper presents a set of ten baseline techniques and their relative performances on five popular benchmarks. The aim of this contribution is to stimulate thought and discussion regarding objective comparison of identification methodologies.

关键词： Nonlinear system Identification Benchmarks NARX Nonlinear State-Space

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection 30

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Cheng, Long He, Haoyang Gu, Yan Liu, Qingzhi Zhao, Zhiming Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Wageningen University and Research Information Technology Group Netherlands University of Amsterdam Research Group of Multi-Scale Networked Systems Netherlands

ISBN: (纸本)9798331515966

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of 53.18% and costs by 61.98% compared to basic techniques, and achieving 20.26% and 25.71% improvements over the latest advanced methods, respectively. © 2024 IEEE.

关键词： Deep reinforcement learning

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Baseline Results for Selected Nonlinear system Identification Benchmarks

arXiv

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arXiv 2024年

作者： Champneys, Max D. Beintema, Gerben I. Tóth, Roland Schoukens, Maarten Rogers, Timothy J. Dynamics Research Group University of Sheffield Sheffield United Kingdom Control System Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

关键词： Nonlinear simulations

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Pallet Detection and Distance Estimation with YOLO and Fiducial Marker Algorithm in Industrial Forklift Robot

Pallet Detection and Distance Estimation with YOLO and Fiduc...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Eric Sean Kesuma Pranoto Hidaya Rusmin Devira Anggi Maharani Control and Computer System Research Group School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

Utilising technology such as artificial intelligence and robotics potentially improves E-Commerce in efficiency. In this trends, the usage of autonomous forklifts in the warehouse to lift and arrange things should be implemented. The picking system in the warehouse needs pallet detection and tracking to carry out the things. This research will find the best performance of the YOLOv5 model and correct the distance estimation model to the fiducial marker. In this paper, we used the ArUco fiducial marker to mark the pallet target and estimate the pose and distance in real time. The insertion points of the pallet were also detected using the YOLOv5 algorithm to validate the pallet and get the coordinate variables of the holes. The YOLOv5n gives the best performance at 24 fps in real-time detection. Distance measurement from the marker detection had an average error of 2.28 cm with linear regression.

关键词： Robot kinematics Linear regression Transportation Cameras Fiducial markers Real-time systems Distance measurement

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NARX Neural Network Modeling of Batch Distillation Process

NARX Neural Network Modeling of Batch Distillation Process

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11th IEEE International Conference on system Engineering and Technology, ICSET 2021

作者： Putri, Adi Novitarini MacHbub, Carmadi Hidayat, Egi Muhammad Idris School of Electrical Engineering and Informatics Institut Teknologi Control Computer System Research Group Bandung Indonesia

ISBN: (纸本)9781665437660

The batch distillation model is highly nonlinear due to the influence of mass and composition of the initial material to be separated. It is also caused by the thermodynamics of the system which is not ideal. Therefore, analytical batch distillation modeling does not fulfill superposition theory, or is nonlinear. Thus, any well-established linear control schemes can not be applied here. Meanwhile, modeling a system using the Nonlinear Autoregressive with eXogenous inputs (NARX) is quite widely used today because of its ability to represent non-linear system dynamics. This paper propose a system identification using NARX Neural Network (NARX-NN) to modeling batch distillation process. In order to prove the superiority of NARX-NN's accuracy in the system identification process, a comparison with linear ARMA models is done. In this study, ARMA approximation was carried out in two ways. The first way is to use a neural network, while the second method is through approximation to the discrete transfer function. The validation results show that the NARX-NN achieves significantly better fit compared to the linear models. NARX-NN and ARMA-NN were compared and their MSE ratio for delay input and output 1 and 3, respectively have the smallest value, i.e 1.71e-04 © 2021 IEEE.

关键词： Thermodynamics Analytical models system dynamics Conferences Neural networks Transfer functions systems engineering and theory

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Output Feedback control for Deterministic Unknown Dynamics Discrete-Time system using Deep Recurrent Q-Networks

TechRxiv

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TechRxiv 2023年

作者： Putri, Adi Novitarini Hidayat, Egi Mahayana, Dimitri Machbub, Carmadi Control System and Computer Research Group School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia Institut Teknologi Sains Bandung Bekasi Indonesia

The current application of control theory is commonly carried out in systems with a model or known system dynamics. However, in practice this is a formidable task to achieve as not all state information can be known. The use of the Output Feedback (OPFB) scheme in the field of control systems also possesses a weakness because it requires the use of an observer. This appears rather contradictory as the use of an observer requires system dynamics information. This research proposes an optimal control scheme using Deep Recurrent Q-Networks (DRQN) to generate an optimal control signal trajectory based on a collection of input and output data from the system itself. The approach proposed in this study is based on the Q-Learning method from the Reinforcement Learning (RL) scheme. The Long-Short Term Memory (LSTM) is used to approximate the Q-function and determine the control signals for a system without a known model. The method that we proposed in this study has been tested on four case studies. The control signal trajectory generated from our proposed algorithm, is much smaller than the control signal that generated from classical Q-Learning scheme. The results of this research are certainly relevant to the aim of OPFB, namely that the controller is designed to be able to regulate (bring the state trajectory to zero) and minimize control signal energy. It is empirically discovered that the same result is proven by the norm values resulting from the Q-function trajectory. The norm of Q-function trajectory for our proposed algorithm on the 1st, 2nd, 3rd, and 4th case studies are 2.11E-08, 3.15E-06, 3.79E-09, and 1.59E-13, respectively. © 2023, CC BY.

关键词： Trajectories

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Linear Quadratic Gaussian using Kalman Network and Reinforcement Learning for Discrete-Time system

Linear Quadratic Gaussian using Kalman Network and Reinforce...

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IEEE International Conference on system Engineering and Technology

作者： Adi Novitarini Putri Carmadi Machbub Dimitri Mahayana Egi M. Idris Hidayat Control and Computer System Research Group School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

ISBN: (纸本)9798350319842

A problem arises when implementing the control scheme into a system, the limitation of the number of sensors used causes not all state information from the plant can be obtained. In addition, the data measurement process also often contains noise. Therefore, implementing the optimal control scheme directly becomes difficult. The status estimation method proposed in this study utilizes the use of an Artificial Neural Network (ANN) as a function approximation. On the other hand, the development of optimal control implementation has also experienced a fairly massive increase. Some of the existing optimal control solutions can only be carried out offline and depend on the dynamics' model of the system to be controlled. The combination of KalmanNet and the Value Iteration (VI) algorithm is used to adapt the role of the Linear Quadratic Gaussian (LQG) method in designing control procedures to produce high performance. Tests were carried out on a cart-pole system in a discrete-time domain. The use of the combination of these methods could make the closed loop system towards a value of 0 and the evolution of the estimator gain is smoother, namely with the norm of 2.33S4E-04.

关键词： Optimal control Artificial neural networks Reinforcement learning Nonlinear filters systems engineering and theory Sensor systems Trajectory

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Long Cheng Haoyang He Yan Gu Qingzhi Liu Zhiming Zhao Fang Fang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing China Information Technology Group Wageningen University and Research Netherlands Research Group of Multi-Scale Networked Systems University of Amsterdam Netherlands

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of $53.18 \%$ and costs by $61.98 \%$ compared to basic techniques, and achieving $20.26 \%$ and $25.71 \%$ improvements over the latest advanced methods, respectively.

关键词： Training Cloud computing Privacy Costs Scalability Deep reinforcement learning Dynamic scheduling Real-time systems Complexity theory Protection

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Trajectory Optimization of Manipulator for Loading and Unloading the Ladle Nozzle Based on Decomposition Multi-Objective Improved Whale Optimization Algorithm

Trajectory Optimization of Manipulator for Loading and Unloa...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Dongliang Zhao Yiming Fang Chen Yang Jingliang Xin Key Lab of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei Province China Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao Hebei Province China Hebei Iron and Steel Group Tangshan Hebei Province China

In recent years, industrial manipulators are widely used in factories. To solve the problems of low efficiency and high joint jitter in ladle nozzle loading and unloading, a new trajectory optimization method is proposed. Firstly, a manipulator model is established on the base of mechanical construction and parameters of KR1000L750. And the 3-5-3 segmented polynomial interpolation approach is used to design the trajectory. Then a trajectory optimization model is proposed based on time and impact as optimization objectives. Secondly, the traditional whale optimization algorithm is improved by introducing a nonlinear adaptive convergence factor to balance the exploration and development of the algorithm. The improved algorithm has also added a new search mechanism during the exploration phase. To reduce the waste of exploration resources, the information on the current optimal solution is added by the Cauchy mutation method. Then, a decomposition-based multi-objective improved whale optimization algorithm (DMOIWOA) is proposed. The algorithm combines a penalty-based boundary intersection approach with the improved whale optimization algorithm. Six representative optimization algorithms are selected for comparison on the test functions. The DMOIWOA has evident superiority in convergence and distribution. Finally, the algorithm is applied to the trajectory optimization model to obtain a better Pareto front. In conclusion, the DMOIWOA can reasonably optimize the manipulator trajectory, improving the manipulator operation efficiency and reducing the manipulator jitter.

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A Model Predictive Current control for a Three-Phase Modular Matrix Converter

A Model Predictive Current Control for a Three-Phase Modular...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Maryam Sarebanzadeh Mohammad Ali Hosseinzadeh Ralph Kennel Ebrahim Babaei Marco Rivera Patrick Wheeler Chair of High-Power Converters System Technical University of Munich Munich Germany Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical and Electronic Engineering Faculty of Engineering Chair of Power Electronics Machines and Control (PEMC) Research Group University of Nottingham Nottingham UK Laboratorio de Conversión de Energías y Electrónica de Potencia (LCEEP) Vicerrectoría Academica Universidad de Talca Talca Chile

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The modular matrix converter (MMC) integrates a multi-winding transformer with a three-phase-to-single-phase matrix converter for each phase, making it suitable for high-power, high-voltage applications. Predictive control is an advanced control technique widely applied to power converters due to its numerous advantages, including fast dynamic response, the elimination of the need for proportional gain, and straightforward implementation. This study proposes a model predictive control (MPC) strategy to regulate load currents, reactive power, and common-mode voltage in a three-phase modular matrix converter. The proposed MPC algorithm selects the optimal switching states from all valid options for the modular matrix converter to apply in the next control step. A cost function is then used to minimize the error between the predicted system variables and their reference values, ensuring that the best states are applied to the modular matrix converter. The proposed MPC technique effectively controls the load currents to follow the desired reference signals, maintains sinusoidal source current waveforms by regulating reactive power, and reduces the magnitude of the common-mode voltage. The performance of the proposed approach is demonstrated through simulation results obtained in the MATLAB/Simulink environment.

关键词： Current control Reactive power Total harmonic distortion Simulation Switches Cost function Transformers Matrix converters Voltage control Predictive control

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