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On the exponential convergence of input-output signals of nonlinear feedback systems

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arXiv 2022年

作者： Su, Lanlan Zhao, Di Khong, Sei Zhen The Department of Automatic Control & Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom The Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China The Department of Electrical Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan

This note studies the exponential convergence of input-output signals of discrete-time nonlinear systems composed of a feedback interconnection of a linear time-invariant system and a nonlinear uncertainty. Both the open-loop subsystems are allowed to be unbounded. Integral-quadratic-constraint-based conditions are proposed for these uncertain feedback systems, including the Lurye type, to exhibit the property that the endogenous input-output signals enjoy an exponential convergence rate for all initial conditions of the linear time-invariant subsystem. The conditions are established via a combination of tools, including integral quadratic constraints, directed gap, and exponential weightings. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear time-invariant system

On the link between explicit MPC and the face lattice of the lifted feasible domain

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IFAC-PapersOnLine 2022年第16期55卷 308-313页

作者： Ştefan S. Mihai Florin Stoican Bogdan D. Ciubotaru Department of Automatic Control and Systems Engineering University "Politehnica" of Bucharest 313 Splaiul Independenţei Sector 6 060042 Bucharest Romania Research Center for Logic Optimization and Security (LOS) Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest.

The paper provides a geometric interpretation for the explicit solution of the quadratic cost, linear-constrained MPC (model predictive control) problem. We link the face lattice of the lifted feasible domain (defined in the input and parameter space) with the critical regions which partition the parameter space and serve as polyhedral support for the piecewise affine explicit MPC solution. We provide geometric (face visibility) and algebraic (polyhedron emptiness) tests for the pruning of the candidate sets of active constraints.

关键词： Model Predictive control (MPC) Explicit MPC Face Lattice Constrained-Optimization Problem

Guaranteed and Reliable Excitation of Asynchronous Generator Coupled to Shaft of Vehicle 20

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Guaranteed and Reliable Excitation of Asynchronous Generator...

20th IEEE International Conference on Modern Electrical and Energy systems, MEES 2021

作者： Zachepa, Iurii Zachepa, Nataliia Khrebtova, Oksana Serhiienko, Ihor Shokarov, Dmytro Mykhalchenko, Ganna Kremenchuk Mykhailo Ostrohradskyi National University Department of Systems of Automatic Control and Electric Drive Kremenchuk Ukraine National Technical University «kharkiv Polytechnic Institute» Department of Electric Power Stations Kharkov Ukraine Ukrainian Engineering Pedagogics Academy Department of Economy of Enterprises and Management Kharkov Ukraine

ISBN: (纸本)9781665423663

A method for determining the minimum allowable speed of the asynchronous generator shaft is proposed and the results of graphical and analytical solution for an asynchronous machine are given. The analytical dependence of the minimum allowable speed of the induction shaft as a function of the parameters of the drive motor of internal combustion, capacitive excitation and load, which must be provided by selecting the gear ratio of the mechanical coupling device. The general criterion for selecting the size of the transmission rollers is presented in the form of a quality functional, which takes into account the range of variation of the crankshaft speed of the primary engine, the range of variation of the diameter of the transmission roller and the range of fuel consumption at the corresponding crankshaft speed. © 2021 IEEE.

关键词： Internal combustion engines

Optimal trajectory planning of complicated robotic timber joints based on particle swarm optimization and an adaptive genetic algorithm

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Construction Robotics 2021年第2期5卷 131-146页

作者： Meng, Yiping Sun, Yiming Chang, Wen-shao People Environments and Performance Group School of Architecture The University of Sheffield Sheffield UK Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK

In this paper, a methodology for path distance and time synthetic optimal trajectory planning is described in order to improve the work efficiency of a robotic chainsaw when dealing with cutting complex timber joints. To demonstrate this approach one specific complicated timber joint is used as an example. The trajectory is interpolated in the joint space by using a quantic polynomial function which enables the trajectory to be constrained in the kinematic limits of velocity, acceleration, and jerk. The particle swarm optimization (PSO) is applied to optimize the path of all cutting surfaces of the timber joint in operating space to achieve the shortest path. Based on the optimal path, an adaptive genetic algorithm (AGA) is used to optimize the time interval of interpolation points of every joint to realize the time-optimal trajectory. The results of the simulation show that the PSO method shortens the distance of the trajectory and that the AGA algorithm reduces time intervals and helps to obtain smooth trajectories, validating the effectiveness and practicability of the two proposed methodology on path and time optimization for 6-DOF robots when used in cutting tasks.

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Modeling and Optimisation of a Hybrid Energy Harvesting System for IoT Nodes Through PSO-MPPT

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Modeling and Optimisation of a Hybrid Energy Harvesting Syst...

IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Sirine Rabah Aida Zaier Hassen Dahman Research Laboratory Modeling Analysis and Control of Systems (MACS-LR16ES22) National Engineering School of Gabes Innov´Lab University of Carthage Tunis Tunisia National Engineering School of Tunis LaPhyMNE-LR05ES14 Gabes University Department of Automatic and Electrical Engineering National Engineering School of Gabes

The principal contribution of this research paper is to propose an efficient hybrid solar-wind energy harvesting approach that can be used as a solution to the limited battery energy problem of IoT nodes. Ideally, the IoT Network nodes using optimized Solar-wind Energy Harvesting should function for many years. In order to obtain the optimal power for a hybrid system, the Maximum power point tracking (MPPT) based on Particle Swarm Optimization (PSO) controller is proposed. This is by adjusting the duty cycle of the boost converter for both Solar and wind system based on voltage and current measurements. During this work, the modeling and simulation of the proposed hybrid system with PSO−MPPT technique under various wind speeds and various irradiation levels are done using MATLAB/SIMULINK. The Battery Condition of Charge (SoC), battery voltage and battery Current are evaluated. Simulation results prove that the proposed hybrid system with MPPT using PSO can provide better performance as compared to the Perturb and Observe (P&O) method. The proposed system has the potential to meet the energy demand of IoT nodes. increases the durability, and total efficiency of the IoT devices, making them perpetually powered.

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Mini-batch stochastic subgradient for functional constrained optimization

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arXiv 2024年

作者： Singh, Nitesh Kumar Necoara, Ion Kungurtsev, Vyacheslav Automatic Control and Systems Engineering Department University Politehnica Bucharest Spl. Independentei 313 Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest050711 Romania Computer Science Department Czech Technical University Karlovo Namesti 13 Prague12135 Czech Republic

In this paper we consider finite sum composite convex optimization problems with many functional constraints. The objective function is expressed as a finite sum of two terms, one of which admits easy computation of (sub)gradients while the other is amenable to proximal evaluations. We assume a generalized bounded gradient condition on the objective which allows us to simultaneously tackle both smooth and nonsmooth problems. We also consider the cases of both with and without a strong convexity property. Further, we assume that each constraint set is given as the level set of a convex but not necessarily differentiable function. We reformulate the constrained finite sum problem into a stochastic optimization problem for which the stochastic subgradient projection method from [17] specializes to a collection of mini-batch variants, with different mini-batch sizes for the objective function and functional constraints, respectively. More specifically, at each iteration, our algorithm takes a mini-batch stochastic proximal subgradient step aimed at minimizing the objective function and then a subsequent mini-batch subgradient projection step minimizing the feasibility violation. By specializing different mini-batching strategies, we derive exact expressions for the stepsizes as a function of the mini-batch size and in some cases we also derive insightful stepsize-switching rules which describe when one should switch from a constant to a decreasing stepsize regime. We also prove sublinear convergence rates for the mini-batch subgradient projection algorithm which depend explicitly on the mini-batch sizes and on the properties of the objective function. Numerical results also show a better performance of our mini-batch scheme over its single-batch counterpart. © 2024, CC BY.

关键词： Constrained optimization

In-flight novelty detection with convolutional neural networks

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arXiv 2021年

作者： Hartwell, Adam Montana, Felipe Jacobs, William Kadirkamanathan, Visakan Mills, Andrew R. Clark, Tom Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom Rolls-Royce Plc United Kingdom

Gas turbine engines are complex machines that typically generate a vast amount of data, and require careful monitoring to allow for cost-effective preventative maintenance. In aerospace applications, returning all measured data to ground is prohibitively expensive, often causing useful, high value, data to be discarded. The ability to detect, prioritise, and return useful data in real-time is therefore vital. This paper proposes that system output measurements, described by a convolutional neural network model of normality, are prioritised in real-time for the attention of preventative maintenance decision makers. Due to the complexity of gas turbine engine time-varying behaviours, deriving accurate physical models is difficult, and often leads to models with low prediction accuracy and incompatibility with real-time execution. Data-driven modelling is a desirable alternative producing high accuracy, asset specific models without the need for derivation from first principles. We present a data-driven system for online detection and prioritisation of anomalous data. Biased data assessment deriving from novel operating conditions is avoided by uncertainty management integrated into the deep neural predictive model. Testing is performed on real and synthetic data, showing sensitivity to both real and synthetic faults. The system is capable of running in real-time on low-power embedded hardware and is currently in deployment on the Rolls-Royce Pearl 15 engine flight trials. © 2021, CC BY-NC-ND.

关键词： Convolution

Modeling pressure pulsation and backflow in progressing cavity pumps with deformable stator

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arXiv 2021年

作者： Müller, Jens Kouhi, Yashar Leonow, Sebastian Mönnigmann, Martin Automatic Control and Systems Theory Department of Mechanical Engineering Ruhr-Universität Bochum Bochum44801 Germany

This contribution studies the impact of the rotor-stator interaction in a single-stage progressing cavity pump on the flow rate and pressure. Specifically, we investigate the effect of the rotor movement on the sealings formed with deformable stators for various speeds and pressures. Sealings are reconstructed with the help of a geometric 3D model. We analyze the tangential and radial deviation of the rotor from its reference path and show that the radial deviation affects the flow rate, whereas the tangential deviation affects the pressure dynamics. The conjectures are confirmed with a laboratory test setup. © 2021, CC BY-NC-ND.

关键词： Stators

Time-Sensor Domain Data Decomposition and Analysis for Fault Diagnosis of Cutting Tools

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Time-Sensor Domain Data Decomposition and Analysis for Fault...

International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Yufei Gui Zi-Qiang Lang Zepeng Liu Yunpeng Zhu Hatim Laalej Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK School of Engineering and Materials Science Queen Mary University of London London UK Advanced Manufacturing Research Centre The University of Sheffield Sheffield UK

ISBN: (纸本)9781665476881

In the present study, a novel time and sensor domain data decomposition and analysis framework is proposed to perform fault diagnosis of cutting tools. The problem of excessive computation burden existing in multi-sensor data-driven tool condition monitoring (TCM) system is resolved at the data level by compressing raw signals into a significantly smaller set of time and sensor domain data. The utilisation of the time domain components eliminates the influence of environmental noise on raw signals. Meanwhile, the introduction of the sensor domain components reveals the correlation relationship within multiple sensors. Experimental studies are conducted to verify the effectiveness of the proposed approach and illustrate the advantages of the time and sensor domain features compared with raw signal features.

关键词： Fault diagnosis Cutting tools Correlation Working environment noise Milling Robot sensing systems Robustness