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control of COVID-19 system using a novel nonlinear robust control algorithm

BIOMEDICAL SIGNAL PROCESSING AND control 2021年 64卷 102317-102317页

作者： Hadi, Musadaq A. Ali, Hazem, I Univ Technol Baghdad Control & Syst Engn Dept Baghdad Iraq

COVID-19 has been a worldwide concern since the outbreak. Many strategies have been involved such as suppression and mitigation strategies to deal with this epidemic. In this paper, a new mathematical-engineering strategy is introduced in order to control the COVID-19 epidemic. Thereby, control theory is involved in controlling the unstable epidemic alongside with the other suggested strategies until the vaccine will hopefully be invented as soon as possible. A new robust control algorithm is introduced to compensate the COVID-19 nonlinear system by propose a proper controller after using necessary assumptions and analysis are made. In addition, the Variable Transformation Technique (VTT) is used to simplify the COVID-19 system. Furthermore, the Most Valuable Player algorithm (MVPA) is applied in order to optimize the parameters of the proposed controller. The simulation results are based on the daily reports of two cities Hubei (China) and Lazio (Italy) since the outbreak. It can be concluded that the proposed control algorithm can effectively compensate the COVID-19 system. In addition, it can be considered as an effective mathematical-engineering strategy to control this epidemic alongside with the other strategies.

关键词： robust control algorithm COVID-19 Nonlinear system Coronavirus Variable Transformation Technique Most Valuable Player algorithm

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Real-time implementation of a robust active control algorithm for narrowband signals suppression

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SOFT COMPUTING 2018年第5期22卷 1545-1554页

作者： Kim, Jeakwan Lee, Minho Lee, Young-Sup Incheon Natl Univ Incheon South Korea

This study presents a practical active noise control (ANC) algorithm with robust stability for reducing the powertrain noise or vibration inside a car. It is important to ensure that a practical ANC system for a car is robustly stable to variations or uncertainties in the actual plant. After investigating the robust stability condition of the ANC algorithm, a robust plant model is designed by considering the multiplicative plant uncertainties within given bounds such as closing or opening door windows. The ANC algorithm was implemented in a dSPACE DS1401 as a control platform, and an error microphone and a subwoofer as a secondary source were positioned at the driver's left ear and the trunk of the experimental car, respectively. The engine rpm information received from the controller area network of the car was used for the generation of relevant reference signals. The real-time control experiments were carried out against the plant perturbation when the engine was either idling or sweeping in the neutral mode. The results showed that the robust control algorithm can suppress the noise whether the actual plant was nominal or perturbed with the stability over the rpm.

关键词： robust control algorithm Active noise suppression Perturbed plant robust stability Vibration

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Design of a Lower Limb Exoskeleton: robust control, Simulation and Experimental Results

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algorithmS 2023年第9期16卷 449-449页

作者： Salcido, E. Anyuli Alvarez Centeno-Barreda, Daniel Rosales, Yukio Lopez-Gutierrez, Ricardo Salazar, Sergio Lozano, Rogelio Ctr Invest & Estudios Avanzados Inst Politecn Nac Av IPN 2508San Pedro Zacatenco Mexico City 07360 Mexico Mexico Consejo Nacl Humanidades Ciencias & Tegnol Av de los Insurgentes Sur 1582 Credito Constructo Mexico City 03940 Mexico

This paper presents the development of a robust control algorithm to be applied in a knee and ankle joint exoskeleton designed for rehabilitation of flexion/extension movements. The goal of the control law is to follow the trajectory of a straight leg extension routine in a sitting position. This routine is commonly used to rehabilitate an injury on an Anterior Cruciate Ligament (ACL) and it is applied to the knee and ankle joints. Moreover, the paper presents the development and implementation of the robotic structure of the ankle joint to integrate it into an exoskeleton for gait rehabilitation. The development of the dynamic model and the implementation of the control algorithm in simulation and experimental tests are presented, showing that the proposed control guarantees the convergence of the tracking error.

关键词： robotic exoskeleton rehabilitation robotics robust control algorithm sliding mode control ankle and knee biomechanics

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Performance enhancement of photovoltaic system under grid voltage distortion utilising total least-square control scheme

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IET POWER ELECTRONICS 2020年第14期13卷 3044-3055页

作者： Kumar, Ajay Patel, Nirav Gupta, Nitin Gupta, Vikas Davari, Pooya Malaviya Natl Inst Technol Jaipur Dept Elect Engn JLN Marg Jaipur Rajasthan India Aalborg Univ Dept Energy Technol Aalborg Denmark

Grid-interfaced solar photovoltaic (GIPV) system requires a robust control algorithm for satisfactory operation under grid voltage disturbances. In this study, an application of Newton's learning rule-based total least-square estimation employing single-layer neural network structure is adopted to interface the photovoltaic unit with the utility grid. In addition, the weight-updating mechanism is uniquely integrated with threshold neuron for attaining speedy extraction of fundamental load current component. In particular, the proposed control algorithm has the capability to transfer maximum active power to the utility grid/AC load at unity power factor while operating as distribution static compensator to offer various ancillary services including current harmonics attenuation and reactive current suppression. This will lead to increased device utilisation factor of the overall GIPV system during night time (i.e. in the absence of solar irradiance). Moreover, the proposed control scheme is designed and developed without any complex transformations and derivative terms, which results in less computational intensiveness. The GIPV system with three-phase double-stage configuration is modelled in MATLAB/Simulink software using sim-power system tool. Finally, the adaptability of the proposed control algorithm has been verified and confirmed through 500 W laboratory prototype using low-cost Arm Cortex-M4 microcontroller under various operating conditions.

关键词： power factor robust control microcontrollers voltage control photovoltaic power systems power grids static VAr compensators neural nets least squares approximations power engineering computing performance enhancement grid voltage distortion least-square control scheme grid-interfaced solar photovoltaic system robust control algorithm grid voltage disturbances Newton learning rule least-square estimation single-layer neural network structure photovoltaic unit weight-updating mechanism fundamental load current component maximum active power unity power factor distribution static compensator current harmonics attenuation current suppression device utilisation factor GIPV system solar irradiance sim-power system tool utility grid-AC load Matlab-Simulink software Arm Cortex-M4 microcontroller power 500 0 W

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Air-fuel ratio control of lean-burn SI engines using the LPV-based fuzzy technique

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IET control THEORY AND APPLICATIONS 2018年第10期12卷 1414-1420页

作者： Wu, Hsiu-Ming Tafreshi, Reza Natl Chin Yi Univ Technol Dept Mech Engn Taichung Taiwan Texas A&M Univ Qatar Mech Engn Program Doha 23874 Qatar

control of air-fuel ratio (AFR) plays a key role in the minimisation of the carbon dioxide and harmful pollutant emissions and maximisation of fuel economy. An inherent time-varying delay existing in lean-burn spark ignition (SI) engines is a major challenge for the AFR control. Herein an unstable internal dynamics with a parameter dependent system caused by time delay is established to represent a dominating feature of AFR. The proposed control scheme, LPV-based fuzzy control technique, combines the features of LPV and fuzzy control to deal with the unstable internal dynamics of an AFR system with external disturbances and a high level of uncertainty in system parameters. Based on the desired error dynamics, an LPV dynamic error consisting of the unstable state and the AFR tracking error is determined. Subsequently, the proposed fuzzy control algorithm through a look-up table is used to stabilise the LPV dynamic error. Then, the tracking error moves along the desired error dynamics towards zero. The system stability is assured via Lyapunov stability criteria. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed control scheme under different operating conditions. Also, compared with the baseline controller, i.e. proportional-integral controller with Smith predictor, demonstrates its superiority.

关键词： ignition Lyapunov methods time-varying systems fuel economy fuzzy control nonlinear control systems multivariable control systems internal combustion engines robust control fuzzy systems stability criteria fuzzy set theory air pollution control delay systems linear parameter varying systems uncertain systems sparks air-fuel ratio control AFR control unstable internal dynamics parameter dependent system parameter dependent problem fuzzy system theory robust control algorithm nonlinear mapping AFR system LPV dynamic error unstable state AFR tracking error fuzzy control algorithm system stability baseline controller proportional-integral controller linear parameter varying technique system parameter uncertainty error dynamics pollutant emission minimization carbon dioxide minimization fuel economy maximization inherent time-varying delay lean-burn SI engines lean-burn spark ignition engines gain-scheduled multivariable controller systematic strategy linguistic rules LPV-based fuzzy control technique look-up table Lyapunov stability criteria Smith predictor

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An intelligent method for high precision oil measurement control

An intelligent method for high precision oil measurement con...

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IEEE International Conference on Intelligent Processing Systems

作者： Feng, DQ Yin, JY Chen, TJ Lin, M Zhengzhou Univ Technol Inst Informat & Control Zhengzhou 450002 Peoples R China

ISBN: (纸本)0780342534

An intelligent method applied to an oil loading measurement control system in Luoyang Petroleum Industrial General Factory (LPIGF) is introduced in this paper. A neural network algorithm is used to model the flowmeters which can modify the coefficients of flowmeter and improve the measure precision intelligently. A robust control algorithm is designed to improve the control precision, which can adjust the action time of the valves to guarantee the amount of the loading oil. The system is cost low, but both measurement and control precision can meet the user's demands. The system is successfully applied in practice.

关键词： petroleum industry oil technology intelligent control flow measurement flowmeters robust control neurocontrollers intelligent method high precision oil measurement control oil loading measurement control system Luoyang Petroleum Industrial General Factory neural network algorithm flowmeters measurement precision robust control algorithm control precision action time user demands

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