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Aperiodic event-triggered robust model predictive control for linear parameter-varying system with round-robin protocol

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INTERNATIONAL JOURNAL OF ROBUST AND NONlinear CONTROL 2022年第1期32卷 227-242页

作者： Wang, Jun Ding, Baocang Wang, Ping Chongqing Univ Posts & Telecommun Coll Automat Chongqing 400065 Peoples R China

This article investigates the robust model predictive control (MPC) problem for networked control systems represented by the linear parameter-varying model, in which an event-triggered strategy and the round-robin (RR) protocol scheduling locate at the sensor-to-controller and controller-to-actuator channels, respectively. By considering the problems of system state immeasurable and communication burden in engineering application, an output feedback controller that combines the aperiodic event-triggered strategy is applied, where the triggering condition is designed in a time-varying fashion. In addition, in order to avoid unexpected data collisions, the RR protocol is utilized to schedule a shared network and guarantee the efficiency of the control system. The controller parameters are obtained by solving an online convex robust MPC optimization problem, and the feasibility of the optimization problem and closed-loop stability are also addressed. The effectiveness of the proposed theoretical results is illustrated by a numerical simulation example.

关键词： aperiodic event-triggered strategy feasibility linear parameter-varying model robust model predictive control round-robin protocol

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A linear Variable parameter Observer-Based Road Profile Height Estimation for Suspension Nonlinear Dynamics Improvements

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第7期72卷 8433-8448页

作者： Zhao, Linhui Qin, Pengliang Gao, Shijin Zhang, Siyuan Vantsevich, Vladimir V. Liu, Zhiyuan Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China Univ Alabama Birmingham Dept Mech Engn Birmingham AL USA

The accuracy of road profile height estimation has a significant impact on suspension control quality, which in turn influences handling, stability of vehicle motion and occupants' comfort. Based on our previous published work, this article designs and proposes a novel method for road profile estimation that is based on suspension nonlinear dynamics. The proposed approach first establishes the road profile height estimation model considering a real suspension design, including nonlinear characteristics of the shock absorber, bushing stiffness and damping, actual geometry of the control arms, and positioning of absorber. Unlike the conventional sprung-unsprung mass model, the real nonlinear suspension model provides much higher output prediction accuracy as computational and experimental results demonstrated. Moreover, the established estimation method and model require only two easily measurable parameters, which are the acceleration of the sprung mass and the travel of the shock absorber. While considering nonlinearity and hysteretic characteristics of the damping force, the proposed approach is based on the estimation framework of linear parameter-varying systems and establishes a sliding mode observer for estimating the road profile height. The stability of estimation error dynamics of the observer is guaranteed by the Lyapunov stability analysis. As bench-test experiments of a hardware implementation of the suspension-wheel unit validated the computational results. The tests demonstrated that the proposed method allows for real-time estimating of the road profile height in different road conditions and electric currents of the suspension damper and is robust to uncertainty of the payload.

关键词： Road profile height estimation suspension nonlinear dynamics linear parameter-varying model sliding mode observer experimental verification and validation

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Nonlinear Disturbance Observer with Sliding Mode Control for a Fabric Soft Robotic Arm 4

Nonlinear Disturbance Observer with Sliding Mode Control for...

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4th modeling, Estimation, and Control Conference (MECC)

作者： Qiao, Zhi Tao, Weijia Zhang, Wenlong Arizona State Univ Sch Mfg Syst & Networks Mesa AZ 85212 USA

Soft robotics, renowned for their flexibility, have become increasingly prevalent in interaction tasks due to their inherent compliance. For practical applications, simplified low-dimensional models are conventionally employed to represent soft robot dynamics;however, these models suffer from large uncertainties which present significant challenges for control design. This paper introduces a robust control strategy that combines a linear parameter-varying (LPV) model with a nonlinear disturbance observer-based sliding mode control (NDOSMC). The NDOSMC consists of a first-order LPV dynamic model of the soft actuator, a fast nonlinear disturbance observer for precise and rapid disturbance estimation, and a sliding mode control for improved robustness against uncertainties. The closed-loop tracking control system is proven to be asymptotically stable. The proposed algorithm is applied in a trajectory tracking task for a two-segment inflatable soft robot arm. The effectiveness of the controller is validated through both simulation and experimental results. Notably, reductions in the root mean square errors, averaged over seven experimental trials, are observed across all state variables. This demonstrates the controller's high trajectory tracking accuracy, even in the presence of underlying model uncertainties. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Soft Robotics Nonlinear disturbance observer sliding mode control linear parameter-varying model motion control

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LPV Unknown Input Observer-Based Estimation of Driver Intervention Torque and Vehicle Dynamics for Human-Machine Shared Driving 22

LPV Unknown Input Observer-Based Estimation of Driver Interv...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Nguyen, Anh-Tu Guerra, Thierry-Marie Lu, Jiyun Pan, Juntao Taghavifar, Hamid Univ Polytech Hauts France Lab LAMIH UMR CNRS 8201 Valence France North Minzu Univ Sch Elect & Informat Engn Yinchuan Ningxia Peoples R China Concordia Univ Montreal PQ Canada

ISBN: (纸本)9781713872344

This paper presents an unknown input observer (UIO)-based method to jointly estimate the vehicle dynamics and the driver torque within the framework of human-machine shared driving. To deal with the time-varying vehicle longitudinal speed, the vehicle dynamics is represented as a linear parameter-varying (LPV) model. Based on an unknown input (UI) decoupling technique, an LPV observer is designed, which can guarantee an asymptotic estimation performance of both the vehicle dynamics and the driver torque. Via Lyapunov stability theory, we propose sufficient conditions, expressed in terms of linear matrix inequalities, for LPV unknown input observer design. High-fidelity Simulink-CarSim co-simulations are carried out to show the effectiveness of the proposed LPV UIO-based estimation method for driver-automation shared driving dynamics. Moreover, a comparative study is performed with a recent LPV method to highlight the practical interest of the new estimation solution. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Vehicle dynamics sideslip angle estimation torque estimation unknown input observer driver steering intervention linear parameter-varying model

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Nonlinear Disturbance Observer with Sliding Mode Control for a Fabric Soft Robotic Arm

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IFAC-PapersOnLine 2024年第28期58卷 516-521页

作者： Zhi Qiao Weijia Tao Wenlong Zhang School of Manufacturing Systems and Networks Arizona State University Mesa AZ 85212 USA

Soft robotics, renowned for their flexibility, have become increasingly prevalent in interaction tasks due to their inherent compliance. For practical applications, simplified low-dimensional models are conventionally employed to represent soft robot dynamics; however, these models suffer from large uncertainties which present significant challenges for control design. This paper introduces a robust control strategy that combines a linear parameter-varying (LPV) model with a nonlinear disturbance observer-based sliding mode control (NDOSMC). The NDOSMC consists of a first-order LPV dynamic model of the soft actuator, a fast nonlinear disturbance observer for precise and rapid disturbance estimation, and a sliding mode control for improved robustness against uncertainties. The closed-loop tracking control system is proven to be asymptotically stable. The proposed algorithm is applied in a trajectory tracking task for a two-segment inflatable soft robot arm. The effectiveness of the controller is validated through both simulation and experimental results. Notably, reductions in the root mean square errors, averaged over seven experimental trials, are observed across all state variables. This demonstrates the controller’s high trajectory tracking accuracy, even in the presence of underlying model uncertainties.

关键词： Soft Robotics Nonlinear disturbance observer sliding mode control linear parameter-varying model motion control

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LPV Unknown Input Observer-Based Estimation of Driver Intervention Torque and Vehicle Dynamics for Human-Machine Shared Driving

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IFAC-PapersOnLine 2023年第2期56卷 5679-5684页

作者： Anh-Tu Nguyen Thierry-Marie Guerra Jiyun Lu Juntao Pan Hamid Taghavifar Laboratory LAMIH UMR CNRS 8201 Université Polytechnique Hauts-de-France Valenciennes France School of Electrical and Information Engineering North Minzu University Yinchuan China Concordia University Montreal Canada

关键词： Vehicle dynamics sideslip angle estimation torque estimation unknown input observer driver steering intervention linear parameter-varying model

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Design and Simulation of MPC for Marine Three-shaft Gas Turbine 6th

Design and Simulation of MPC for Marine Three-shaft Gas Turb...

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6th IFAC Conference on Engine Powertrain Control, Simulation and modeling (E-COSM)

作者： Li, Jian Wang, Zhitao Li, Shuying Harbin Engn Univ Power & Energy Engn Dept Harbin Heilongjiang Peoples R China

Maneuverability is one of the key indicators for the marine power plant. In order to improve the maneuverability, a speed controller using model predictive control (MPC) technique for a marine three-shaft gas turbine is designed in this paper. The linear parameter-varying(LPV) model is established by analyzing the performance of gas turbine. Make use of the LPV model as the prediction model to design a model predictive controller. On that basis, the Kalman filter is combined with MPC algorithm to improve the robustness of the speed controller. The simulation results show that the designed speed controller can effectively improve the maneuverability of marine gas turbine. Copyright (C) 2021 The Authors.

关键词： model prediction control Kalman filtering three-shaft gas turbine linear parameter-varying model maneuverability marine propulsion

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An Online Data-Driven LPV modeling Method for Turbo-Shaft Engines

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ENERGIES 2022年第4期15卷 1255-1255页

作者： Gu, Ziyu Pang, Shuwei Zhou, Wenxiang Li, Yuchen Li, Qiuhong Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Jiangsu Prov Key Lab Aerosp Power Syst Nanjing 210016 Peoples R China

The linear parameter-varying (LPV) model is widely used in aero engine control system design. The conventional local modeling method is inaccurate and inefficient in the full flying envelope. Hence, a novel online data-driven LPV modeling method based on the online sequential extreme learning machine (OS-ELM) with an additional multiplying layer (MLOS-ELM) was proposed. An extra multiplying layer was inserted between the hidden layer and the output layer, where the hidden layer outputs were multiplied by the input variables and state variables of the LPV model. Additionally, the input layer was set to the LPV model's scheduling parameter. With the multiplying layer added, the state space equation matrices of the LPV model could be easily calculated using online gathered data. Simulation results showed that the outputs of the MLOS-ELM matched that of the component level model of a turbo-shaft engine precisely. The maximum approximation error was less than 0.18%. The predictive outputs of the proposed online data-driven LPV model after five samples also matched that of the component level model well, and the maximum predictive error within a large flight envelope was less than 1.1% with measurement noise considered. Thus, the efficiency and accuracy of the proposed method were validated.

关键词： turbo-shaft engine linear parameter-varying model data-driven method online sequential extreme learning machine (OS-ELM)

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Soft sensor for an orifice flowmeter in presence of disturbances

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FLOW MEASUREMENT AND INSTRUMENTATION 2022年 86卷

作者： Vemulapalli, Sravani Venkata, Santhosh Krishnan Manipal Acad Higher Educ Manipal Inst Technol Ctr Excellence Cyber Phys Syst Dept Instrumentat & Control Engn Manipal India

Measurements using an orifice flowmeter are widely used in industry. In certain instances, the output of the flowmeter may be corrupted due to plate contamination, changes in fluid density, or incorrect insertion of the plate. This paper describes a method for estimating the correct output in the presence of such disturbances. First, a linear parameter-varying model of the orifice flowmeter is developed using data extracted from computational fluid dynamics simulations. The simulation and experimental output are found to have an average deviation of 6.5% and 3.49% in terms of the differential pressure and discharge coefficient, respectively. Observer-based estimators for the linear parameter-varying models are developed for different combinations of the settling time and maximum overshoot. These estimators enable the disturbance-induced output to be corrected close to the true value. The error in the disturbed output due to plate contamination is reduced from 45% to 0.82%. Similarly, the error due to an accidental change of plate decreases from 76% to 2.03%. Thus, the proposed estimator can be used to nullify the disturbances induced in the measurements from orifice flowmeters.

关键词： Flow measurement Orifice meter Data-driven modeling linear parameter-varying model Soft sensor

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Design and Simulation of MPC for Marine Three-shaft Gas Turbine

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IFAC-PapersOnLine 2021年第10期54卷 382-387页

作者： Jian Li Zhitao Wang Shuying Li Power and Energy Engineering Department Harbin Engineering University Heilongjiang China

关键词： model prediction control Kalman filtering three-shaft gas turbine linear parameter-varying model maneuverability marine propulsion

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