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Precise motion tracking of piezo-actuated stages via a neural network-based data-driven adaptive predictive controller

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NONLINEAR DYNAMICS 2023年第20期111卷 19047-19072页

作者： Wang, Yifan Yu, Yewei Shen, Chuanliang Zhou, Miaolei Jilin Univ Dept Control Sci & Engn Changchun 130022 Peoples R China Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China

Piezo-actuated stages have applications in many areas such as aerospace, semiconductor manufacturing, and biotechnology. However, the inherent hysteresis, creep, and vibration of these stages render it challenging to guarantee tracking precision in positioning control. Although various control strategies based on accurate models of piezo-actuated stages have been developed that show remarkable efficacy, the associated complexity in model development and identification, especially when the system exhibits uncertainties, often presents a hurdle to their practical adoption. In this study, we develop a data-driven control method using an adaptive predictive controller that dynamically obtains an equivalent linear model by estimating the pseudo-gradient of the underlying nonlinear dynamics online using only the input/output measurement data. For controller implementation, a radial basis function neural network is adopted to adjust the controller parameters by leveraging its powerful self-learning adaptation. Tracking control experiments illustrate the effectiveness of the proposed method in comparison with the proportional-integral-derivative controller and classical model-free adaptive predictive controller.

关键词： Piezo-actuated stage Hysteresis data-driven control method Adaptive predictive controller Nonlinear dynamics

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Wide-area power system stabiliser based on model-free adaptive control

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IET control THEORY AND APPLICATIONS 2015年第13期9卷 1996-2007页

作者： Lu, Chao Zhao, Yi Men, Kun Tu, Liang Han, Yingduo Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China Power Grid Dept CSG Elect Power Res Inst Guangzhou 510080 Guangdong Peoples R China

The interconnection of regional power grids has resulted in inter-area low-frequency oscillation, which has become a serious problem that threatens the security of power systems. Wide-area power system stabiliser (WAPSS) is an effective device to damp this oscillation. Given that the interconnected multi-machine power system is a strongly non-linear and complex system with time-varying structure and operating conditions, satisfactory control performance of WAPSS is difficult to obtain by using conventional model-based control methods. In this study, a data-driven control method called model-free adaptive control (MFAC) is introduced to WAPSS design. The MFAC algorithm is improved to meet the wide-area damping control requirements in consideration of system disturbances. The stability of the closed-loop system with the improved MFAC-WAPSS is also analysed, and the parameter settings of the improved MFAC algorithm are provided in detail. The improved MFAC-WAPSS can adjust its parameters adaptively only based on the input and output data and avoid the complex system modelling process. The proposed adaptive WAPSS is evaluated on a simplified New England power system. Simulation results show the effectiveness of this new technique.

关键词： power grids power system interconnection power system stability power system security adaptive control nonlinear control systems power system control damping closed loop systems wide-area power system stabiliser model-free adaptive control regional power grid interconnection inter area low-frequency oscillation power system security interconnected multimachine power system nonlinear system complex system time-varying structure operating conditions data-driven control method MFAC-WAPSS wide-area damping control requirements system disturbances closed-loop system stability New England power system

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Model-free adaptive control method for a class of unknown MIMO systems with measurement noise and application to quadrotor aircraft

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IET control THEORY AND APPLICATIONS 2020年第15期14卷 2084-2096页

作者： Liu, Shida Hou, Zhongsheng Zhang, Xin Ji, Honghai North China Univ Technol Sch Elect & Control Engn Beijing 100093 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China China Mobile Hangzhou Informat Technol Co Ltd Hangzhou 310000 Peoples R China

In this work, a novel robust model-free adaptive control (Ro-MFAC) algorithm for a class of unknown multiple-input multiple-output (MIMO) systems with measurement noise is presented. The proposed algorithm, designing by a dynamic linearisation data model with the concept of the pseudo-Jacobian matrix and an adaptive decreasing factor, is a pure a data-driven control method, and only the input-output data are involved for the control system design. The introduction of the adaptive decreasing factor is to attenuate the noise effect on the performance for improving the robustness of the algorithm. The stability of the Ro-MFAC proposed algorithm is proven by rigorous mathematical theory, and the effectiveness of the Ro-MFAC is verified by a series of numerical simulations. Furthermore, the Ro-MFAC is applied to an attitude adjustment problem of a practical quadrotor aircraft for demonstrating the applicability of the proposed approach.

关键词： control system synthesis nonlinear control systems robust control MIMO systems adaptive control Jacobian matrices linear systems helicopters attitude control linearisation techniques model-free adaptive control method unknown MIMO systems measurement noise dynamic linearisation data model pseudoJacobian matrix adaptive decreasing factor data-driven control method input-output data control system design practical quadrotor aircraft robust model-free adaptive control algorithm unknown multiple-input multiple-output systems Ro-MFAC stability mathematical theory altitude adjustment

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Intensive insulin therapy for critically ill subjects based on direct data-driven model predictive control

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JOURNAL OF PROCESS control 2014年第5期24卷 493-503页

作者： Wang, Youqing Fang, Mengqi Jiang, Xu Bequette, B. Wayne Xie, Hongzhi Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Rensselaer Polytech Inst Dept Chem & Biol Engn Troy NY 12118 USA

Patients in the intensive care units (ICU) can suffer from stress-induced hyperglycemia, which can result in negative outcomes and even death. Recent studies show that, regulation of blood glucose (BG) brings in improved outcomes. In this study, a novel direct data-driven model predictive control (MPC) strategy is developed to tightly regulate BG concentration in the ICU. The effectiveness of the proposed direct data-driven MPC strategy is validated on 30 virtual ICU patients, and the in silico results demonstrate the proposed method's excellent robustness with respect to intersubject variability and measurement noises. In addition, the mean percentage values in A-zone of the control variability grid analysis (CVGA) plots are 14% under the Yale protocol, 67% under the combination of particle swarm optimization (PSO) and MPC method (for short, termed as PSO-MPC method), and 90% under the proposed method. In summary, as a good candidate for full closed-loop glycemic control algorithm, the proposed method has superior performance to the nurse-driven Yale protocol and the closed-loop PSO-MPC method. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Closed-loop glycemic control Intensive insulin therapy Intensive care units (ICU) data-driven control method Model predictive control (MPC)

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Model-free output feedback discrete sliding mode control with disturbance compensation for precision motion systems

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IET control THEORY AND APPLICATIONS 2020年第14期14卷 1867-1876页

作者： Li, Min Tan, Shuhua Xiong, Jiaxi Gan, Jinqiang Zhang, Xinxin China Univ Geosci Wuhan Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Univ Duisburg Essen Dept Mech & Robot D-47057 Duisburg Germany

This study proposes a novel model-free output feedback discrete sliding mode control (OFDSMC) with disturbance compensation to achieve high performance for precision motion systems encountering model uncertainties and disturbances. Traditional discrete sliding mode control design requires model and/or state information. In this study, OFDSMC with disturbance compensation is first developed by using a parametric input-output model such that no system states are needed. The essential four control terms of OFDSMC with disturbance compensation is revealed. Then a data-driven control method is introduced into OFDSMC to eliminate the need for the parametric model. The proposed model-free OFDSMC facilitates a rapid implementation without either a parameter model or a state observer, and achieves good robustness against model uncertainties and disturbances. Finally, three simulation cases are presented to illustrate the effectiveness and enhanced performance of the proposed approach.

关键词： control system synthesis discrete time systems variable structure systems feedback robust control observers nonlinear control systems disturbance compensation data-driven control method parametric model model-free OFDSMC parameter model model uncertainties precision motion systems novel model-free output feedback discrete traditional discrete sliding mode control design parametric input-output model system states essential four control terms

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Local learning-based model-free adaptive predictive control for adjustment of oxygen concentration in syngas manufacturing industry

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IET control THEORY AND APPLICATIONS 2016年第12期10卷 1384-1394页

作者： Hou, Zhongsheng Liu, Shida Yin, Chenkun Beijing Jiaotong Univ Sch Elect & Informat Engn Adv Control Syst Lab Beijing Peoples R China

During the united gas improvement (UGI) gasification process in the syngas industry, the oxygen-enriched technique plays an important role, since the obtained oxygen-enriched air with a high oxygen concentration can enhance the production efficiency of the syngas. However, satisfactory control performance for the oxygen concentration of the oxygen-enriched air is hard to achieve because an accurate dynamical model of the oxygen concentration control process by the first principles is fairly difficult to obtain due to strong non-linearity and unknown disturbances in practice. A novel data-driven control method called compact-form-dynamic-linearisation-based model-free adaptive predictive control approach combined with the local learning (LL-CFDL-MFAPC) is proposed to address the control problem. In LL-CFDL-MFAPC, the online and offline input-output measurement data of the plant are fully and simultaneously utilised during the control process, and the design of the controller is model free by means of compact-form-dynamic-linearisation technique. Moreover, the controller has strong robustness because the prediction mechanism participates in control design and only the input/output measurement data are used. The stability and convergence of LL-CFDL-MFAPC are guaranteed by theoretical analysis under several reasonable assumptions, and simulation experiments using real data collected from a practical UGI gasifier verify that the oxygen concentration control problem can be effectively addressed by the proposed method.

关键词： syngas learning systems adaptive control predictive control oxygen linearisation techniques stability local learning-based model-free adaptive predictive control oxygen concentration adjustment syngas manufacturing industry UGI gasification process oxygen-enriched technique oxygen-enriched air syngas production efficiency oxygen concentration control process data-driven control method compact-form-dynamic-linearisation-based model-free adaptive predictive control approach LL-CFDL-MFAPC online input-output measurement data offline input-output measurement data stability

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Virtual reference feedback tuning applied to cascade control

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IET control THEORY AND APPLICATIONS 2020年第20期14卷 3738-3746页

作者： Remes, Chrystian L. Binz, Ricardo E. Flores, Jeferson, V Campestrini, Luciola UFRGS Fed Univ Rio Grande Sul Sch Engn Porto Alegre RS Brazil

Virtual reference feedback tuning (VRFT) is a data-driven control method that optimises the response of a closed-loop system to reference tracking at the expense of a not optimised response to disturbance rejection. This work presents the application of VRFT to two cascade control structures usually considered in the control literature to improve the overall closed-loop performance for single-input systems. For both structures, the authors derive the optimal and practical filter formulation to cope with the case where the controller to be designed is underparameterised. Also, in the case where the internal controller is at the feedback path, they derive a new approach that uses data from a single experiment and considers a unique reference model. Simulation examples illustrate the importance of using the VRFT filter, along with the performance improvements obtained from both cascade loops in terms of reference tracking and disturbance rejection.

关键词： cascade control feedback closed loop systems control system synthesis nonlinear control systems tuning practical filter formulation internal controller feedback path unique reference model VRFT filter cascade loops reference tracking disturbance rejection virtual reference feedback tuning data-driven control method closed-loop system optimised response cascade control structures control literature closed-loop performance single-input systems optimal filter formulation

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Closed-Loop Glycemic control for Critically Ill Subjects Based on data-driven Model Predictive control

Closed-Loop Glycemic Control for Critically Ill Subjects Bas...

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第三十一届中国控制会议

作者： Xu Jiang,Youqing Wang~* College of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100029

Hyperglycemia is a frequent and serious issue in the intensive care units(ICU),which can result in negative outcomes or even ***-loop glycemic control is a promising direction to deal with this *** reducing the blood glucose level,negative outcomes and even mortality can be *** a closed-loop control method,model predictive control(MPC) performs well in glycemic control due to its super ability of dealing with constraints and time ***,conventional MPC encounters difficulties when it is used in the ICU,because the individualized model of an ICU patient is usually ***,an online subspace identification method(SIM) was used to identify one subject's individualized model;based on this model,MPC was implemented to design the insulin delivery rate *** combination is termed as a SIM-based model predictive control(SIM-MPC) method, categorized as a data-driven control *** effectiveness and robustness of the SIM-MPC method have been validated by using some simulation tests.

关键词： Closed-loop glycemic control intensive care units(ICU) subspace identification model predictive control(MPC) data-driven control method

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