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Combined Unfixed-structure and Fixed-structure data-driven Feedforward control Approach for Ball Screw Feed-Drive System

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第2期20卷 2331-2341页

作者： Huang, Tao Kang, Yueting Pi, Yangjun Li, Min Chongqing Univ State Key Lab Mech Transmission Chongqing 400044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China China Univ Geosci Wuhan Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China

Ball screw feed-drive system (BSFDS) is a widely used high-precision positioning mechanism. And its positioning accuracy is strongly limited by friction, vibration, etc. Those factors are difficult to model or to model completely. Since the data-driven control approach is not model-dependent, it is suitable to overcome the previous problems. Iterative learning control (ILC) is one of the general unfixed-structure data-driven approaches, but it is restricted by its residual error. Therefore, this paper proposes a combined unfixed-structure and fixed-structure data-driven feedforward control method. Notably, the unfixed-structure part adopts a standard norm-optimal ILC, and the fixed-structure part is a finite impulse response (FIR) filter structure. Then, the corresponding optimization algorithms are proposed to design an unfixed-structure ILC feedforward controller and to optimize the parameters of the fixed-structure FIR feedforward controller. Furthermore, it is analyzed that the convergence and trajectory tracking performance of the proposed method. The proposed data-driven feedforward control strategy is experimentally validated and compared with a pre-existing FIR approach and standard norm-optimal ILC. The results show that the proposed approach has better convergence and trajectory tracking performance, and represents an effective strategy to improve the positioning accuracy in BSFDS.

关键词： Ball screw data-driven control friction trajectory tracking control vibration

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A data-driven feedforward control combining feedforward tuning and cascaded iterative learning control

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MEASUREMENT 2025年 253卷

作者： Tian, Zongwei Yu, Siyang Chen, Qingrong Yang, Fan Wang, Jian Liu, Jixiao Yang, Jupu Li, Fanxing Yan, Wei Chinese Acad Sci Natl Key Lab Opt Field Manipulat Sci & Technol Chengdu 610209 Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China Univ Chinese Acad Sci Chinese Acad Sci Beijing 101408 Peoples R China

Feedforward control can effectively enhance the accuracy of control systems. This paper introduces a data-driven feedforward control method that combines iterative feedforward parameter tuning with cascaded iterative learning control (CILC). The proposed approach employs a feedforward parameterization technique with an input shaping filter (CFT) to obtain an optimal feedforward controller, effectively eliminating errors induced by the reference trajectory and significantly enhancing extrapolation capability for trajectory variations. CILC builds upon standard iterative learning control (ILC) by incorporating an external iteration loop, which more fully utilizes the ability of ILC to suppress repetitive disturbances in the system, and further improve control accuracy. The proposed method integrates the flexibility of iterative feedforward tuning with the high tracking accuracy of CILC and is validated through theoretical analysis and simulation. Additionally, experiments conducted on a wafer stage confirm the effectiveness and practical value of this approach.

关键词： Feedforward control data-driven control Wafer stage

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data-driven Safe control of Discrete-Time Non-Linear Systems

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IEEE control SYSTEMS LETTERS 2024年 8卷 1553-1558页

作者： Zheng, Jian Miller, Jared Sznaier, Mario Northeastern Univ ECE Dept Robust Syst lab Boston MA 02115 USA Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Automat Control Lab & NCCR Automat CH-8092 Zurich Switzerland

This letter proposes a framework to perform verifiably safe control of all discrete-time non-linear systems that are compatible with collected data. Most safety-maintaining control synthesis algorithms (e.g., control barrier functions, density functions) are limited to obtaining theoretical guarantees of safety in continuous-time, even while their implementation on real systems is typically in discrete-time. We first present a sum-of-squares based program to prove the existence of an (acausal) control policy that can safely stabilize all possible data-consistent systems. Causal control policies may be extracted by online optimization, and we provide sufficient conditions for the extraction of this control policy in general scenarios. As a specific case, we introduce a method for tractable online controller recovery when convexity assumptions are imposed on the candidate Lyapunov function and safety region descriptor. Discrete-time safe stabilization is demonstrated on three example systems.

关键词： Safety Polynomials Lyapunov methods Trajectory Vectors Stability criteria Asymptotic stability data-driven control discrete-time systems nonlinear systems safety stability sum-of-squares

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Fast data-driven Predictive control for LTI Systems: A Randomized Approach

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IEEE control SYSTEMS LETTERS 2024年 8卷 3416-3421页

作者： Kedia, Vatsal George, Sneha Susan Chakraborty, Debraj Indian Inst Technol Dept Elect Engn Mumbai 400076 India

In this letter, the problem of reducing the computational complexity of a recently developed data-driven predictive control scheme is considered. For this purpose, a randomized data compression technique is proposed, which makes the dimension of the decision variable independent of the recorded data size, thereby reducing the complexity of the online optimization problems in data-driven predictive control to that of classical model-based predictive control. The proposed method outperforms other competing complexity reduction schemes in benchmark tests, while guaranteeing similar control performance and stability properties.

关键词： Trajectory Predictive control Optimization Linear systems data models Computational modeling Computational efficiency Vectors Training Standards data-driven control predictive control for linear systems randomized algorithms

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data-driven predictive control for demand side management: Theoretical and results

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APPLIED ENERGY 2024年 353卷

作者： Yin, Mingzhou Cai, Hanmin Gattiglio, Andrea Khayatian, Fazel Smith, Roy S. Heer, Philipp Swiss Fed Inst Technol Zurich Automat Control Lab Zurich Switzerland Swiss Fed Labs Mat Sci & Technol Empa Urban Energy Syst Lab Dubendorf Switzerland

Demand side management is perceived as a tool to support a secure and reliable energy system operation amid growing integration of renewable energy resources. However, the lack of scalable modeling and control procedures hinders the practical implementation. To address this challenge, this paper proposes a novel signal matrix model predictive control algorithm. Compared to existing data-driven methods, this approach explicitly provides stochastic predictions considering both disturbance and measurement errors with few tuning parameters, ensuring reliability by high-probability constraint satisfaction. The performance is extensively compared with three state-of-the-art algorithms in a space heating case study using a high-fidelity simulator. The results are further validated with physical experiments using the same system that the simulator is based on. To assess transferability, the algorithm is further implemented on diverse controlled systems, including a domestic hot water heating system and a stationary electric battery. The simulation results show that, compared to existing data-driven methods, the proposed approach improves constraint satisfaction and energy savings by up to 90 % and 8 %, respectively. The experimental results further confirm that the algorithm is applicable to multiple tasks of demand side management, with reasonable control performance observed in all case studies.

关键词： data-driven control Building energy management Signal matrix model predictive control Demand side management Space heating Domestic hot water heating

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Orthogonal Polynomial Bases for data-driven Analysis and control of Continuous-Time Systems

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IEEE TRANSACTIONS ON AUTOMATIC control 2024年第7期69卷 4307-4319页

作者： Rapisarda, P. van Waarde, Henk J. Camlibel, M. K. Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intelli NL-9747 AG Groningen Netherlands

We use polynomial approximation theory to perform data-driven analysis and control of linear, continuous-time invariant systems. We transform the continuous-time input trajectories and state trajectories into discrete sequences consisting of the coefficients of their orthogonal polynomial bases representations. We show that the dynamics of the transformed input signals and state signals and those of the original continuous-time trajectories are described by the same system matrices. We investigate informativity, quadratic stabilization, and H-2-performance problems for continuous-time systems. We deal with the case in which machine-precision accuracy in the representation of continuous-time signals can be achieved from the data using a finite number of basis elements, and the case in which the approximation error is nonnegligible.

关键词： Trajectory Approximation error Chebyshev approximation System dynamics Standards Transforms controllability H-2-performance continuous-time linear systems data-driven control informativity polynomial orthogonal basis quadratic stabilization

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data-driven distributed control of input-coupled interconnected systems based on Nash optimality

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International Journal of Industrial and Systems Engineering 2025年第2期49卷 186-205页

作者： Zhang, Dawei Gao, Shouli Xia, Rui Zhao, Dongya Shandong Qingdao266400 China

This paper introduces a data-driven distributed controller for interconnected systems with input coupling of unknown models. The estimation of input coupling terms does not depend on historical data. The complex interconnected systems with input couplings are decomposed into individual subsystems. The proposed strategy not only alleviates computational load, but also optimises the interaction between subsystems, effectively addressing the output oscillations of the system during abrupt reactions of input couplings. The convergence of the control algorithm and the stability of the closed-loop system response are examined, and the efficacy of the proposed control method is validated by comparative simulations. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： data-driven control distributed control dynamic linearisation method input couplings Nash optimality

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Designing Experiments for data-driven control of Nonlinear Systems 24

Designing Experiments for Data-Driven Control of Nonlinear S...

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24th International Symposium on Mathematical Theory of Networks and Systems (MTNS)

作者： De Persis, Claudio Tesi, Pietro Univ Groningen ENTEG Nijenborgh 4 NL-9747 AG Groningen Netherlands Univ Florence DINFO I-50139 Florence Italy

In a recent paper we have shown that data collected from linear systems excited by persistently exciting inputs during low-complexity experiments, can be used to design state- and output -feedback controllers, including optimal Linear Quadratic Regulators (LQR), by solving linear matrix inequalities (LMI) and semidefinite programs. We have also shown how to stabilize in the first approximation unknown nonlinear systems using data. In contrast to the case of linear systems, however, in the case of nonlinear systems the conditions for learning a controller directly from data may not be fulfilled even when the data are collected in experiments performed using persistently exciting inputs. In this paper we show how to design experiments that lead to the fulfilment of these conditions. Copyright (C) 2021 The Authors.

关键词： Nonlinear systems Nonlinear control control system design data-driven control Convex programming

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Dynamic Event-Triggered data-driven control for Discrete-Time Linear Systems

Dynamic Event-Triggered Data-Driven Control for Discrete-Tim...

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第43届中国控制会议

作者： Jingtao Mu Zenghui Hu Xiaowu Mu School of Mathematics and Statistics Zhengzhou University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In this article, we propose a data-driven approach to design dynamic event-triggered state feedback controllers for discrete-time linear systems. By utilizing a sufficiently rich set of offline state measurements and inputs, we initially designed a data-driven state feedback controller to ensure the stability of the closed-loop system from input to state. Secondly, static and a type of dynamic event-triggered mechanisms were considered in system modeling, and the dynamic event triggering mechanism effectively reduces the number of triggers. A sufficient condition for the global asymptotic stability of discrete-time linear systems was established using the Lyapunov method. Finally, the control performance was verified through simulation.

关键词： dynamic event-trigger scheme feedback control discrete-time linear systems data-driven control

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data-driven Superstabilizing control Under Quadratically-Bounded Errors-in-Variables Noise

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IEEE control SYSTEMS LETTERS 2024年 8卷 1655-1660页

作者： Miller, Jared Dai, Tianyu Sznaier, Mario Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Automatic Control Lab IfA & NCCR Automat CH-8092 Zurich Switzerland MathWorks Inc Elect Engn Dept Natick MA 01760 USA Northeastern Univ ECE Dept Robust Syst Lab Boston MA 02115 USA

The Errors-in-Variables model of system identification/control involves nontrivial input and measurement corruption of observed data, resulting in generically nonconvex optimization problems. This letter performs full-state-feedback stabilizing control of all discrete-time linear systems that are consistent with observed data for which the input and measurement noise obey quadratic bounds. Instances of such quadratic bounds include elementwise norm bounds (at each time sample), energy bounds (across the entire signal), and chance constraints arising from (sub)gaussian noise. Superstabilizing controllers are generated through the solution of a sum-of-squares hierarchy of semidefinite programs. A theorem of alternatives is employed to eliminate the input and measurement noise process, thus improving tractability.

关键词： Errors-in-variables data-driven control robust control linear systems sum-of-squares

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