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Factorization-Based Information Reconstruction for Enhancing Missing Modality Robustness
IEEE Signal Processing Letters
Year: 2025 | Early Access Article |
In recent years, Multimodal Sentiment Analysis (MSA) has emerged as a prominent research area, utilizing multiple signals to better understand human sentiment. Previous studies in MSA have primarily concentrated on performing interaction and fusion with complete signals. However, they have overlooked the issue of missing signals, which commonly arise in real-world scenarios due to factors such as ...Show More
Output Feedback Bounded Control for Unidirectional Input Constrained Hysteretic Systems With Application to Piezoelectric-Driven Micropositioning Stage
In this study, an observer-based output feedback bounded control strategy is investigated for states unmeasurable nonlinear systems subjected to actuator hysteresis and unidirectional input constraints. The inevitable complex actuators restrictions and hysteresis that exist in practice are considered and transformed by applying a new model transformation operations. To cope with the challenge intr...Show More
Phased Optimal Experience Replay Method
2024 China Automation Congress (CAC)
Year: 2024 | Conference Paper |
In the field of reinforcement learning, experience replay buffers are frequently used. In previous work, most of the experience replay methods used uniform sampling or preferential sampling based on value of TD-errors. However, while these methods can improve data utilization and accelerate training speed, they still fall short in terms of rapid convergence. In this paper we develop a framework fo...Show More
Compound Estimation-Based Output-Feedback Hysteresis Compensation and Sensor Fault-Tolerant Control Strategy: Application to Piezoelectric Micropositioning Stage
IEEE/ASME Transactions on Mechatronics
Year: 2024 | Early Access Article |
A piezoelectric micropositioning stage (PMPS), which is a type of piezoelectric-driven system, exhibits hysteresis characteristics that affect its positioning precision. In addition, the system is sensitive to disturbances, such as environmental noise, system uncertainties, and sensor uncertainties or faults. This study investigated a new hysteresis-compensation fault-tolerant control strategy for...Show More
Event-Triggered Adaptive Trajectory Tracking Control for Hysteretic Nonlinear Systems With Fixed-Time Performance
In this study, a fixed-time event-triggered output feedback control scheme is presented for the nonstrict-feedback nonlinear systems subject to unknown hysteresis. A nonlinear auto-regressive moving average with the hysteresis term (HT-NARMAX) model is constructed to describe the hysteresis behavior. On this basis, a modified state observer is designed to estimate the unavailable states and unknow...Show More
A Localization Method Based on Multi-Area Selection and Error Compensation for Indoor Wireless Sensor Network
Yingzhou Wang;Shuguang Xu;Shun Cheng;Xiuyu Zhang;Miaolei Zhou
Indoor localization technology is affected by the combination of random and systematic errors, which results in the ranging error not obeying a single distribution over a large monitoring area, making it difficult to perform comprehensive modeling. This causes the accuracy problem of indoor positioning technology and becomes the most challenging research hotspot in recent years. To address the afo...Show More
Neural Network Based Iterative Learning Control for Dynamic Hysteresis and Uncertainties in Magnetic Shape Memory Alloy Actuator
Miaolei Zhou;Liangcai Su;Chen Zhang;Luming Liu;Yewei Yu;Xiuyu Zhang;Chun-Yi Su
Magnetic shape memory alloy-based actuator (MSMA-BA) is constructed based on the strain mechanism of MSMA material and the magnetic effect of electric current. It can generate macroscopic deformation with micro-nano scale resolution. However, the rate-dependent and load-dependent hysteresis characteristics in MSMA-BA will reduce the positioning accuracy and hinder its applications. In this study, ...Show More
Iterative Learning Control Based on Neural Network and Its Application to Ni-Mn-Ga Alloy Actuator With Local Lipschitz Nonlinearity
The inherent hysteresis property of Ni-Mn-Ga alloy material is the main reason that affects the positioning accuracy of Ni-Mn-Ga alloy-based actuator. This study proposes an iterative learning control based on feedforward neural network (ILCBFNN) to eliminate the effect of hysteresis on actuator positioning accuracy. In addition, the convergence analysis problem of the system that is subject to sy...Show More
Observer-Based Finite-Time Adaptive Motion Control for Nonlinear Asymmetric Hysteresis Systems in Pure-Feedback Form
This study focused on finite-time adaptive motion control for nonlinear pure-feedback systems subject to unknown asymmetric hysteresis and immeasurable states. Accordingly, a finite-time output-feedback dynamic surface control algorithm based on the inverse hysteresis compensator was developed. A fuzzy state observer was utilized to estimate the immeasurable states. A rate-dependent asymmetric Pra...Show More
Data-Driven Adaptive Tracking Control of Piezoelectric-Actuated Motion Stage Using a Multi-Factor Objective Function
This brief proposes a modified data-driven adaptive tracking control (MDDATC) scheme for a piezoelectric-actuated motion stage (PAMS) via the full form dynamic linearization (FFDL) technique and a multi-factor objective function. First, the FFDL method is employed to build a data model for the PAMS. Then, to improve the transient performance of the PAMS, a novel multi-factor objective function is ...Show More
Sliding Mode Iterative Learning Control With Iteration-Dependent Parameter Learning Mechanism for Nonlinear Systems and Its Application
Miaolei Zhou;Tiannan Li;Chen Zhang;Yewei Yu;Xiuyu Zhang;Chun-Yi Su
In this study, the data-driven sliding-mode iterative learning tracking control problem of a piezoelectric-actuated micro-positioning (PAMP) stage is investigated. To improve the convergence performance of the data-driven sliding mode iterative learning control (DDSILC) method, a novel iteration-dependent parameter learning mechanism is proposed. Subsequently, an enhanced DDSILC (E-DDSILC) scheme ...Show More
Data-Driven Adaptive Control With Hopfield Neural Network–Based Estimator for Piezo-Actuated Stage With Unknown Hysteresis Input
Miaolei Zhou;Yuhe Zhang;Yifan Wang;Yewei Yu;Liangcai Su;Xiuyu Zhang;Chun-Yi Su
In this study, a data-driven adaptive controller (DDAC) is designed for piezo-actuated stages (PASs). The primary objective of the controller design is to achieve high tracking accuracy and ensure system stability in the presence of nonaffine uncertainties and unknown hysteresis in the PAS. The proposed DDAC involves three aspects: 1) description of a PAS as a nonaffine nonlinear discrete-time sys...Show More
Improved Mode-Free Adaptive Control for Piezoelectric Micro-Positioning Platform
2023 6th International Conference on Intelligent Robotics and Control Engineering (IRCE)
Year: 2023 | Conference Paper |
Piezoelectric micro-positioning (PMP) platforms have been widely applied due to their fast response and high resolution. However, the strong nonlinearities in the PMP plat-form seriously degrade the positioning accuracy. In this study, an improved model-free adaptive control (MFAC) method is proposed to solve the nonlinearities in the PMP platform. In the improved MFAC, the classical dynamic linea...Show More
Neural Network Adaptive Control of Magnetic Shape Memory Alloy Actuator With Time Delay Based on Composite NARMAX Model
Magnetic shape memory alloy (MSMA) based actuator plays an important role in the field of micro and nano-fabrication due to its large stroke. However, the inherent hysteresis of MSMA seriously affects the application prospects of MSMA-based actuator in the field of precision positioning. In this study, a composite model is proposed for hysteresis in the MSMA-based actuator by coupling an improved ...Show More
Improved Nonlinear Extended Observer Based Adaptive Fuzzy Output Feedback Control for a Class of Uncertain Nonlinear Systems With Unknown Input Hysteresis
This study focuses on the problem of adaptive fuzzy dynamic surface output feedback control for a class of uncertain nonlinear systems subjected to unknown input hysteresis. A Prandtl–Ishlinskii (PI) model is applied to the uncertain nonlinear system for describing the unknown input hysteresis, making the controller design feasible. In addition, a nonlinear extended state observer (NESO) is design...Show More
Finite-Time Adaptive Quantized Motion Control for Hysteretic Systems With Application to Piezoelectric-Driven Micropositioning Stage
This study addresses the finite-time adaptive output feedback quantized control problem for nonstrict-feedback nonlinear systems subject to unknown hysteresis and time delays. An estimated inverse compensator (EIC) is constructed to mitigate hysteresis by resorting to a generalized Duhem model. A Lyapunov–Krasovskii functional is employed to deal with the uncertainties of time delays. However, the...Show More
Predefined–Time Adaptive Control of a Piezoelectric–Driven Motion System With Time–Varying Output Constraint
Chen Zhang;Yewei Yu;Xiuyu Zhang;Chuanliang Shen;Chun-Yi Su;Miaolei Zhou
This brief investigates the output feedback adaptive tracking control of a piezoelectric–driven motion (PDM) system with time–varying output constraint. The key features of the developed predefined–time adaptive neural control (PTANC) method are as follows. i) A broad learning system with recurrent enhancement node (RENBLS) is used to construct an RENBLS–based observer such that state information ...Show More
Composite Data Driven-Based Adaptive Control for a Piezoelectric Linear Motor
Yifan Wang;Miaolei Zhou;Dawei Hou;Wenjing Cao;Xiaoliang Huang
Piezoelectric linear motors play an important role in ultraprecision manufacturing technology. However, the complex nonlinear relationship between the input and output of the piezoelectric linear motors limits their further application. In this article, to achieve precise motion control for a piezoelectric linear motor, a composite data-driven-based adaptive control method is proposed, consisting ...Show More
The accuracy of the micro-positioning system impelled by a magnetic shape memory alloy-driven actuator (MSMADA), is severely restricted by the frequency-dependent hysteresis nonlinearity. Moreover, the actuating accuracy is further affected by various operating factors such as load and temperature. In this study, a Duhem model (DM) identified online by a Takagi-Sugeno fuzzy neural network (TSFNN-D...Show More
Neural-Network-Based Iterative Learning Control for Hysteresis in a Magnetic Shape Memory Alloy Actuator
The typical characteristic of a magnetic shape memory alloy (MSMA)-based actuator is rate-dependent and stress-dependent hysteresis. In addition, the hysteresis in an MSMA-based actuator includes memory, multivalued mapping, and asymmetry properties, which seriously degrade the positioning accuracy. In this article, a feedforward neural network (FNN)-based nonlinear autoregressive moving average w...Show More
Adaptive output feedback tracking control for piezoelectric-driven micropositioning stage via hysteresis observer
2021 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO)
Year: 2021 | Conference Paper |
This paper presents the precision tracking control for a piezoelectric-driven micropositioning stage (PDMS) by resorting to a new adaptive output feedback control strategy. The dynamics model with the hysteresis state is established for the PDMS, where the Duhem model is used to illustrate the hysteresis behavior. Considering that the information of the hysteresis state is unknown in practice, a n...Show More
Adaptive Output Feedback Dynamic Surface Sliding Mode Control for a Class of Uncertain Nonlinear System with Unknown Actuator Hysteresis
2021 IEEE 7th International Conference on Control Science and Systems Engineering (ICCSSE)
Year: 2021 | Conference Paper |
In this paper, a RBFNNs based output feedback adaptive dynamic surface sliding mode control (DSSMC) method is proposed for a class of uncertain nonlinear system with actuator hysteresis. The main contributions are listed as follows: 1) by comprehensively considering the mutual influence between the tracking errors of each subsystem, a sliding mode dynamic surface is defined. Also, the recursive me...Show More
Data Driven Adaptive Control with Hysteresis Input for a Piezo-Actuated Stage
2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
As a core component in the micro/nano-manufacturing, the piezo-actuated stage has superior performance of ultra-precision positioning, fast response and compact size. However, the inherent nonlinearity, such as hysteresis and creep characteristics, in the piezo-actuated stage impacts its positioning precision severely. To eliminate the impact of the nonlinearity of a piezo-actuated stage, a data d...Show More
Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator Based on Volterra Series
IEEE Transactions on Magnetics
Cited by: Papers (6)
Magnetic shape memory alloy-based actuator (MSMA-BA) has the capability to generate the micro-nano-scale precision positioning, which has unparalleled virtue in contrast with the traditional motion driving mechanism. Nevertheless, the output displacement of the MSMA-BA exhibits complex hysteresis nonlinearity, which hinders its utility in the high-precision positioning field. In this article, an o...Show More
Chaotic Neural Network-Based Hysteresis Modeling With Dynamic Operator for Magnetic Shape Memory Alloy Actuator
IEEE Transactions on Magnetics
Cited by: Papers (11)
The magnetic shape memory alloy (MSMA) is a new family of smart materials, which exhibits great strain deformation and high energy density. Based on these properties, the MSMA has excellent potential to represent an available means for developing a novel generation of actuators in the micro-positioning application. However, the MSMA-based actuator suffers from the inherent hysteresis and it has be...Show More
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