International Journal of Adaptive Control and Signal Processing最新文献

{"title":"Adaptive Super-Twisting Terminal Sliding Mode Observer-Based Stabilizer for Uncertain Nonlinear Time-Delay Systems With Unmatched Disturbances","authors":"Ming-Chang Pai","doi":"10.1002/acs.3994","DOIUrl":"https://doi.org/10.1002/acs.3994","url":null,"abstract":"<div>\u0000 \u0000 <p>The aim of this paper is to propose a novel adaptive observer-based stabilizer for a class of uncertain nonlinear time-delay systems subject to unmatched external disturbances. A terminal sliding mode controller based on an adaptive super-twisting terminal sliding mode observer is developed by using the linear matrix inequality (LMI) technique. Under the proposed scheme, the closed-loop system converges to origin in finite time as the matching condition is satisfied and converges to the uniform ultimate bound in the effects of unmatched external disturbances. The design of the observer and the stabilizer does not require the upper bound of uncertainties and external disturbances. The chattering phenomenon is eliminated. The observer and the stabilizer can be efficiently designed from the solutions of LMIs. Simulation results illustrate the effectiveness of the proposed control scheme.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 5","pages":"1091-1099"},"PeriodicalIF":3.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ℒ\u0000 asso \u0000 \u0000 ℳ\u0000 𝒫\u0000 𝒞\u0000 -Based \u0000 \u0000 \u0000 \u0000 ℒ\u0000 \u0000 \u0000 1\u0000 \u0000 \u0000 Adaptive Control for Uncertain Euler–Lagrange Systems: Guaranteed Stability Robustness and Performance","authors":"Hossein Ahmadian,&nbsp;Heidar Ali Talebi,&nbsp;Iman Sharifi","doi":"10.1002/acs.3957","DOIUrl":"https://doi.org/10.1002/acs.3957","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;The challenge of assessing system states and considering the robot's physical limitations impedes the development of an &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℒ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {mathcal{L}}_1 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; adaptive controller for robotic systems. To solve this challenge, this study proposes an &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℒ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {mathcal{L}}_1 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; adaptive controller based on &lt;i&gt;ℒ&lt;/i&gt;asso &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℳ&lt;/mi&gt;\u0000 &lt;mi&gt;𝒫&lt;/mi&gt;\u0000 &lt;mi&gt;𝒞&lt;/mi&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt; (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℒ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mi&gt;A&lt;/mi&gt;\u0000 &lt;mi&gt;ℒ&lt;/mi&gt;\u0000 &lt;mi&gt;ℳ&lt;/mi&gt;\u0000 &lt;mi&gt;𝒫&lt;/mi&gt;\u0000 &lt;mi&gt;𝒞&lt;/mi&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt;) (for &lt;i&gt;Euler–Lagrange systems&lt;/i&gt;) that combines the method by a &lt;i&gt;Barrier Lyapunov Function&lt;/i&gt;(&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℬ&lt;/mi&gt;\u0000 &lt;mi&gt;ℒ&lt;/mi&gt;\u0000 &lt;mi&gt;ℱ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ mathit{mathcal{BLF}} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) and an &lt;i&gt;adaptive high-gain observer&lt;/i&gt; (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;𝒜&lt;/mi&gt;\u0000 &lt;mi&gt;ℋ&lt;/mi&gt;\u0000 &lt;mi&gt;𝒢&lt;/mi&gt;\u0000 &lt;mi&gt;𝒪&lt;/mi&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt;). In the face of uncertainty, time delay, and inaccessibility of system states, the presented approach establishes a mechanism to compromise between &lt;i&gt;fast adaptation&lt;/i&gt; and &lt;i&gt;robustness&lt;/i&gt;. The &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℬ&lt;/mi&gt;\u0000 &lt;mi&gt;ℒ&lt;/mi&gt;\u0000 &lt;mi&gt;ℱ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ mathit{mathcal{BLF}} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; constrains the system's outputs and adjusts the observer gain to ensure the output estimation error stays within a predetermined range. Then, to increase the prediction accuracy, &lt;i&gt;ℒ&lt;/i&gt;asso &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℳ&lt;/mi&gt;\u0000 ","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 5","pages":"842-861"},"PeriodicalIF":3.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PFEKF Filter-Based Fault Detection and Isolation in Non-Linear Systems","authors":"Ismain Guedaouria,&nbsp;Noureddine Doghmane,&nbsp;Mohamed-Faouzi Harkat","doi":"10.1002/acs.3986","DOIUrl":"https://doi.org/10.1002/acs.3986","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, we propose a robust fault detection method for non-Gaussian non-linear systems, isolating various fault types and accurately estimating faults in robotic systems. Leveraging recent advancements in particle filter algorithms, we adopt the combined particle filter with an extended Kalman filter (PFEKF) for its robustness in mitigating estimation errors in strongly non-linear systems. The PFEKF method, integrating the Monte Carlo method and extended Kalman filter equations, is employed to estimate conditional likelihood. We propose a novel fault detection method that utilizes the PFEKF-based CUSUM rule, and we compare its performance to that of the intelligent particle filter (IPF). We introduce a recursive algorithm to calculate an adaptive threshold that minimizes false alarms and undetected fault rates while enhancing detection speed. A fault isolation method for highly non-linear systems uses the enhanced CUSUM rule, with likelihoods calculated by the PFEKF filter. Finally, we accurately estimate faults in robotic systems using only the PFEKF filter, comparing it with the adaptive exogenous Kalman filter (AXKF) approach.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 5","pages":"982-1003"},"PeriodicalIF":3.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Dehydration Systems: Implementing Model Reference Adaptive Control for Enhanced Efficiency","authors":"Pablo Sánchez–Sánchez,&nbsp;José Guillermo Cebada–Reyes,&nbsp;Aideé Montiel–Martínez,&nbsp;Fernando Reyes–Cortés","doi":"10.1002/acs.3979","DOIUrl":"https://doi.org/10.1002/acs.3979","url":null,"abstract":"<div>\u0000 \u0000 <p>This study presents a robust control framework for enhancing the performance of thermal systems with significant input-induced delays, using a cabin dehydrator as a representative case study. The proposed Model Reference Adaptive Control (MRAC) strategy leverages a reference model to emulate desired system dynamics, enabling adaptive adjustments to maintain optimal performance under varying operating conditions. Stability of the system is rigorously established through Lyapunov-based analysis, ensuring global asymptotic stability. Additionally, the frequency response of the system, characterized using Bode plots, provides critical insights into the bandwidth and responsiveness of the control strategies. A comparative evaluation is conducted with classical PD and PID controllers as well as a simplified MRAC design to highlight the advantages of the complete MRAC framework. The analysis includes stability metrics such as gain and phase margins, offering a quantitative assessment of the robustness of each controller. Experimental validation further evaluates the proposed approach in terms of its effectiveness in regulating temperature and preserving active substances during the dehydration process. The findings underscore the MRAC framework as a promising solution for achieving precise and adaptive thermal regulation in systems subject to delays.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 5","pages":"871-893"},"PeriodicalIF":3.9,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Adaptive State Estimation Model: Kalman Filter Coupled With Neural Networks","authors":"Yuting Bai,&nbsp;Bin Yan,&nbsp;Wei Dong,&nbsp;Xuebo Jin,&nbsp;Tingli Su,&nbsp;Huijun Ma","doi":"10.1002/acs.3982","DOIUrl":"https://doi.org/10.1002/acs.3982","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional motion models often cannot describe real-world motion systems accurately when using the Kalman filter (KF) for target tracking. This paper aims to achieve an adaptive estimation of motion states and proposes a KF coupled with neural networks (NNs). First, an adaptive estimation framework is proposed for motion state recognition and target tracking, which couples different NN models with the classical KF. Second, an adaptive NN filtering algorithm is introduced. This filter utilizes NNs to learn the motion patterns of the target and the total Gaussian probability density of the state sequence and performs iterative updates within the framework of the KF. Finally, simulation results on the KITTI dataset demonstrate the proposed filter's high estimation accuracy. Compared to traditional KFs, this filter achieves the prediction of target states through a data-driven approach, thereby avoiding issues related to fixed motion models and parameters during the filtering process.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 5","pages":"914-926"},"PeriodicalIF":3.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Efficient Neuro-Dynamic Network for Constructing the Pareto Front of Convex Multiobjective Optimization Problems","authors":"M. Abkhizi,&nbsp;M. Ghaznavi,&nbsp;M. H. Noori Skandari","doi":"10.1002/acs.3981","DOIUrl":"https://doi.org/10.1002/acs.3981","url":null,"abstract":"<div>\u0000 \u0000 <p>This article introduces an effective neural network model for addressing convex multiobjective optimization problems, developed using the Karush–Kuhn–Tucker optimality conditions for multiobjective optimization problems. The proposed model is shown to be stable in the sense of Lyapunov and globally convergent to efficient solutions of the original problem. Additionally, a novel algorithm is presented to achieve a uniform approximation of the Pareto frontier. The approach's validity and effectiveness are demonstrated through experimental multiobjective problems. For a thorough comparison with other methods, four metrics including purity, uniformity, coverage, and spacing indicators are used, focusing on the positioning of the non-dominated points. Extensive numerical tests highlight the proposed algorithm's substantial advantages.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 5","pages":"894-913"},"PeriodicalIF":3.9,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Fixed-Time Control for Switched Nonlinear Systems With Actuator Faults","authors":"Shuo Liu,&nbsp;Huaguang Zhang,&nbsp;Hongbo Pang","doi":"10.1002/acs.3971","DOIUrl":"https://doi.org/10.1002/acs.3971","url":null,"abstract":"<div>\u0000 \u0000 <p>This article concerns the adaptive fuzzy fixed-time tracking control of uncertain switched strict-feedback nonlinear systems with external disturbances and actuator faults, which consist of the loss of effectiveness and switched time-varying bias fault. Fuzzy fixed-time control problem of each subsystem is not required to be solvable, an adaptive state-dependent switching law with dwell time and adaptive fuzzy fixed-time fault-tolerant controllers are designed constructively to make the output to track a reference signal in fixed time based on a switched fixed-time command filter. The designed switching law is dependent on adaptive parameter estimation, which can prevent the Zeno behavior from happening. Finally, to verify the effectiveness of the developed method, it is applied to switched RLC circuit system.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 5","pages":"862-870"},"PeriodicalIF":3.9,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Adaptive Control With Global Sliding Behavior: A Dynamic Parametrization Approach","authors":"Mingxuan Sun,&nbsp;Shengxiang Zou,&nbsp;Wei Li","doi":"10.1002/acs.3983","DOIUrl":"https://doi.org/10.1002/acs.3983","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, the problem of data-driven adaptive control is addressed for a general class of non-linear systems. Input/output difference representation is provided for the system undertaken, with which dynamic parametrization is applied for handling the involved non-linearity. By underlining finite difference principle, this article proposes a design method of data-driven adaptive control with guaranteed global sliding behavior, through estimation for the time-varying parameters and compensation for the prediction error. The sliding behavior characterization is presented through an assessment of the resultant closed-loop system by power-rate rule. The derivations for the absolute attracting layer, steady-state error band, and monotone decreasing region of the error dynamics are presented in detail. Numerical simulation is carried out to examine the error behavior and validate the effectiveness of the proposed control scheme.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 5","pages":"927-951"},"PeriodicalIF":3.9,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Simplified Adaptive Fixed Time Control of Pure-Feedback Stochastic Nonlinear Systems Subject to Full State Constraints","authors":"Nan Wang,&nbsp;Fazhan Tao,&nbsp;Pengyu Fan,&nbsp;Mengyang Li,&nbsp;Zhumu Fu","doi":"10.1002/acs.3976","DOIUrl":"https://doi.org/10.1002/acs.3976","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, a novel fixed time adaptive fuzzy control scheme for pure-feedback stochastic nonlinear systems with full state constraints is proposed based on dynamic surface control (DSC) technique and barrier Lyapunov functions (BLFs) method. Firstly, the mean value theorem is utilized to transform the pure-feedback structure of the considered systems into strict-feedback ones, which make it possible for the utilization of backstepping method to design the controller. Then, DSC technique is used to reduce the computational complexity problem caused by backstepping method. Fuzzy logic systems are exploited to approximate the unknown nonlinear functions. Moreover, combine BLFs method with fixed time stability theorem, the fixed time adaptive fuzzy controller is constructed, which guarantees that all states do not violate the prescribed constraints and all signals are semi-globally uniform ultimately bounded. Finally, a simulation example is given to verify the effectiveness of the studied control.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 4","pages":"829-840"},"PeriodicalIF":3.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Finite-Time RL Control for Stochastic Non-Linear Systems With Full State Constraints and Dead Zone Output","authors":"Hongyao Li,&nbsp;Fuli Wang","doi":"10.1002/acs.3980","DOIUrl":"https://doi.org/10.1002/acs.3980","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, the finite-time control problem of adaptive neural network (NN) reinforcement learning (RL) is investigated for the continuous time stochastic non-linear systems with full state constraints and dead zone output. Firstly, the adaptive estimation and smooth approximation technique are introduced to solve the difficulty arising from the dead zone non-linearity. Moreover, to overcome the problem of calculating the explosion caused by the repeated differentiation of the virtual control signals, a finite-time command filter is constructed. Combining the backstepping technique and the identifier-actor-critic RL strategy, an adaptive neural finite-time RL control scheme is proposed for the considered system by constructing the tangent-type time-varying barrier Lyapunov functions (BLFs), which optimizes the tracking performance while ensuring all states do not violate the constraints. Under the proposed control strategy, it is guaranteed that all signals are bounded in probability, and the output of the system can track the reference signal within a finite-time. Finally, the simulation results verify the effectiveness of the proposed scheme.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 4","pages":"818-828"},"PeriodicalIF":3.9,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}