International Journal of Adaptive Control and Signal Processing最新文献_第9页

Data-driven based optimal output feedback control with low computation cost 基于数据驱动的低计算成本优化输出反馈控制

IF 3.9 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-05-13 DOI: 10.1002/acs.3832

Xinyi Yu, Jiaqi Yu, Yongqi Zhang, Jiaxin Wu, Yan Wei, Linlin Ou

{"title":"Data-driven based optimal output feedback control with low computation cost","authors":"Xinyi Yu, Jiaqi Yu, Yongqi Zhang, Jiaxin Wu, Yan Wei, Linlin Ou","doi":"10.1002/acs.3832","DOIUrl":"10.1002/acs.3832","url":null,"abstract":"<div>\u0000 \u0000 <p>A partial model-free, data-driven adaptive optimal output feedback (OPFB) control scheme with low computational cost continuous-time is proposed in this paper. The design objective is to obtain the optimal control law by using measurable input and output data, without some knowledge of system model information. Firstly, the system states are decoupled into measurable and unmeasurable parts, and a new state-space equation is built to estimate the unmeasurable states by using a reduced-order observer. Based on this, a parametrization method is utilized to reconstruct the system states. Subsequently, by using the reconstructed states, the adaptive dynamic programming (ADP) Bellman equations based on policy-iteration (PI) and value-iteration (VI) are presented to solve the control problems with initially stable and unstable conditions, respectively. Then, the convergence of the system is proved. Compared with the early proposed OPFB algorithms, only the unknown internal state needs to be reconstructed. Therefore, the computation cost and design complexity are reduced for the proposed scheme. The effectiveness of the proposed scheme is verified through two numerical simulations. In addition, a practical inverted pendulum experiment is carried out to demonstrate the performance of the proposed scheme.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"38 8","pages":"2790-2809"},"PeriodicalIF":3.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925515","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}

引用次数: 0

Fuzzy finite-time adaptive control of switched nonlinear systems with input nonlinearities 具有输入非线性的开关非线性系统的模糊有限时间自适应控制

IF 3.9 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-05-07 DOI: 10.1002/acs.3819

Huanqing Wang, Miao Tong

引用次数: 0

Probability-guaranteed encoding–decoding-based state estimation for delayed memristive neutral networks with event-triggered mechanism 具有事件触发机制的延迟记忆中性网络基于概率保证的编码-解码状态估计

IF 3.9 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-05-07 DOI: 10.1002/acs.3831

Chen Hu, Shuhua Zhang, Hongyuan Zhao, Lifeng Ma, Jian Guo

{"title":"Probability-guaranteed encoding–decoding-based state estimation for delayed memristive neutral networks with event-triggered mechanism","authors":"Chen Hu, Shuhua Zhang, Hongyuan Zhao, Lifeng Ma, Jian Guo","doi":"10.1002/acs.3831","DOIUrl":"10.1002/acs.3831","url":null,"abstract":"<div>\u0000 \u0000 <p>This article handles the probability-guaranteed state estimation problem for a class of nonlinear memristive neural networks (MNNs) by using an event-triggered mechanism. Both time-varying delays and incomplete measurements are considered in the MNNs dynamics. To mitigate the impact of limited communication bandwidth, a communication protocol is proposed that incorporates an encoding–decoding technique in addition to an event-triggered scheme. The aim is to devise a state estimator that can estimate the states of MNNs, ensuring that the state estimation error falls within the required ellipsoidal area with a desired chance. We obtain sufficient conditions for the feasibility of the addressed problem, where the requested gains can be found iteratively by solving certain convex optimization problems. On the basis of the proposed framework, some issues are further presented to determine locally optimal estimator parameters according to different specifications. Finally, we utilize an illustrative numerical example to show the validity of our provided theoretical results.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"38 8","pages":"2750-2770"},"PeriodicalIF":3.9,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925462","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}

引用次数: 0

Parameters online identification-based data-driven backstepping control of hypersonic vehicles 基于参数在线识别的高超音速飞行器数据驱动反步态控制

IF 3.9 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-05-06 DOI: 10.1002/acs.3829

Shihong Su, Bing Xiao, Lingwei Li, Jingfeng Luo

引用次数: 0

Adaptive dynamic programming and distributionally robust optimal control of linear stochastic system using the Wasserstein metric 利用瓦瑟斯坦度量对线性随机系统进行自适应动态编程和分布稳健优化控制

IF 3.9 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-05-04 DOI: 10.1002/acs.3830

Qingpeng Liang, Jiangping Hu, Linying Xiang, Kaibo Shi, Yanzhi Wu

{"title":"Adaptive dynamic programming and distributionally robust optimal control of linear stochastic system using the Wasserstein metric","authors":"Qingpeng Liang, Jiangping Hu, Linying Xiang, Kaibo Shi, Yanzhi Wu","doi":"10.1002/acs.3830","DOIUrl":"10.1002/acs.3830","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we consider the optimal control of unknown stochastic dynamical system for both the finite-horizon and infinite-horizon cases. The objective of this paper is to find an optimal controller to minimize the expected value of a function which depends on the random disturbance. Throughout this paper, it is assumed that the mean vector and covariance matrix of the disturbance distribution is unknown. An uncertainty set in the space of mean vector and the covariance matrix is introduced. For the finite-horizon case, we derive a closed-form expression of the unique optimal policy and the opponents policy that generates the worst-case distribution. For the infinite-horizon case, we simplify the Riccati equation obtained in the finite-hozion setting to an algebraic Riccati equation, which can guarantee the existence of the solution of the Riccati equation. It is shown that the resulting optimal policies obtained in these two cases can stabilize the expected value of the system state under the worst-case distribution. Furthermore, the unknown system matrices can also be explicitly computed using the adaptive dynamic programming technique, which can help compute the optimal control policy by solving the algebraic Riccati equation. Finally, a simulation example is presented to demonstrate the effectiveness of our theoretical results.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"38 8","pages":"2810-2832"},"PeriodicalIF":3.9,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838847","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}

引用次数: 0

Artificial neural network-based adaptive control for nonlinear dynamical systems 基于人工神经网络的非线性动力系统自适应控制

IF 3.9 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-05-03 DOI: 10.1002/acs.3823

Kartik Saini, Narendra Kumar, Bharat Bhushan, Rajesh Kumar

{"title":"Artificial neural network-based adaptive control for nonlinear dynamical systems","authors":"Kartik Saini, Narendra Kumar, Bharat Bhushan, Rajesh Kumar","doi":"10.1002/acs.3823","DOIUrl":"10.1002/acs.3823","url":null,"abstract":"<div>\u0000 \u0000 <p>This research article presents an artificial neural network (ANN)-based indirect adaptive control method for nonlinear dynamical systems. In this article, a modified Elman recurrent neural network (MERNN) is proposed as an identifier and controller for controlling nonlinear systems. The architecture of the proposed controller is a modified form of the existing Elman recurrent neural network. The parameter training of ANN-based controllers is obtained by using the most popular optimization algorithm which is known as the back-propagation algorithm. A comparative study includes Elman, Diagonal, Jordan, feed-forward neural network (FFNN), and radial basis function network (RBFN)-based controllers to compare with the proposed MERNN controller. To determine the controller's robustness, parameter variations, and disturbance signals have been considered. The performance analysis of the proposed controller is illustrated by two simulation examples. The simulation results reveal that MERNN can not only identify the unknown dynamics of the plant but also adaptively control it compared to the others.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"38 8","pages":"2693-2715"},"PeriodicalIF":3.9,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838823","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}

引用次数: 0

Adaptive super twisting observer-based prescribed time integral sliding mode tracking control of uncertain robotic manipulators 基于规定时间积分滑模跟踪控制的不确定机器人操纵器的自适应超扭曲观测器

IF 3.9 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-04-30 DOI: 10.1002/acs.3824

Hesong Shen, Tangzhong Song, Lijin Fang, Huaizhen Wang, Yue Zhang

{"title":"Adaptive super twisting observer-based prescribed time integral sliding mode tracking control of uncertain robotic manipulators","authors":"Hesong Shen, Tangzhong Song, Lijin Fang, Huaizhen Wang, Yue Zhang","doi":"10.1002/acs.3824","DOIUrl":"10.1002/acs.3824","url":null,"abstract":"<div>\u0000 \u0000 <p>A novel integral sliding mode control (ISMC) strategy combined with an adaptive super twisting observer (ASTO) for an uncertain robotic manipulator tracking control system is presented in this article. The comprehensive uncertainties including both parameter perturbations and external disturbances are considered during the controller design. Firstly, a new nominal control law with prescribed time convergent property based on time varying scaling function is presented for the system without uncertainties. Then this nominal control law constitutes the prescribed time convergent sliding surface for ISMC. As the reaching phase is eliminated in ISMC, leading to the prescribed time stability of the whole control system without uncertainties. Secondly, take the system uncertainties (both the matched and unmatched uncertainties) into consideration, two ASTOs are designed for handling them. So, the lumped uncertainties of the robotic manipulator control system can be well estimated and compensated in finite time with the help of backstepping method. Besides, the finite time convergent adaptive switching gains of the ASTO make the system stable without knowing the bounds of the uncertainties exactly and suppress the chattering phenomenon of control input. Finally, the proposed control algorithm is validated by simulation and experiment on a robotic manipulator. Also, from a quantitative analysis, we testify the proposed control scheme outperforms the compared one in all of the discussed cases of simulation part.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"38 7","pages":"2588-2616"},"PeriodicalIF":3.9,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838825","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}

引用次数: 0

Fixed-time adaptive neural network tracking control for output-constrained high-order systems using command filtered strategy 利用指令滤波策略实现输出受限高阶系统的固定时间自适应神经网络跟踪控制

IF 3.9 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-04-30 DOI: 10.1002/acs.3827

Lian Chen, Junzhong Tang, Song Ling

引用次数: 0

Command filter based input quantized adaptive tracking control for multi-input and multi-output non-strict feedback systems with unmodeled dynamics and full state time-varying constraints 基于指令滤波器的输入量化自适应跟踪控制，适用于具有未建模动态和全状态时变约束的多输入多输出非严格反馈系统

IF 3.9 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-04-30 DOI: 10.1002/acs.3828

Xinfeng Zhu, Jinyu Li

{"title":"Command filter based input quantized adaptive tracking control for multi-input and multi-output non-strict feedback systems with unmodeled dynamics and full state time-varying constraints","authors":"Xinfeng Zhu, Jinyu Li","doi":"10.1002/acs.3828","DOIUrl":"10.1002/acs.3828","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper addresses the problem of adaptive tracking control for multi-input and multi-output (MIMO) non-strict feedback systems with unmodeled dynamics and full state time-varying constraints. To tackle the interference of unmodeled dynamics, the dynamic signal generated by the auxiliary system is used. Hyperbolic tangent function is used as a nonlinear mapping tool to transform the constrained system into an unconstrained one. Hysteresis quantizer is introduced to mitigate the chattering phenomenon and quantization error in the quantization signal. The derivative of virtual signal can be approximated more efficiently by command filter. Furthermore, an error compensation mechanism is established to mitigate the error introduced by the command filter. Unknown nonlinear functions are approximated by radial basis function neural networks (RBFNNs). Stability analysis of the proposed controller is performed through the Lyapunov stability theory and the output tracking error can be constrained within a specified range. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"38 8","pages":"2731-2749"},"PeriodicalIF":3.9,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838796","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}

引用次数: 0

Preview controller synthesis for a class of linear parameter periodic systems 一类线性参数周期系统的预览控制器合成

IF 3.9 4区计算机科学

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-04-30 DOI: 10.1002/acs.3825

Li Li, Yonglong Liao, Yaofeng Zhang

引用次数: 0