Article
Automation & Control Systems
Tao Dong, Xiaomei Gong, Aijuan Wang, Huaqing Li, Tingwen Huang
Summary: This article proposes a solution to the tracking control problem of multiagent systems (MASs) with unknown dynamics. By designing a compensator, an augmented neighborhood error system is proposed. Then, a multithreading iterative $Q$-learning algorithm is developed to transform the tracking control problem into the optimal regulation of the augmented error system.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Ronghu Chi, Yangchun Wei, Rongrong Wang, Zhongsheng Hou
Summary: This study proposes an observer-based iterative learning control method for accurate consensus tracking in nonlinear nonaffine multi-agent systems. By estimating the uncertainty of nonrepetitive initial states and disturbances and utilizing I/O data, it aims to reduce the influence of nonrepetitive initial values and measurement noises in iterative learning control.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Computer Science, Information Systems
Na Lin, Ronghu Chi, Biao Huang
Summary: This work proposes a data-driven optimal set-point control (DDOSC) scheme for nonlinear non-affine systems, which bypasses the challenges of modeling complex processes. The proposed method adopts an ideal nonlinear set-point control function in the outer loop, which is transformed into a linear parametric control law using dynamic linearization (DL), and then updated through a parameter updating law. Simulation results demonstrate the effectiveness of the proposed method in improving the performance of the local feedback controller.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Huimin Zhang, Ronghu Chi, Biao Huang
Summary: This work addresses the problems of predictive compensation, unknown nonlinearity, and nonaffine structure in quantized iterative learning control (QILC) under a data-driven framework. It proposes a compensation strategy to improve data transmission quality and utilizes a dynamic linearization methodology to transform the nonlinear plant into a virtual iterative linear data model. The proposed predictive compensation-based QILC is optimized using quadratic functions and can be extended to MIMO nonlinear nonaffine discrete-time systems.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Mathematics, Interdisciplinary Applications
Yizhao Zhan, Shengxiang Zou, Xiongxiong He, Mingxuan Sun
Summary: This article focuses on incremental adaptive control for nonlinear systems in nonaffine form, developing incremental adaptive mechanisms and presenting corresponding control schemes to avoid numerical integration. The use of the implicit function theorem helps solve the intractability problem of the nonaffine structure, with robustness of tracking error characterized and effectiveness of the control design verified through numerical results.
Article
Automation & Control Systems
Yu Hui, Ronghu Chi, Yang Liu
Summary: In this work, two challenging problems of iterative learning control (ILC) are addressed: the assumption on repetitive conditions and the dependence on linear or nonlinear parametric models. A data-based analysis method is presented for a general multi-input multi-output nonaffine nonlinear system with multiple nonrepetitive uncertainties. The method constructs an extended iterative dynamic relationship to extract the linear relationship between I/O dynamics of a nonaffine nonlinear system. Simulations validate the effectiveness of the theoretical results.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Yang Yang, Jie Tan, Dong Yue, Yu-Chu Tian, Yusheng Xue
Summary: This study introduces an output-based containment control strategy for a class of nonaffine nonlinear MASs, addressing uncertainties and directed topology. Different techniques are utilized for control and correction of MAS, showing that by selecting appropriate parameters, convergence errors of followers can be confined to a small neighborhood around the origin, demonstrating effective control performance.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Hong-Gui Han, Chen-Yang Wang, Hao-Yuan Sun, Hong-Yan Yang, Jun-Fei Qiao
Summary: In this article, a fuzzy neural network-based iterative learning model predictive control (FNN-ILMPC) is designed for complex nonlinear systems. The controller considers the impact of external disturbances, effectively eliminates their influence, and ensures system stability.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Guowei Dong, Hongru Ren, Deyin Yao, Hongyi Li, Renquan Lu
Summary: The article investigates leader-following consensus fault-tolerant control for multiagent systems with time-varying nonaffine nonlinear faults. The use of dynamic surface control technique, fuzzy logic systems, and backstepping technique helps solve the complexity explosion problem and ensure synchronized output signals of all followers and leader. The proposed distributed consensus fuzzy controller guarantees that all variables of MASs are uniformly ultimately bounded, as demonstrated through simulation results.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Zhongwen Cao, Ben Niu, Guangdeng Zong, Xudong Zhao, Adil M. M. Ahmad
Summary: This article investigates the active disturbance rejection-based distributed event-triggered bipartite consensus problem of nonaffine nonlinear multiagent systems with input saturation. An event-triggered mechanism is employed for each follower to reduce the update frequency of the control signal. The active disturbance rejection technology, a combination of the extended state observer and the tracking differentiator, is introduced to estimate uncertainties and address complexity issues in the control law design.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Yue Li, Qing Gao, Ruohan Yang, Hao Liu
Summary: This paper studies the problem of finite-time formation tracking control for networked nonaffine nonlinear systems with unmeasured dynamics and unknown uncertainties/disturbances. A unified distributed control framework is proposed, and various control methods such as adaptive backstepping control, dynamic gain control, and dynamic surface control are integrated. The effectiveness of the proposed control scheme is demonstrated through simulation results.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Computer Science, Information Systems
Shuai Zhang, Jiaxi Chen, Chan Bai, Junmin Li
Summary: This paper proposes a new global fuzzy iterative learning scheme for nonlinear multi-agent systems with unknown dynamics. Unlike traditional design schemes, where fuzzy systems are used as feedback compensators, this scheme utilizes fuzzy systems as feedforward compensators to describe the unknown dynamics, thus avoiding restrictions on the control system states. In this scheme, a hybrid fuzzy adaptive learning controller is designed based on the characteristics of the network structure. The effectiveness of this hybrid learning protocol is verified through simulations.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Xian Yu, Zhongsheng Hou, Marios M. Polycarpou, Li Duan
Summary: This article discusses the tracking control of unknown nonlinear nonaffine repetitive discrete-time multi-input multi-output systems, and proposes two data-driven iterative learning control schemes based on dynamic linearization data models. The learning control gain matrixes of the learning controllers are optimized through the steepest descent method, and the effectiveness of the approaches is verified through numerical simulation and experiments conducted on a Gantry-type linear motor drive system.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Hongjing Liang, Guangliang Liu, Huaguang Zhang, Tingwen Huang
Summary: This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. The use of radial basis function neural networks to approximate unknown nonlinear functions and a dynamic signal to handle design difficulties in unmodeled dynamics were highlighted. A novel event-triggered control protocol was proposed to reduce communication burden and achieve convergence of follower outputs to a neighborhood of the leader's output, while ensuring bounded signals in the closed-loop system. An illustrative simulation example was provided to verify the efficacy of the proposed algorithms.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Panpan Gu, Hong Wang, Liping Chen, Zhaobi Chu, Senping Tian
Summary: This paper studies the consensus for multi-agent systems with one-sided Lipschitz nonlinearity using the iterative learning control approach. The P-type and D-type learning schemes with initial state learning are introduced. The convergence conditions of the consensus algorithms are presented and analyzed under a directed communication graph, utilizing the one-sided Lipschitz and quadratically inner-bounded constraints. It is shown that both algorithms can achieve perfect consensus tracking on a fixed finite-time interval. The correctness of the obtained results is illustrated with simulation examples.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Yue Cao, Nabil Magbool Jan, Biao Huang, Yalin Wang, Zhuofu Pan, Weihua Gui
Summary: The primary goal of multimodal process monitoring is to detect abnormalities or occurrence of faults. This paper proposes a Gaussian mixture model based variational Bayesian principal component analysis (GMM-VBPCA) method that combines global GMM and local VBPCA models to better characterize normal multimodal processes. The monitoring statistics of KL divergence and model residuals are used to detect fault occurrences.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yu Hui, Ronghu Chi, Biao Huang, Zhongsheng Hou
Summary: This study addresses the challenges of heterogeneous dynamics, strongly non-linear and non-affine structures, and cooperation-antagonism networks in multi-agent systems output consensus. It introduces a heterogeneous linear data model and an adaptive learning consensus protocol to improve system performance effectively.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Ronghu Chi, Huaying Li, Na Lin, Biao Huang
Summary: In this work, a data-driven indirect iterative learning control (DD-iILC) is proposed for a repetitive nonlinear system using a proportional-integral-derivative (PID) feedback control in the inner loop. A linear parametric iterative tuning algorithm for the set-point is developed from an ideal nonlinear learning function using an iterative dynamic linearization (IDL) technique. An adaptive iterative updating strategy of the parameter in the linear parametric set-point iterative tuning law is presented by optimizing an objective function for the controlled system. The convergence is proven using contraction mapping and mathematical induction, and the theoretical results are verified through simulations.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Junyao Xie, Oguzhan Dogru, Biao Huang, Chris Godwaldt, Brett Willms
Summary: Data-driven soft sensors have been widely used in the process industry for quality variable estimation. However, building reliable soft sensors for complex industrial processes under limited data conditions is challenging. To address this issue, we propose a reinforcement learning framework that leverages samples from source domains to solve the cross-domain soft sensor problem. The proposed framework incorporates a method for sample selection and soft sensor design, taking into account correlation and estimation error metrics.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Automation & Control Systems
Wentao Bai, Fan Guo, Lei Chen, Kuangrong Hao, Biao Huang
Summary: This article proposes a robust variational Bayesian algorithm for identifying piecewise autoregressive exogenous systems with time-varying time-delays. To mitigate the effects of outliers, a $t$-distribution is used to model the noise probability distribution. A solution strategy for accurately classifying undecidable data points is proposed, using support vector machines to determine the hyperplanes for data splitting. Maximum-likelihood estimation is employed for re-estimating unknown parameters based on the classification results. The time-delay is treated as a hidden variable and identified through the variational Bayesian algorithm. The effectiveness of the algorithm is demonstrated through two simulation examples.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Huaying Li, Ronghu Chi, Zhongsheng Hou, Biao Huang
Summary: This article proposes a higher order indirect adaptive iterative learning control scheme for nonlinear nonaffine systems, which improves the control performance by using a P-type controller and iterative learning to update set points, and introduces an iterative dynamic linearization method to transform into a linear parametric learning controller.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Energy & Fuels
Stefan Jespersen, Zhenyu Yang, Dennis Severin Hansen, Mahsa Kashani, Biao Huang
Summary: To reduce the environmental impact of offshore oil and gas, stricter regulations on hydrocarbon discharge are being implemented. One approach to reducing oil discharge is by improving control systems through the introduction of new oil-in-water sensing technologies and advanced control methods. However, obtaining valid control-oriented models for de-oiling hydrocyclones has proven to be challenging, as existing models are often based on droplet trajectory analysis and do not account for the dynamics or require the measurement of droplet size distribution.
Article
Automation & Control Systems
Hongtian Chen, Biao Huang
Summary: This study develops three novel data-driven approaches for the development of fault-tolerant soft sensors in automation systems. The approaches, namely MSaS, SSaS, and IMSaS, aim to address the issue of unpredictable faults and their impact on soft sensor performance. MSaS constructs an optimal estimator of faults, SSaS removes influences from unknown sensor faults using a constructed subspace, and IMSaS is an improved version of MSaS that eliminates the effects of past prediction errors. These fault-tolerant soft sensors rely solely on system measurements and are evaluated through performance analysis and case studies.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Vamsi Krishna Puli, Biao Huang
Summary: Extraction of underlying patterns from measured variables is important for data-driven control applications. The proposed model can separate oscillating patterns and nonstationary variations. The methodology is applied to solve a fouling monitoring problem for an industrial oil production process.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Qingyang Dai, Chunhui Zhao, Biao Huang
Summary: Due to frequent changes in operating conditions, industrial processes often exhibit time-varying behaviors, resulting in shifting data distributions. Conventional adaptive methods struggle to distinguish normal shifts from real faults when the distribution shifts widely. This study proposes an incremental variational Bayesian Gaussian mixture model (IncVBGMM) for adaptive monitoring to accommodate the changing data distribution caused by different degrees of time-varying behaviors. The proposed method effectively differentiates various types of faults from normal shifts and adapts to the time-varying dynamics.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Na Lin, Huaying Li, Ronghu Chi, Zhongsheng Hou, Biao Huang
Summary: In this work, a data-driven virtual reference setting learning (DDVRSL) method is proposed to enhance the PD feedback controller of the repetitive nonlinear system. The method utilizes an ideal nonlinear learning law and an iterative adaptation law to estimate parameters and improve robustness against uncertainties. The proposed method does not require exact mechanistic model knowledge and its convergence is proven through mathematical analysis and simulations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Ronghu Chi, Xiaolin Guo, Na Lin, Biao Huang
Summary: This article addresses the challenges of data-driven control design in the presence of strong uncertainties, hard nonlinearities, and model dependency. It proposes a dynamic linearization (DL) method and an extended state observer (ESO) to handle an unknown nonlinear nonaffine system. The article presents a modified linear data model (mLDM) that accurately captures the input-output dynamics, including both linear parameter increments and unmodeled uncertainties and disturbances. The theoretical results are mathematically proven and validated through simulations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Shiyong Sun, Ronghu Chi, Yang Liu, Na Lin
Summary: This work presents a new perspective on the consensus tracking issue by considering learning from communicable agents in a strongly connected nonlinear nonaffine multiagent system. A communicable-agent-based linear data model (CA-LDM) is introduced to describe the input-output dynamics between an agent and its neighbors, and an iterative adaptive method is designed to update the CA-LDM using I/O data. Based on this, a communicable-agent-based model-free adaptive iterative learning consensus (CA-MFAILC) scheme is developed by learning the spatial behavior of the MAS and the behavior of the agent itself.
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS
(2023)
Article
Engineering, Electrical & Electronic
ZhenZhen Pan, Ronghu Chi, Zhongsheng Hou
Summary: In this work, a unified data-driven control method is developed for a nonlinear networked multi-agent system to resist three different types of cyber-attacks without modeling processes. The distributed output is defined for each agent to represent its relationship with adjacent agents. The nonlinear dynamics of the distributed output are transformed into a linear data model, and a compensation-based distribute model-free adaptive control (cDMFAC) is proposed to resist unconfined cyber-attacks. The convergence is rigorously proved, and simulation study confirms the effectiveness of the proposed cDMFAC method.
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS
(2023)
Article
Automation & Control Systems
Ronghu Chi, Wenzhi Cui, Na Lin, Zhongsheng Hou, Biao Huang
Summary: This work presents a new sampled-data model-free adaptive control approach for continuous-time systems. The approach utilizes the sampling period and past I/O data to enhance control performance. A sampled-data-based dynamical linearization model is established to address the nonlinearity and nonaffine structure of the continuous-time system. The proposed control approach is data-driven and overcomes the issues caused by model-dependence.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
