Article
Automation & Control Systems
Serdar Coskun, Cong Huang, Fengqi Zhang
Summary: This article discusses the development of cooperative adaptive cruise control under uncertainty using a model predictive control strategy. The goal is to design a predictive controller that ensures the stability of vehicles under disturbances and to test the cooperative vehicle platooning control under different disturbance scenarios. The computational effectiveness of the proposed control strategy is also verified for potential real-time deployment in next-generation cooperative vehicles.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2021)
Article
Engineering, Civil
Rui Chen, Christos G. Cassandras
Summary: We have developed an event-driven Receding Horizon Control scheme for a Mobility-on-Demand System in a transportation network, which reduces the complexity of the vehicle assignment problem and enables real-time implementation. Simulation results demonstrate the effectiveness of the RH controller in terms of real-time implementation and performance compared to known greedy heuristics.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Shirantha Welikala, Christos G. Cassandras
Summary: The paper addresses a multi-agent persistent monitoring problem and proposes an event-driven receding horizon control approach that automatically optimizes planning horizon length, showing improvements compared to existing solutions.
Article
Automation & Control Systems
Shirantha Welikala, Christos G. Cassandras
Summary: In this paper, we discuss the problem of estimating the states of a distributed network of nodes through a team of cooperating agents. We propose a distributed online agent controller where each agent controls their trajectory by solving a sequence of receding horizon control problems, and we also leverage machine learning to improve the computational efficiency.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Liuquan Yang, Weida Wang, Chao Yang, Xuelong Du, Mingjun Zha, Huibin Yang
Summary: This paper proposes an adaptive receding horizon control method for hybrid electric vehicles, consisting of an optimization layer and a feedback control layer. The optimization layer utilizes a novel adaptive factor to adjust the prediction horizon length and control constraints. The feedback control layer tracks the reference trajectory using a lower-RHC method. Simulation and bench experiment results show that the proposed method reduces the acceleration time and jerk during 0-40 km/h acceleration.
CONTROL ENGINEERING PRACTICE
(2023)
Article
Computer Science, Information Systems
Hazem Issa, Jozsef K. Tar
Summary: Receding horizon controllers are a special approximation of optimal controllers that discretize the continuous time variable over an optimization horizon. They utilize a cost function to calculate contributions at grid points and minimize it under the constraint of the dynamic model. By exerting control force only for one step of the horizon and redesigning the next horizon from the initial state, the effects of modeling errors can be avoided. The suggested solution directly utilizes the dynamic model and achieves fast operation through a transition between gradient descent and Newton-Raphson methods. An overestimated dynamic model is used for optimization, and an approximate dynamic model is used to adaptively track the optimized trajectory for system operation.
Article
Automation & Control Systems
Lianhao Yin, Gabriel Turesson, Per Tunestal, Rolf Johansson
Summary: This paper combines receding horizon sliding control (RHSC) with a state-augmented Kalman filter to address model mismatch and disturbance problems, showing faster convergence rate than MPC in tracking reference signal in an advanced heavy-duty engine air system.
CONTROL ENGINEERING PRACTICE
(2021)
Article
Computer Science, Information Systems
Joaquim Leitao, Carlos M. Fonseca, Paulo Gil, Bernardete Ribeiro, Alberto Cardoso
Summary: The paper addresses the problem of residential load scheduling using optimization techniques, proposing a compressive receding horizon strategy for week-ahead load shifting driven by traditional receding horizon and day-ahead allocation strategy misalignment with weekly household appliance usage patterns. The simulation results confirm the validity of the proposed strategy in the context of household appliance scheduling problems and show competitive electricity costs and resident discomfort performance compared to state-of-the-art approaches.
Article
Computer Science, Artificial Intelligence
Lijie Wang, Jiahong Xu, Yang Liu, C. L. Philip Chen
Summary: This article investigates the optimal consensus control problem for multiagent systems with input constraints. It proposes a single critic neural network with time-varying activation function for approximate optimal control and an improved learning law for weight update. It also designs an effective dynamic event-triggering mechanism to improve the utilization rate of communication resource. A simulation example is provided to support the effectiveness of the proposed method and the superiority of the designed mechanism.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Chen Hu, Ke Yin, Qiang Lu
Summary: This paper proposes a data-driven receding horizon control approach for signal source traversal using a quadrotor in environments with obstacles. The approach combines a data-driven learning method based on Gaussian process and a receding horizon control method to efficiently navigate the quadrotor through signal sources.
Article
Engineering, Electrical & Electronic
Adib Allahham, David Greenwood, Charalampos Patsios, Phil Taylor
Summary: This paper proposes a receding horizon controller for optimal, adaptive scheduling of a battery energy storage system, which incorporates age, condition, and state of charge to evaluate degradation, efficiency, and operating costs online and make adaptive decisions.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Automation & Control Systems
Junfei Qiao, Ruyue Yang, Ding Wang
Summary: This article presents an adaptive batch learning algorithm for optimal control of complex wastewater treatment processes. By learning and evaluating from offline data, the algorithm can improve and optimize control policies to achieve better control performance.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Ding Wang, Peng Xin, Mingming Zhao, Junfei Qiao
Summary: The constrained receding-horizon heuristic dynamic programming (RH-HDP) algorithm is established in this article to address the approximate optimal control problem of nonlinear affine systems with the terminal state constraint and asymmetric control constraints. The approximate optimal control problem is transformed into a battery of subproblems based on the RH mechanism of model predictive control (MPC). The algorithm considers the terminal state constraint and introduces asymmetric control constraints to confine the control input within the given constraint range.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Walter Lucia, Giuseppe Franze, Domenico Famularo
Summary: This paper addresses intelligent traffic management within a smart city environment using an ad-hoc model predictive control strategy based on an event-driven formulation. A low-demanding receding horizon controller is derived through set-theoretic arguments for safety verification, and simulations on the train-gate benchmark system demonstrate the effectiveness and benefits of the proposed methodology.
DISCRETE EVENT DYNAMIC SYSTEMS-THEORY AND APPLICATIONS
(2021)
Article
Automation & Control Systems
Maria Charitidou, Dimos V. Dimarogonas
Summary: This article proposes a continuous-time receding horizon control scheme that encodes the satisfaction of STL tasks using time-varying control barrier functions designed online, thus avoiding integer expressions. The recursive feasibility of the scheme is guaranteed by satisfying a time-varying terminal constraint that ensures task satisfaction with predetermined robustness. The effectiveness of the method is demonstrated in a multirobot simulation scenario.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Engineering, Environmental
Fengxue Zhang, Chunhua Yang, Hongqiu Zhu, Yonggang Li, Weihua Gui
CHEMICAL ENGINEERING JOURNAL
(2020)
Article
Automation & Control Systems
Keke Huang, Yiming Wu, Haofei Wen, Yishun Liu, Chunhua Yang, Weihua Gui
CONTROL ENGINEERING PRACTICE
(2020)
Article
Chemistry, Analytical
Fei Cheng, Chunhua Yang, Can Zhou, Lijuan Lan, Hongqiu Zhu, Yonggang Li
Article
Automation & Control Systems
Keke Huang, Yiming Wu, Chen Wang, Yongfang Xie, Chunhua Yang, Weihua Gui
Summary: A semisupervised robust projective and discriminative dictionary learning method is proposed to address the complexity of real industrial process data, characterized by multimode, high dimensional, corrupted, and less labeled data. The method introduces a semisupervised strategy to label unsupervised training data, utilizes low-rank and sparse features for data decomposition, and extracts features of clean data through a simultaneously projective and discriminative model. The efficiency of this hybrid framework is demonstrated through synthetic examples and real industrial process cases.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
Keke Huang, Yiming Wu, Cheng Long, Hongquan Ji, Bei Sun, Xiaofang Chen, Chunhua Yang
Summary: The study introduces an online dictionary learning method that outperforms traditional methods by adapting to time-varying processes, having lower computational complexity, and more reliably resolving issues in principal component analysis.
Article
Automation & Control Systems
Zhenxiang Feng, Yonggang Li, Bei Sun, Chunhua Yang, Hongqiu Zhu, Zhisheng Chen
Summary: The stability of the roasting temperature is crucial for product quality in the zinc roasting process, but controlling the temperature in a large-scale zinc roaster is challenging due to complex process characteristics, fluctuating working conditions, and delayed detection of product quality. A new trend-based event-triggering fuzzy control strategy has been proposed to improve the utilization of information contained in temperature measurements.
JOURNAL OF PROCESS CONTROL
(2021)
Article
Computer Science, Information Systems
Keke Huang, Haofei Wen, Han Liu, Chunhua Yang, Weihua Gui
Summary: Data-driven process monitoring methods rely on geometry constrained dictionary learning to balance reconstructive and discriminative items. Inspired by the manifold method, discriminative sparse coding is employed to identify samples from the same class.
INFORMATION SCIENCES
(2021)
Article
Engineering, Electrical & Electronic
Chao Yang, Zhiliang Wu, Tao Peng, Hongqiu Zhu, Chunhua Yang
Summary: In this article, a new method for transient fault diagnosis in high-speed train TDCS is proposed, which can handle TF scenarios with small amplitude, short duration, and energy feature pattern. The research overcomes the ambiguity between transient signals and noise through parameter optimization and energy distribution.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Article
Computer Science, Artificial Intelligence
Keke Huang, Ke Wei, Yonggang Li, Chunhua Yang
Summary: This paper proposes a distributed dictionary learning algorithm based on the MapReduce framework for process monitoring in industrial systems. The method efficiently extracts useful information from high-dimensional data and improves the effectiveness and robustness of process monitoring for industrial processes.
APPLIED INTELLIGENCE
(2021)
Article
Automation & Control Systems
Xiaofeng Yuan, Lin Li, Yuri A. W. Shardt, Yalin Wang, Chunhua Yang
Summary: An LSTM network with spatiotemporal attention is proposed for soft sensor modeling in industrial processes, improving prediction performance by identifying important input variables related to the quality variable and discovering quality-related hidden states adaptively.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Computer Science, Artificial Intelligence
Zhaoke Huang, Chunhua Yang, Xiaofang Chen, Xiaojun Zhou, Guo Chen, Tingwen Huang, Weihua Gui
Summary: The study introduces a novel method for multivariate time series (MTS) classification, called functional deep echo state network (FDESN), which overcomes the drawbacks of existing methods through two special operators: temporal aggregation and spatial aggregation. Experimental results demonstrate the superiority of the proposed method and its successful application in anode condition identification in aluminum electrolysis.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Zhiwen Chen, Chang Liu, Steven X. Ding, Tao Peng, Chunhua Yang, Weihua Gui, Yuri A. W. Shardt
Summary: A new method for monitoring and fault detection of multimode processes is proposed in the article, integrating K-means into just-in-time learning to build local models and addressing limitations of traditional canonical correlation analysis methods in handling processes with multiple operating points. Its effectiveness is demonstrated in an industrial benchmark process, showing better fault detection rate compared to conventional methods.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Automation & Control Systems
Peng Lin, Jiahao Xu, Wei Ren, Chunhua Yang, Weihua Gui
Summary: This article investigates a distributed constrained optimization problem in single-integrator multiagent systems with nonconvex input constraints, nonuniform convex state constraints, and nonuniform step sizes, presenting a new analysis approach that proves all agents can converge to a common point and solve the given optimization problem.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Automation & Control Systems
Shijun Deng, Zhiwen Chen, Fuhua Kuang, Chunhua Yang, Weihua Gui
Summary: This study focuses on the optimal control problem of the chilled water system in modern large buildings, proposing an ensemble learning method for cooling load prediction and a new control strategy to reduce energy consumption while meeting cooling load demands. The optimal control strategy is learned in real time using a cloud edge terminal form, leading to significant improvements compared to existing methods, with a 5.59% decrease in energy consumption and monthly savings of 35,645 kWh of electric energy on average.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Yichi Zhang, Chunhua Yang, Keke Huang, Marko Jusup, Zhen Wang, Xuelong Li
Summary: The paper discusses the fundamental problem of reconstructing complex networks from observed data and proposes a novel method that combines the alternating direction method of multipliers and clustering algorithm to overcome the drawbacks of compressive sensing in network reconstruction. Experimental results demonstrate the accuracy and robustness of the proposed method.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2021)