Article
Automation & Control Systems
Antonio Sala, Leopoldo Armesto
Summary: This study introduces a new criterion for adaptive meshing in polyhedral partitions to interpolate value functions, employing an initial condition probability density function, uncertainty propagation, and temporal-difference error to determine the addition of new points. A collection of lemmas justifies the algorithmic proposal, with comparative analysis highlighting the advantages of this proposal over other options in literature. The developed methods are applied in simulation examples and an experimental robotic setup.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jingliang Duan, Zhengyu Liu, Shengbo Eben Li, Qi Sun, Zhenzhong Jia, Bo Cheng
Summary: This paper presents a constrained adaptive dynamic programming algorithm that can directly handle state-constrained nonlinear nonaffine optimal control problems. By transforming the traditional policy improvement process into a constrained policy optimization problem and approximating the policy and value functions with multi-layer neural networks, the algorithm linearizes the constrained optimization problem and obtains optimal updates.
Article
Automation & Control Systems
Yan Wei, Xinyi Yu, Yu Feng, Qiang Chen, Linlin Ou, Libo Zhou
Summary: This paper investigates the issue of event-triggered adaptive optimal tracking control for uncertain nonlinear systems with stochastic disturbances and dynamic state constraints. By utilizing adaptive dynamic programming (ADP) of identifier-actor-critic architecture and event triggering mechanism, the adaptive optimized event-triggered control (ETC) approach for the nonlinear stochastic system is first proposed.
Article
Automation & Control Systems
Yan Wei, Jun Fu, Huaicheng Yan, Minrui Fei, Yueying Wang
Summary: This article investigates the problem of adaptive neural optimal fault-tolerant control for a class of nonlinear uncertain systems subject to dynamic state constraints and external disturbances. It proposes a unified tangent-type nonlinear mapping to handle more general dynamic constraints and a single network adaptive dynamic program method to solve the problem of actuator faults and external disturbances. The effectiveness of the proposed control approach is evaluated through two comparative simulation examples.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Cong Li, Yongchao Wang, Fangzhou Liu, Qingchen Liu, Martin Buss
Summary: This article presents a new formulation for model-free robust optimal regulation of continuous-time nonlinear systems using an incremental adaptive dynamic programming (IADP) approach. It leverages time delay estimation (TDE) technique and measured input-state data to achieve incremental stabilization under uncertainties, disturbances, and saturation. Numerical simulations validate its effectiveness and superiority in reducing energy expenditure and enhancing robustness.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Ziyu Lin, Jingliang Duan, Shengbo Eben Li, Haitong Ma, Jie Li, Jianyu Chen, Bo Cheng, Jun Ma
Summary: The research addresses the challenge of solving the finite-horizon HJB equation, proposes a new algorithm, and validates its effectiveness through simulations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Bo Sun, Erik-Jan van Kampen
Summary: This paper introduces a novel intelligent control scheme that combines global dual heuristic programming with an incremental model-based identifier to tackle the issue of partial observability. Experimental results demonstrate that the developed method outperforms the global model-based method in terms of stability and adaptability.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Jiahui Xu, Jingcheng Wang, Jun Rao, Yanjiu Zhong, Hongyuan Wang
Summary: This article introduces an optimal controller for solving discrete-time nonlinear systems with state constraints, addressing difficult state constraints by introducing a control barrier function. The stability proof and conditions for satisfying state constraints are provided. The proposed method uses an actor-critic network structure to implement and approximate the control barrier function-based adaptive dynamic programming algorithm, and its performance is validated through testing on a simulation example.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Engineering, Electrical & Electronic
Guijie Zhao, Jiayue Sun, Ying Yan, Huaguang Zhang
Summary: In this paper, a costate-adaptive dynamic programming algorithm is proposed to solve a class of nonlinear critical surface problems with input constraints. The algorithm avoids super-dimensional calculation by incubating the model-neural network through data catalysis and transforms the problem into solving the optimal action sequence for the HJB equation.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Computer Science, Artificial Intelligence
Ye Zhou
Summary: GDHP is a comprehensive adaptive critic design that aims to minimize error with respect to cost-to-go and its derivatives simultaneously. This article proposes a novel GDHP design based on a critic network and an associated dual network to increase online learning efficiency while maintaining analytical accuracy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Shan Xue, Biao Luo, Derong Liu, Ying Gao
Summary: The article introduces an event-triggered constrained optimal tracking control algorithm using integral reinforcement learning (IRL) which transforms the problem into an optimal regulation one and solves the Hamilton-Jacobi-Bellman equation. The event-triggering mechanism helps ease the pressure on data transmission, making it suitable for control systems with limited computational and communication resources.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Qinglai Wei, Liao Zhu, Ruizhuo Song, Pinjia Zhang, Derong Liu, Jun Xiao
Summary: In this article, an online adaptive optimal control algorithm based on adaptive dynamic programming is developed to solve the multiplayer nonzero-sum game (MP-NZSG) problem for discrete-time unknown nonlinear systems. The algorithm utilizes a model-free structure and online adaptive learning to approximate the value functions and optimal policies for all players.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Fuyu Zhao, Weinan Gao, Zhong-Ping Jiang, Tengfei Liu
Summary: This article introduces an event-triggered output-feedback adaptive optimal control method for continuous-time linear systems. It reconstructs unmeasurable states and reduces controller updates through an event-based feedback strategy. The iterative solution to the discrete-time algebraic Riccati equation is carried out using event-triggered adaptive dynamic programming, with convergence and closed-loop stability verified using Lyapunov techniques.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Jiahui Xu, Jingcheng Wang, Jun Rao, Shunyu Wu, Yanjiu Zhong
Summary: This article introduces a novel algorithm, OptNet-PGADP, for optimizing the performance of nonlinear systems. The algorithm integrates OptNet and PGADP to tackle control problems in discrete-time nonlinear systems. Simulation and experimental results demonstrate that the algorithm outperforms traditional PGADP and NMPC algorithms in control performance.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ali Heydari
Summary: This study investigates optimal control of nonlinear impulsive systems with free impulse instants, developing a scheme based on adaptive dynamic programming. The scheme handles single and multiple impulsive actuators efficiently and is applied to challenging problems such as spacecraft orbital maneuver.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Mingming Ha, Ding Wang, Derong Liu
Summary: In this article, a novel value iteration scheme is proposed, which introduces a relaxation factor and combines with other methods to accelerate and guarantee the convergence. The theoretical results and numerical examples demonstrate its fast convergence speed and stability.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiong Yang, Yingjiang Zhou, Zhongke Gao
Summary: This paper investigates the robust stabilization problem of a class of nonlinear systems with asymmetric saturating actuators and mismatched disturbances. By constructing a discounted cost function for the auxiliary system, the robust stabilization problem is transformed into a nonlinear-constrained optimal control problem. A simultaneous policy iteration method is then developed in the reinforcement learning framework to solve the nonlinear-constrained optimal control problem.
Article
Computer Science, Artificial Intelligence
Mingduo Lin, Bo Zhao, Derong Liu
Summary: A novel policy gradient (PG) adaptive dynamic programming method is proposed for nonlinear discrete-time zero-sum games with unknown dynamics. A policy iteration algorithm is used to approximate the Q-function and the control and disturbance policies using neural network approximators. The control and disturbance policies are then updated using the PG method based on the iterative Q-function. The experience replay technique is applied to improve training stability and data usage efficiency. Simulation results show the effectiveness of the proposed method.
Article
Computer Science, Artificial Intelligence
Mingming Liang, Derong Liu
Summary: This article presents a novel neural-network-based optimal event-triggered impulsive control method. The proposed method utilizes a general-event-based impulsive transition matrix (GITM) to represent the evolving characteristics of all system states across impulsive actions. Through the developed event-triggered impulsive adaptive dynamic programming (ETIADP) algorithm and its high-efficiency version (HEIADP), the optimization problems for stochastic systems with event-triggered impulsive controls are addressed. The results show that the proposed methods can reduce computational and communication burdens and fulfill the desired goals.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Mingming Ha, Ding Wang, Derong Liu
Summary: Inspired by the successive relaxation method, a novel discounted iterative adaptive dynamic programming framework is developed, which possesses an adjustable convergence rate for the iterative value function sequence. The convergence properties of the value function sequence and the stability of the closed-loop systems under the new discounted value iteration (VI) are investigated. An accelerated learning algorithm with convergence guarantee is presented based on the properties of the given VI scheme. Amidst the implementation of the new VI scheme and its accelerated learning design, value function approximation and policy improvement are involved. The performance of the developed approaches is verified using a nonlinear fourth-order ball-and-beam balancing plant, showing significant acceleration of the convergence rate of the value function and reduction in computational cost compared to traditional VI.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Yongwei Zhang, Bo Zhao, Derong Liu, Shunchao Zhang
Summary: In this article, the event-triggered robust control problem of unknown multiplayer nonlinear systems with constrained inputs and uncertainties is investigated using adaptive dynamic programming. A neural network-based identifier is constructed to relax the requirement of system dynamics. By designing a nonquadratic value function, the stabilization problem is converted into a constrained optimal control problem. The approximate solution of the event-triggered Hamilton-Jacobi equation is obtained using a critic network with a novel weight updating law, and the Lyapunov stability theorem ensures that the multiplayer system is uniformly ultimately bounded.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Mingming Liang, Yonghua Wang, Derong Liu
Summary: In this study, a novel general impulsive transition matrix is defined to reveal the transition dynamics and probability distribution evolution patterns between impulsive events. Based on this matrix, policy iteration-based impulsive adaptive dynamic programming algorithms are developed to solve optimal impulsive control problems. The algorithms demonstrate convergence to the optimal impulsive performance index function and allow for optimization on computing devices with low memory spaces.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Proceedings Paper
Automation & Control Systems
Jinquan Lin, Bo Zhao, Derong Liu
Summary: In this paper, an integral reinforcement learning (IRL)-based approximate optimal control (AOC) method is developed for unknown nonaffine systems using dynamic feedback. The optimal control policy for nonaffine systems cannot be explicitly expressed due to the unknown input gain matrix. Thus, a dynamic feedback signal is introduced to transform the nonaffine system into an augmented affine system. The AOC for unknown nonaffine systems is formulated by designing an appropriate value function for the augmented affine system, and the IRL method is adopted to derive the approximate solution of the Hamilton-Jacobi-Bellman equation.
2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS
(2023)
Article
Automation & Control Systems
Bo Zhao, Guang Shi, Derong Liu
Summary: This article investigates local control problems for nonlinear interconnected systems by using adaptive dynamic programming (ADP) with particle swarm optimization (PSO). It constructs a proper local value function and employs a local critic neural network to solve the local Hamilton-Jacobi-Bellman equation. The event-triggering mechanism is introduced to determine the sampling time instants and ensure asymptotic stability through Lyapunov stability analysis.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Hui Wang, Zhigang Liu, Zhiwei Han, Yanbo Wu, Derong Liu
Summary: Active pantograph control is a promising technique for improving train's current collection quality. Existing solutions have limitations in handling various operating conditions and lack of adaptability. In this study, a context-based deep meta-reinforcement learning algorithm is proposed to alleviate these problems. Experimental results show that the proposed algorithm can quickly adapt to new conditions and reduce contact force fluctuations.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Zhanyu Yang, Bo Zhao, Derong Liu
Summary: In this article, a novel pinning control method that requires only partial node information is developed to synchronize drive-response memristor-based neural networks with time delay. An improved mathematical model of the networks is established to accurately describe their dynamic behaviors. Unlike previous literature that requires information from all nodes, the proposed method only relies on local information to achieve synchronization of delayed networks, reducing communication and calculation burdens. Sufficient conditions for synchronization are provided, and numerical simulation and comparative experiments validate the effectiveness and superiority of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Mingduo Lin, Bo Zhao, Derong Liu
Summary: In this article, an event-triggered robust adaptive dynamic programming (ETRADP) algorithm is proposed to solve multiplayer Stackelberg-Nash games (MSNGs) for uncertain nonlinear continuous-time systems. The hierarchical decision-making process considering different roles of players is described, transforming the robust control problem into an optimal regulation problem. An online policy iteration algorithm is used to solve the derived Hamilton-Jacobi equation with an event-triggered mechanism to reduce computational and communication burdens. Critic neural networks (NNs) are constructed to obtain the event-triggered approximate optimal control policies for all players, ensuring the stability of the closed-loop system.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Mingming Liang, Derong Liu
Summary: This article focuses on designing the optimal impulsive controller (IMC) of continuous-time nonlinear systems and proposes a new adaptive dynamic programming algorithm with high generality and feasibility. The introduced policy-improving mechanism makes the algorithm more flexible for memory-limited computing devices.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Ke Wang, Chaoxu Mu, Zhen Ni, Derong Liu
Summary: This paper presents a novel composite obstacle avoidance control method that generates safe motion trajectories for autonomous systems in an adaptive manner. The method combines model-based policy iteration and state-following-based approximation in a safe reinforcement learning framework. The proposed learning-based controller achieves stable reaching of target points while maintaining a safe distance from obstacles. The effectiveness of the method is demonstrated through simulations and comparisons with other avoidance control methods.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Bo Zhao, Yongwei Zhang, Derong Liu
Summary: This article presents a cooperative motion/force control scheme for modular reconfigurable manipulators (MRMs) based on adaptive dynamic programming (ADP). The dynamic model of the entire MRM system is treated as a set of joint modules interconnected by coupling torque, and the Jacobian matrix is mapped into each joint. A neural network is used as a robust decentralized observer, and an improved local value function is constructed for each joint module. The control scheme is achieved by using force feedback compensation and is proven to be uniformly ultimately bounded through Lyapunov stability analysis.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
