Article
Computer Science, Artificial Intelligence
Xiong Yang, Yuanheng Zhu, Na Dong, Qinglai Wei
Summary: The study focuses on the decentralized event-driven control problem of nonlinear dynamical systems, transforming it into a group of nonlinear optimal control problems and solving it using event-driven Hamilton-Jacobi-Bellman equations to ensure overall system stability. The critic neural network architecture is utilized to address the problem, with Lyapunov method ensuring signal stability in closed-loop subsystems.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Mingming Ha, Ding Wang, Derong Liu
Summary: In this paper, a new approach is proposed to address the tracking control problem. By introducing a new cost function and a novel stability analysis method, the issue of incomplete elimination of tracking error in traditional approaches is solved. The specific implementation scheme for the special case of linear systems is also provided.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
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
Engineering, Industrial
Benedikt Finnah, Jochen Goensch
Summary: This paper addresses the decision problem of a power producer selling electricity from wind turbines on the continuous intraday market, considering the use of batteries and hydrogen-based storage systems. The results show that employing a backward approximate dynamic programming algorithm can provide high-quality solutions, and tests on different storage parameters reveal their impact on profit.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2021)
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
Ankush Chakrabarty, Devesh K. Jha, Gregery T. Buzzard, Yebin Wang, Kyriakos G. Vamvoudakis
Summary: A method is developed for obtaining safe initial policies for uncertain systems using ADP techniques and kernelized Lipschitz estimation. The multiplier matrices learned are used in semidefinite programming frameworks to compute admissible initial control policies with provably high probability, enabling safe initialization and constraint enforcement while ensuring exponential stability of the closed-loop system equilibrium.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Michael E. Cholette, Lei Liu, Babak Jeddi, Yateendra Mishra
Summary: The article proposes an approximate dynamic programming methodology to control battery energy storage systems for minimizing end users' electricity bills, by modeling net demand and formulating a Markov Decision Process. The approach is applied to real data and outperforms benchmark policies in reducing peak demand and overall electricity costs.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Linghuan Kong, Shuang Zhang, Xinbo Yu
Summary: An approximate optimal scheme is proposed for an uncertain n-link robot subject to saturation nonlinearity. The proposed method takes into account model uncertainty in robotic dynamics and designs an optimal control under the frame of adaptive dynamic programming. The method is proved to be effective in stabilizing the unknown system and reducing control cost.
Article
Engineering, Electrical & Electronic
Xuguang Hu, Zhaokang Zhan, Dazhong Ma, Tianbiao Wang, Hui Liu
Summary: This article proposes a data-driven signal evaluation method based on adaptive dynamic programming (ADP) to address the emergency handling of leak events in energy transportation systems. A three-layer neural network is used to establish a signal estimation model for pressure signal changes, and a flow signal evaluation method based on a value iteration scheme is developed to determine the leak flow rate. By incorporating abnormal signal analysis principles into the iterative solving process, the method is able to obtain results that closely align with actual leak event situations. Tested with different leak scenarios, the proposed method demonstrates reasonable and reliable evaluation outcomes.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Xiong Yang, Mengmeng Xu, Qinglai Wei
Summary: We study the dynamic event-driven Hop constrained control problem through approximate dynamic programming (ADP). Differing from the existing literature considering systems with either symmetric constraints or asymmetric constraints, we consider the two different constraints simultaneously. Initially, by constructing a generalized nonquadratic value function, we transform the H-8 constrained control problem into an unconstrained two-player zero-sum game. Then, we present an event-driven Hamilton-Jacobi-Isaacs equation (ED-HJIE) corresponding to the zero-sum game for lowering down the computational load. To solve the ED-HJIE, we propose a dynamic triggering mechanism together with a sole critic neural network (CNN) being built under the ADP framework. The CNN's weights are tuned via the gradient descent approach. After that, we prove uniform ultimate boundedness of the closed-loop system and the CNN's weight estimation error via Lyapunov's method. Finally, we separately use an F16 aircraft plant and an inverted pendulum system to validate the present theoretical claims.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Zhongjie Guo, Wei Wei, Mohammad Shahidehpour, Laijun Chen, Shengwei Mei
Summary: This paper studies intraday dynamic energy-reserve dispatch following a two-timescale setting, which includes coarse timescale and fine timescale. A stochastic dynamic programming method is proposed to make decisions at the coarse timescale while guaranteeing the robust feasibility of the fast process. The fast-response actions at the fine timescale are updated using a truncated rolling-horizon optimization.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Thermodynamics
Qirun Sun, Zhi Wu, Wei Gu, Tao Zhu, Lei Zhong, Ting Gao
Summary: This study proposes a flexible distribution system expansion planning model based on approximate dynamic programming, taking into account long-term load growth uncertainty and short-term power fluctuations, and developing a flexible investment strategy using Markov decision process. Case studies show the feasibility and benefits of the proposed planning approach in significantly reducing investment risks and configuring renewable energy equipment more reasonably.
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
Automation & Control Systems
Derong Liu, Shan Xue, Bo Zhao, Biao Luo, Qinglai Wei
Summary: This article reviews the recent development of adaptive dynamic programming (ADP) with applications in control, highlighting efficient algorithms and future research directions. ADP is applied in optimization, game theory, and large-scale systems, showing great potential in the era of artificial intelligence.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Jun Yuan, Si-Zhe Chen, Samson S. Yu, Guidong Zhang, Zhe Chen, Yun Zhang
Summary: Modern home energy management systems (HEMSs) face challenges due to system complexity, uncertain load consumptions, and renewable energy generation. To address these issues, we propose an HEMS that integrates a kernel-based real-time adaptive dynamic programming (K-RT-ADP) with a new preprocessing short-term prediction technique. The HEMS uses the GRU-BERT model to predict load consumption and solar generation, and the K-RT-ADP algorithm for real-time control.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
