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Intelligent sliding mode fault-tolerant control for aircraft engines with actuator dynamics and faults based on adaptive dynamic programming

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AERONAUTICAL JOURNAL 2024年第1333期129卷 651-670页

作者： Xiao, L. F. Tan, Y. S. Du, Y. B. Zhang, X. L. Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Automat Engn Nanjing Peoples R China

The fault-tolerant control issue of aircraft engines with actuator dynamics and faults is investigated in this paper. By proposing a novel intelligent sliding mode fault-tolerant control (ISMFTC) method, which combines an adaptive dynamic programming (ADP) sliding surface with Grey Wolf Optimizer (GWO) for controller parameter optimisation, the goal is to achieve quality steady-state and dynamic performance in aircraft engines while maintaining strong fault-tolerance properties. Firstly, by considering not only actuator dynamics but also actuator faults, an uncertain nonlinear cascaded model of aircraft engines is developed according to characteristic of aircraft engines and their actuators. Secondly, an ADP-based sliding surface is proposed for considered aircraft engine uncertain nonlinear cascaded system. It can obtain a certain sense of optimised performance, and could be solved by ADP strategy off-line as well. Thirdly, fault-tolerant controller is obtained on the basis of sliding mode theory and adaptive fault estimation law, namely, ADP-based ISMFTC controller. Meanwhile, GWO is integrated into the investigation of ADP-based ISMFTC controller, optimised designable control parameters are obtained subsequently. Besides, robustness analysis is elaborated according to Lyapunov theory, fault estimation error is bounded and states of closed-loop system are uniformly ultimately bounded. Simulation on a twin-shaft turbofan aircraft engine, indicates the effect of proposed ADP-based ISMFTC method.

关键词： sliding mode fault-tolerant control adaptive dynamic programming aircraft engine actuator dynamics actuator faults Grey Wolf Optimizer

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Reactive power control of grid-connected wind farm based on adaptive dynamic programming

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NEUROCOMPUTING 2014年 125卷 125-133页

作者： Tang, Yufei He, Haibo Ni, Zhen Wen, Jinyu Sui, Xianchao Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Huazhong Univ Sci & Technol Coll Elect & Elect Engn Wuhan 430074 Peoples R China

Optimal control of large-scale wind farm has become a critical issue for the development of renewable energy systems and their integration into the power grid to provide reliable, secure, and efficient electricity. Among many enabling technologies, the latest research results from both the power and energy community and computational intelligence (Cl) community have demonstrated that Cl research could provide key technical innovations into this challenging problem. In this paper, a neural network based controller is presented for the reactive power control of wind farm with doubly fed induction generators (DFIG). Specifically, we investigate the on-line learning and control approach based on adaptive dynamic programming (ADP) for wind farm control and integration with the grid. This controller can effectively dampen the oscillation of the wind farm system after the ground fault of the grid. Compared to previous control strategies, this controller is on-line and "model free", and therefore, can reduce the control complexity. Simulation studies are carried out in Matlab/Simulink and the results demonstrated the effectiveness of the ADP controller. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Computational intelligence adaptive dynamic programming Doubly fed induction generator Wind farm Power system adaptive control

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Modeling city logistics using adaptive dynamic programming based multi-agent simulation

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2019年 125卷 74-96页

作者： Firdausiyah, N. Taniguchi, E. Qureshi, A. G. Brawijaya Univ Malang 65145 Indonesia Kyoto Univ Nishikyo Ku Kyoto 6158540 Japan

The effects of city logistics solutions are uncertain due to fluctuating demand, parking issues and multiple agents within the system. This research modelled the behavior of freight carriers and an Urban Consolidation Center (UCC) operator using Multi-Agent Simulation-adaptive dynamic programming based Reinforcement Learning (MAS-ADP based RL) to evaluate a Joint Delivery Systems in an uncertain environment. The MAS-ADP based RL is superior to MAS-Q-learning in replicating the potential actions of the agents under uncertain environment by adapting to the changing environment properly into accurate decisions thus increasing the accuracy of agent's decision making and eventually reducing environmental emissions as well.

关键词： adaptive dynamic programming Multi-agent simulation Reinforcement learning City logistics Urban consolidation center

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Design and HIL Realization of an Online adaptive dynamic programming Approach for Real-Time Economic Operations of Household Energy Systems

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IEEE TRANSACTIONS ON SMART GRID 2022年第1期13卷 330-341页

作者： Yuan, Jun Yu, Samson S. Zhang, Guidong Lim, Chee Peng Hieu Trinh Zhang, Yun Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Deakin Univ Sch Engn Geelong Vic 3216 Australia Deakin Univ Inst Intelligent Syst Res & Innovat Waurn Ponds Vic 3216 Australia

Stochastic, nonlinear and time-varying factors bring great challenges to accurate modeling and design of real-time (RT) energy management systems (EMSs) for modern energy systems with intermittent renewable energy sources. In this paper, we propose a novel model-free RT-EMS based on the predictive adaptive dynamic programming (ADPredictive) algorithm for energy scheduling problems in a home EMS environment. The proposed RT-ADPreditive EMS can minimize the total cost of electricity, reduce battery life loss, and maximize the utilization of renewable energy generation. In the proposed RT-ADPredictive EMS, the gated recurrent unit (GRU) neural network (NN) is employed to perform online prediction of renewable energy generation and load consumption of the household based on RT data. The RT-ADPreditive algorithm can approximate the performance index function and the optimal control law based on the RT data and predicted data for the next time step. Convergence of the proposed method is mathematically proven, and a hardware-in-the-loop (HIL) experimental platform comprising dSPACE and RT-lab is built to verify the effectiveness of the proposed RT-ADPreditive home EMS.

关键词： Batteries Energy management Artificial neural networks Prediction algorithms Mathematical model Optimization Renewable energy sources adaptive dynamic programming energy management system energy economics gated recurrent unit neural network hardware-in-the-loop online soft computing

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Antomated web navigation using multiagent adaptive dynamic programming

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2003年第3期33卷 412-417页

作者： Varghese, J Mukhopadhyay, S Indiana Univ Purdue IN 46202 USA

Today a massive amount of information available on the WWW often makes searching for information of interest a long and tedious task. Chasing hyperlinks to rind relevant information may be daunting. To overcome such a problem, a learning system, cognizant of a user's interests, can be employed to automatically search for and retrieve relevant information by following appropriate hyperlinks. In this paper, we describe the design of such a learning system for automated Web navigation using adaptive dynamic programming methods. To improve the performance of the learning system, we introduce the notion of multiple model-based learning agents operating in parallel, and describe methods for combining their models. Experimental results on the WWW navigation problem are presented to indicate that combining multiple learning agents, relying on user feedback, is a promising direction to improve learning speed in automated WWW navigation.

关键词： adaptive dynamic programming multi-agent learning relevance feedback vector-space model Web navigation

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Distributed Fault Tolerant Consensus Control of Nonlinear Multiagent Systems via adaptive dynamic programming

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第7期35卷 9041-9053页

作者： Zhang, Yongwei Zhao, Bo Liu, Derong Zhang, Shunchao Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen 518055 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Guangdong Univ Finance Sch Internet Finance & Informat Engn Guangzhou 510521 Peoples R China

This article develops a distributed fault-tolerant consensus control (DFTCC) approach for multiagent systems by using adaptive dynamic programming. By establishing a local fault observer, the potential actuator faults of each agent are estimated. Subsequently, the DFTCC problem is transformed into an optimal consensus control problem by designing a novel local value function for each agent which contains the estimated fault, the consensus errors, and the control laws of the local agent and its neighbors. In order to solve the coupled Hamilton-Jacobi-Bellman equation of each agent, a critic-only structure is established to obtain the approximate local optimal consensus control law of each agent. Moreover, by using Lyapunov's direct method, it is proven that the approximate local optimal consensus control law guarantees the uniform ultimate boundedness of the consensus error of all agents, which means that all following agents with potential actuator faults synchronize to the leader. Finally, two simulation examples are provided to validate the effectiveness of the present DFTCC scheme.

关键词： Actuators Consensus control Fault tolerant systems Fault tolerance Multi-agent systems Mathematical models dynamic programming adaptive dynamic programming fault-tolerant control multiagent systems optimal consensus control

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Model-free optimal tracking over finite horizon using adaptive dynamic programming

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OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第6期44卷 3114-3138页

作者： Jha, Mayank Shekhar Theilliol, Didier Weber, Philippe Univ Lorraine CNRS Ctr Rech Automat Nancy CRAN UMR 7039Fac Sci & Technol Vandoeuvre Les Nancy France Univ Lorraine Fac Sci & Technol Ctr Rech Automat Nancy CRAN UMR 7039CNRS BP 70239 F-54506 Vandoeuvre Les Nancy France

adaptive dynamic programming (ADP) based approaches are effective for solving nonlinear Hamilton-Jacobi-Bellman (HJB) in an approximative sense. This paper develops a novel ADP-based approach, in that the focus is on minimizing the consecutive changes in control inputs over a finite horizon to solve the optimal tracking problem for completely unknown discrete time systems. To that end, the cost function considers within its arguments: tracking performance, energy consumption and as a novelty, consecutive changes in the control inputs. Through suitable system transformation, the optimal tracking problem is transformed to a regulation problem with respect to state tracking error. The latter leads to a novel performance index function over finite horizon and corresponding nonlinear HJB equation that is solved in an approximative iterative sense using a novel iterative ADP-based algorithm. A suitable Neural network-based structure is proposed to learn the initial admissible one step zero control law. The proposed iterative ADP is implemented using heuristic dynamic programming technique based on actor-critic Neural Network structure. Finally, simulation studies are presented to illustrate the effectiveness of the proposed algorithm.

关键词： actor critic adaptive dynamic programming model free neural networks nonlinear Hamilton Jacobi bellman optimal tracking

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Data-driven approximate optimal tracking control schemes for unknown non-affine non-linear multi-player systems via adaptive dynamic programming

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ELECTRONICS LETTERS 2017年第7期53卷 465-467页

作者： Jiang, He Luo, Yanhong Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

Game theory, optimal control theory and adaptive dynamic programming (ADP) to deal with the optimal tracking control issue for unknown non-affine multi-player systems are integrated. It is known that non-zero-sum games of non-linear multi-player systems rely on solving a set of Hamilton-Jacobi equations, which are generally difficult to be computed analytically due to the non-linear nature. Traditional ADP methods require the knowledge of accurate system models, and only consider the simple affine system version. A novel data-driven Q-learning approach, which only needs the measurable data generated by running systems, is proposed. To implement this method, neural networks (NNs) are utilised, and NN weights are updated through the least-square form.

关键词： optimal control nonlinear systems game theory dynamic programming learning (artificial intelligence) neural nets least squares approximations data-driven approximate optimal tracking control nonaffine nonlinear multiplayer systems adaptive dynamic programming game theory optimal control theory ADP nonzero-sum games Hamilton-Jacobi equations affine system data-driven Q-learning approach neural networks NN weights least-square form

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Research on Microgrid Superconductivity-Battery Energy Storage Control Strategy Based on adaptive dynamic programming

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2024年第8期34卷 1页

作者： Liu, Yang Li, Pengtao Xing, Zuoxia Han, Xingfan Fu, Qitong Jiang, Zhanpeng Shenyang Univ Technol Sch Elect Engn Shenyang 110870 Peoples R China

Aiming at the influence of the fluctuation rate of wind power output on the stable operation of microgrid, a hybrid energy storage system (HESS) based on superconducting magnetic energy storage (SMES) and battery energy storage is constructed, and a hybrid energy storage control strategy based on adaptive dynamic programming (ADP) is designed. The stability of microgrid operation and the service life of the HESS, as well as the economy of microgrid operation, can be improved by optimizing the capacity and output profile of the HESS. Taking the power of a typical wind farm as an example, the capacity configuration of the HESS is carried out, and the control effects of different control strategies on the HESS are compared to verify the effectiveness and feasibility of the ADP-based control strategy for the HESS.

关键词： Microgrids Energy storage Batteries Wind power generation Fluctuations Power system stability Hybrid power systems adaptive dynamic programming capacity allocation frequency control hybrid energy storage system

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Hamiltonian-driven adaptive dynamic programming for mixed H₂/H_∞ performance using sum-of-squares

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2021年第6期31卷 1941-1963页

作者： Yang, Yongliang Mazouchi, Majid Modares, Hamidreza Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing Peoples R China Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA

In this article, the mixed H-2/H-infinity performance optimization is first formulated as a nonzero-sum game, of which the sufficient condition guaranteeing the existence of the Nash equilibrium is derived using the Hamilton-Jacobi (HJ) theory. Then, Hamiltonian-driven inequalities are presented to evaluate the H-2 and H-infinity performances. Using this Hamiltonian-inequality driven approach, the coupled HJ equations arising from finding the Nash equilibrium are relaxed to the HJ inequality constraints. A novel mixed policy iteration (PI) algorithm is developed that uses sum-of-squares (SOS) program in policy evaluation step, and consists of an H-2 performance improvement step and an H-infinity performance guarantee step. This constrained-driven approach allows us to present a PI algorithm that takes into account both robustness and performance objectives. Finally, a numerical simulation is carried out to highlight the efficacy of the proposed framework.

关键词： adaptive dynamic programming Hamiltonian-driven framework mix H-2/H-infinity performance nonzero-sum game sum-of-squares programming

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