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3rd IFAC Conference on Modelling, Identification and Control of Nonlinear Systems (MiCNON)

作者： Liu, Tong Cui, Leilei Pang, Bo Jiang, Zhong-Ping NYU Brooklyn NY 11201 USA

The emergence of connected and autonomous vehicles (CAVs) has increased opportunities to mitigate the traffic congestion, improve safety and reduce accidents. In this paper, we consider the mixed traffic case with multiple heterogeneous human-driven vehicles and multiple CAVs on freeways. Under mild conditions, the stabilizability of the overall system is proved. With the tracking errors of relative distance and velocity as the states, we design an input-to-state stabilizing controller that solves a linear quadratic regulator problem by means of reinforcement learning and adaptive dynamic programming techniques. The priori knowledge of the vehicle network model is not needed. For a string of connected human-driven and automated vehicles, we give the sufficient conditions to guarantee the general string stability. The proposed learning-based control methodology is validated by means of simulation results. Copyright (C) 2021 The Authors.

关键词： Connected and Autonomous Vehicles Stabilizability String Stability adaptive dynamic programming

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Model-Free Off-Policy Iterative adaptive dynamic programming for Nitrate-Nitrogen Concentration Control 33

Model-Free Off-Policy Iterative Adaptive Dynamic Programming...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Yang, Ruyue Wang, Ding Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China

ISBN: (纸本)9781665440899

To solve the optimal control problem of the nitrate-nitrogen concentration in the wastewater treatment plant (WWTP), a model-free off-policy iterative adaptive dynamic programming algorithm using online data is proposed. Under the actor-critic structure, the developed algorithm approximates the optimal Q-function by minimizing the temporal difference and improves the control law through the policy gradient method. Neural networks are utilized in the proposed scheme. Besides, the experience replay buffer is involved in the off-policy iteration of neural networks. Finally, simulation examples for a nonlinear system and WWTP are presented to verify the effectiveness of the proposed method.

关键词： adaptive dynamic programming Model-Free Off-Policy Learning Wastewater Treatment Plant

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Regulating Control of In-Pipe Intelligent Isolation Plugging Tool Based on adaptive dynamic programming

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JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE 2022年第2期13卷

作者： Miao, Xingyuan Zhao, Hong China Univ Petr Coll Mech & Transportat Engn Beijing 102249 Peoples R China

In-pipe intelligent isolation plugging tools (IPTs) are crucial in pipeline maintenance. During the plugging process, the flow field around the IPT changes drastically, resulting in vibration and instability of the plugging process. Therefore, three foldable spoilers were designed at the tail of the IPT to reduce the vibration of the IPT. First, a disturbing flow experiment of IPT with spoilers was designed. A mathematical model of the pneumatic spoiler control system was established to regulate the spoiler angles. Second, based on the experimental data, a bidirectional long short-term memory (Bi-LSTM) neural network predictor between the plugging states, the spoiler angles, and the pressure gradient was established. Then, an adaptive dynamic programming controller was designed to select the optimal control action for each plugging state, thereby reducing the pressure gradient. Finally, Python and MATLAB/Simulink were used for simulation. The results showed that prediction errors were controlled within 9%, and the controller could reduce the pressure gradient during the plugging process by an average of 25.94%, which alleviated the vibration of the IPT and achieved a smooth plugging operation.

关键词： Intelligent isolation plugging tool (IPT) Vibration reduction Spoiler Bi-long short-term memory (Bi-LSTM) adaptive dynamic programming Optimal control

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An adaptive dynamic programming Algorithm Based on ITF-OELM for Discrete-Time Systems 33

An Adaptive Dynamic Programming Algorithm Based on ITF-OELM ...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Zhang, Xiaofei Ma, Hongbin Chen, Junyong Li, Weixue Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China

ISBN: (纸本)9781665440899

adaptive dynamic programming (ADP) is a kind of intelligent control method, and it is a non-model-based method that can directly approximate the optimal control policy via online learning. The gradient algorithm is usually used to update weights of action networks and critic networks, however it is clear that gradient descent-based learning methods are generally very slow due to improper learning steps or may easily converge to local minimum. In this paper, in order to overcome those disadvantages of gradient descent-based learning methods, a novel ADP algorithm based on initial-training-free online extreme learning machine (ITF-OELM), in which the critic network link weights of hidden nodes to output nodes can be obtained by least squares instead of gradient algorithm, is introduced. Finally, the ADP algorithm based on ITF-OELM is tested on a discrete time torsional pendulum system, and simulation results indicate that this algorithm makes the system converge in a shorter time compared with the ADP based on gradient algorithm.

关键词： adaptive dynamic programming Extreme Learning Machine Discrete-time Systems

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Improved Proxy-based Sliding Mode Control Integrated adaptive dynamic programming For Pneumatic Muscle Actuators 6

Improved Proxy-based Sliding Mode Control Integrated Adaptiv...

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6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Peng, Zengqi Huang, Jian Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9780738133645

It is an urgent social problem that the rehabilitation needs of the elderly and the disabled can hardly be satisfied. The human-centered rehabilitation robot is a potential solution. In this paper, an auxiliary control scheme based on adaptive dynamic programming is proposed for a class of pneumatic artificial muscles. The improved proxy-based sliding mode controller is used as the basic controller, and an adaptive dynamic programming controller is used to provide auxiliary control signals. To ensure the reliability of the control strategy, the Lyapunov theorem is used to analyze the stability of the control scheme. The simulation results of the proposed method are compared with those of PID, proxy-based sliding mode control, and adaptive proxy-based sliding mode control without the auxiliary controller. Numerical results verify the effectiveness of the control scheme.

关键词： adaptive dynamic programming proxy-based sliding mode control pneumatic muscle actuator

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Model-Free Composite Control for a Class of Semi-Coupled Two-Time-Scale Systems: An adaptive dynamic programming Approach 3rd

Model-Free Composite Control for a Class of Semi-Coupled Two...

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3rd IFAC Conference on Modelling, Identification and Control of Nonlinear Systems (MiCNON)

作者： Zhao, Jianguo Yang, Chunyu Zhou, Linna Gao, Weinan China Univ Min & Technol Minist Educ Engn Res Ctr Intelligent Control Underground Spac Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Florida Inst Technol Dept Mech & Civil Engn Melbourne FL 32901 USA

This paper proposes a composite control approach for a class of semi-coupled two-time-scale systems, whose model parameters are completely unknown. In the framework of adaptive dynamic programming (ADP), a novel model-free off-policy learning algorithm without ill-conditioned numerical issues is derived on the basis of input and state data. The convergence of the developed ADP algorithm is guaranteed under reasonable assumptions. Using singular perturbation theory, we prove that the closed-loop system with the obtained controller in each iteration is asymptotically stable. Simulation results on an industrial process demonstrate the feasibility and effectiveness of our proposed approach. Copyright (C) 2021 The Authors.

关键词： Singular perturbation theory model-free control adaptive dynamic programming two-time-scale systems optimal control

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An echo state network based adaptive dynamic programming approach for time-varying parameters optimization with application in algal bloom prediction

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APPLIED SOFT COMPUTING 2022年 122卷

作者： Zhang, Huiyan Hu, Bo Wang, Xiaoyi Wang, Li Xu, Jiping Sun, Qian Zhao, Zhiyao Beijing Technol & Business Univ Sch Artificial Intelligence Beijing 100048 Peoples R China Beijing Inst Fash Technol Beijing 100029 Peoples R China State Grid Beijing Shijingshan Elect Power Supply Beijing 100043 Peoples R China Beijing Technol & Business Univ China Light Ind Key Lab Ind Internet & Big Data Beijing 100048 Peoples R China

The prediction of algal bloom is one of the important links in eutrophication prevention. Chlorophyll a concentration is the indicating variable of algal bloom, and its time series is non-stationary and nonlinear, which brings challenges to its effective prediction. Although the current algae growth model (AGM) can directly describe the algal bloom dynamics, the fixed parameters limit the adaptability of the model. If the fixed parameters are dynamically adjusted, the trend of chlorophyll a concentration can be better captured. Therefore, the adaptive dynamic programming (ADP) approach is used to optimize the parameters of the AGM. The ADP contains an action network and a critic network by echo state network, where the action network is used to output the increment value of the fixed parameters, and the critic network is used to approximate the performance index function. In this paper, the input of the action network uses the time series features extracted by the relevant variables, so that the time-varying parameters of the AGM have better dynamic characteristics. We verify the effectiveness of the proposed model through the dataset of the North Canal and Taihu Lake, and the convergence analysis proves the theoretical reliability. In this way, the improved mechanism model with timevarying parameters not only maintains the better interpretability of the original AGM, but also further enhances the prediction accuracy and adaptability by extracting inherent interactive features from the relevant variables.

关键词： Algal bloom prediction Mechanism model Time-varying parameters adaptive dynamic programming Echo state network

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A multi-timescale smart grid energy management system based on adaptive dynamic programming and Multi-NN Fusion prediction method

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KNOWLEDGE-BASED SYSTEMS 2022年 241卷 108284-108284页

作者： Yuan, Jun Zhang, Guidong Chen, Zhe Li, Zhong Zhang, Yun Yu, Samson S. Guangdong Univ Technol Sch Automation Guangzhou Peoples R China Deakin Univ Sch Engn Melbourne Australia Aalborg Univ Dept Energy Technol Aalborg Denmark Minnan Normal Univ Sch Math & Stat Zhangzhou Peoples R China

The complexity of power grids, the intermittent renewable energy generation and the uncertainty of load consumption bring great challenges to modern energy management systems (EMSs). To solve the energy optimization problem in the time-varying smart grid, this paper proposes a multi-timescale EMS based on the adaptive dynamic programming (ADP) algorithm and multi-neural-network fusion (MNNF) prediction technology. In detail, according to different power consumption characteristics, this paper uses fuzzy C-means (FCM) clustering algorithm to classify power users into industrial users, commercial users and residential users. Based on the classification results, an MNNF prediction method is proposed that can integrate different influencing factors to predict load consumption and renewable energy generation. Then a multi-timescale ADP optimization algorithm is proposed to maximize the utilization of renewable energy on daily, intra-day and real-time (i.e., three timescales) of energy behavior. The convergence of the multi-timescale ADP algorithm is proved mathematically when the initial value is a random semi-positive definite function. Finally, the proposed ADP with MNNF energy management system is verified on a hardware-in-the-loop (HIL) platform. (c) 2022 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Energy management system Multi-neural network fusion prediction algorithm Hardware-in-loop

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State of the Art of adaptive dynamic programming and Reinforcement Learning

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CAAI Artificial Intelligence Research 2022年第2期1卷 93-110页

作者： Derong Liu Mingming Ha Shan Xue Department of Mechanical and Energy Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Electrical and Computer Engineering University of Illinois at ChicagoIL 606071USA School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China

This article introduces the state-of-the-art development of adaptive dynamic programming and reinforcement learning(ADPRL).First,algorithms in reinforcement learning(RL)are introduced and their roots in dynamic programming are *** dynamic programming(ADP)is then introduced following a brief discussion of dynamic *** in ADP and RL have enjoyed the fast developments of the past decade from algorithms,to convergence and optimality analyses,and to stability *** key steps in the recent theoretical developments of ADPRL are mentioned with some future *** particular,convergence and optimality results of value iteration and policy iteration are reviewed,followed by an introduction to the most recent results on stability analysis of value iteration algorithms.

关键词： adaptive dynamic programming approximate dynamic programming adaptive critic designs neuro-dynamic programming neural dynamic programming reinforcement learning intelligent control learning control optimal control

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adaptive dynamic programming without Persistence Excitation for Tracking in A Special Class of Linear Systems with Input Constraints

Adaptive Dynamic Programming without Persistence Excitation ...

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第33届中国控制与决策会议

作者： Chunbin Qin Jinguang Wang Heyang Zhu Jishi Zhang Dehua Zhang School of Artificial Intelligence Henan University School of Software Henan University

In this paper,an approximate optimal control method with input constraints based on adaptive dynamic programming(ADP) is proposed for a class of special linear *** method is based on an actor/critic *** critic approximator is used to approximate the optimal cost function,and the actor approximator is used to approximate the bounded control *** view of the fact that the algorithm requires a persistence of excitation(PE) condition,we use the previous data and the current data to alleviate this *** Lyapunov method is used to prove stability,the error between the optimal control and the bounded control is *** is prove that the closed-loop system can be guaranteed to be uniformly ultimately bounded(UUB).On this basis,a robustness term is added to compensate the effect of the approximation error.A simulation example shows the effectiveness.

关键词： adaptive dynamic programming Bounded input control Persistence of excitation Reinforcement learning

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