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γ-Iterative dual heuristic dynamic programming for Nonlinear Critical Surfaces With Strong Constraints

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第6期54卷 3334-3343页

作者： Zhang, Huaguang Yan, Ying Sun, Jiayue Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110189 Liaoning Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110189 Liaoning Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110189 Liaoning Peoples R China

In this article, the critical value problem of a class of model-free nonlinear surfaces with strong constraints is studied, and a gamma-iterative dual heuristic dynamic programming algorith mis proposed. Considering the high coupling of nonlinear surface and the algorithm structure of traditional dual heuristic dynamic programming, this article directly uses neural network to recon-struct the gradient relationship between adjacent strips of the Janbu segmentation method, so as to reduce the super dimensional calculation of partial derivatives still needed by first fitting the model. The critical value of the strongly constrained nonlinear surface is transformed into the optimal control law for solving the input-constrained nonquadratic HJB equation with discountfactor gamma and the cofunction is defined to avoid the need to calculate the integral term in the repeated iterative process. Finally, the simulation results show that the gamma-iterative dual heuristic dynamic programming algorithm is effective in solving the critical sliding surface of a kind of rock and soil mass

关键词： Adaptive dynamic programming (ADP) critical slip surface (CSS) dual heuristic dynamic programming nonlinear critical surfaces nonquadratic

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Nonlinear Flight Attitude Control Using Error dynamics Based dual heuristic dynamic programming 35

Nonlinear Flight Attitude Control Using Error Dynamics Based...

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35th Chinese Control and Decision Conference (CCDC)

作者： Huang, Xu Liu, Jiarun Zhong, Honghao Wang, Zhaolei Luo, Wuyi Beijing Aerosp Automat Control Inst Natl Key Lab Sci & Technol Aerosp Intelligence Co Beijing Peoples R China

ISBN: (纸本)9798350334722

This paper presents an improved approach called error dynamics based dual heuristic dynamic programming (ED-DHP) for nonlinear flight attitude control. The augmented system state containing tracking error and reference trajectory are defined for the DHP to solve the tracking problem, without requiring the complete knowledge of the vehicle dynamics and the reference trajectory dynamics. A recursive least square method is used to identify not only the input dynamics, but also the total uncertainty of the system based on the error dynamics in real time. The simulations of two different air vehicles demonstrate the good performance of ED-DHP for the online self-learning flight attitude control.

关键词： error dynamics dual heuristic dynamic programming self-learning flight control

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The dual heuristic dynamic programming Learning Control of Soft Measurement Model in Wiped Film Evaporation Process

The Dual Heuristic Dynamic Programming Learning Control of S...

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Chinese Automation Congress (CAC)

作者： Li, Hui Chang, Huapeng Jiang, Tianyi Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Jilin Peoples R China Jilin Univ Coll Commun Engn Changchun 130012 Jilin Peoples R China

ISBN: (纸本)9781728113128

The wiped film molecular distillation system has the characteristics of multivariable, strong coupling, nonlinearity and large hysteresis. Current modeling and control methods are difficult to achieve the desired results. In order to stabilize production, the online sequential extreme learning machine soft measurement model driven by data is constructed by analyzing the auxiliary parameter variables. The dual heuristic dynamic programming learning control was used to optimize for the molecular distillation system. Comparing with traditional back propagation neural network modeling. The simulation experiments are performed to verify the effectiveness and advantage of dual heuristic dynamic programming learning control with online sequential extreme learning machine soft measurement model.

关键词： wiped film molecular distillation online sequential extreme learning machine soft measurement model dual heuristic dynamic programming

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Research on Water Level Optimal Control of Boiler Drum Based on dual heuristic dynamic programming

Research on Water Level Optimal Control of Boiler Drum Based...

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8th International Symposium on Neural Networks

作者： Huang, Qingbao Song, Shaojian Lin, Xiaofeng Peng, Kui Guangxi Univ Coll Elect Engn Nanning 530004 Peoples R China

ISBN: (纸本)9783642211041

Boiler drum system is an important component of a thermal power plant or industrial production, and the water level is a critical parameter of boiler drum control system. Because of non-linear, strong coupling and large disturbance, it is difficult to reach a suitable working state of drum system by using traditional control methods. It is necessary to explore new methods to realize optimal control of drum water level. The back propagation (BP) neural network model of boiler drum system is built in this paper firstly, then the optimal control of the drum system by the dual heuristic dynamic programming (DHP) algorithm is realized, and compared with the heuristic dynamic programming (HDP) algorithm at last. The result shows that the DHP optimization algorithm has good performance in control precision and rejecting process disturbances.

关键词： boiler drum level BP neural network dual heuristic dynamic programming optimal control

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Online self-learning attitude tracking control of morphing unmanned aerial vehicle based on dual heuristic dynamic programming

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AEROSPACE SCIENCE AND TECHNOLOGY 2023年 143卷

作者： Huang, Xu Liu, Jiarun Jia, Chenhui Wang, Zhaolei Li, Wenting Beijing Aerosp Automat Control Inst Beijing 100854 Peoples R China Natl Key Lab Sci & Technol Aerosp Intelligence Con Beijing 100854 Peoples R China

This article presents a partially model-free dual heuristic dynamic programming (DHP) control approach to address the online discrete-time attitude tracking problem. The DHP method learns the control policy by solving the translated optimal regulation problem of the augmented system, which is constructed with the tracking error dynamics and the desired trajectory. In the context of the augmented system, a corresponding recursive least squares (RLS) identification strategy has been devised, and the DHP update rules have been derived. The identified augmented system model not only provides dynamic information and predicted states for the actor and critic updates but also incorporates an uncertainty compensation part to reduce the learning complexity and enhance robustness of the controller. It is noteworthy that concurrent learning (CL) is employed to improve the critic convergence by utilizing an identified model-based data generation method. This approach eliminates the need for an offline learning stage and enables flexible controller design based on the available system knowledge. Based on the proposed approach, different attitude control frameworks have been designed for a morphing unmanned aerial vehicle (UAV). In addition to numerical simulations, flight tests have been conducted to further demonstrate the capabilities of the proposed approach, highlighting its effectiveness and advantages.

关键词： dual heuristic dynamic programming Attitude tracking control Concurrent learning Unmanned aerial vehicle Flight test

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Transformation Invariant On-Line Target Recognition

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2011年第6期22卷 906-918页

作者： Iftekharuddin, Khan M. Univ Memphis Dept Elect & Comp Engn Intelligent Syst & Image Proc Lab Memphis TN 38152 USA

Transformation invariant automatic target recognition (ATR) has been an active research area due to its widespread applications in defense, robotics, medical imaging and geographic scene analysis. The primary goal for this paper is to obtain an on-line ATR system for targets in presence of image transformations, such as rotation, translation, scale and occlusion as well as resolution changes. We investigate biologically inspired adaptive critic design (ACD) neural network (NN) models for on-line learning of such transformations. We further exploit reinforcement learning (RL) in ACD framework to obtain transformation invariant ATR. We exploit two ACD designs, such as heuristic dynamic programming (HDP) and dual heuristic dynamic programming (DHP) to obtain transformation invariant ATR. We obtain extensive statistical evaluations of proposed on-line ATR networks using both simulated image transformations and real benchmark facial image database, UMIST, with pose variations. Our simulations show promising results for learning transformations in simulated images and authenticating out-of plane rotated face images. Comparing the two on-line ATR designs, HDP outperforms DHP in learning capability and robustness and is more tolerant to noise. The computational time involved in HDP is also less than that of DHP. On the other hand, DHP achieves a 100% success rate more frequently than HDP for individual targets, and the residual critic error in DHP is generally smaller than that of HDP. Mathematical analyses of both our RL-based on-line ATR designs are also obtained to provide a sufficient condition for asymptotic convergence in a statistical average sense.

关键词： Active on-line learning automatic target recognition dual heuristic dynamic programming face authentication heuristic dynamic programming image transformation invariance reinforcement learning

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Adaptive dynamic programming for discrete-time linear quadratic regulation based on multirate generalised policy iteration

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INTERNATIONAL JOURNAL OF CONTROL 2018年第6期91卷 1223-1240页

作者： Chun, Tae Yoon Lee, Jae Young Park, Jin Bae Choi, Yoon Ho Yonsei Univ Sch Elect & Elect Engn Seoul South Korea Univ Alberta Dept Comp Sci Edmonton AB Canada Kyonggi Univ Dept Elect Engn Suwon South Korea

In this paper, we propose two multirate generalised policy iteration (GPI) algorithms applied to discrete-time linear quadratic regulation problems. The proposed algorithms are extensions of the existing GPI algorithm that consists of the approximate policy evaluation and policy improvement steps. The two proposed schemes, named heuristic dynamic programming (HDP) and dual HDP (DHP), based on multirate GPI, use multi-step estimation (M-step Bellman equation) at the approximate policy evaluation step for estimating the value function and its gradient called costate, respectively. Then, we show that these two methods with the same update horizon can be considered equivalent in the iteration domain. Furthermore, monotonically increasing and decreasing convergences, so called value iteration (VI)-mode and policy iteration (PI)-mode convergences, are proved to hold for the proposed multirate GPIs. Further, general convergence properties in terms of eigenvalues are also studied. The data-driven online implementation methods for the proposed HDP and DHP are demonstrated and finally, we present the results of numerical simulations performed to verify the effectiveness of the proposed methods.

关键词： Multirate generalised policy iteration heuristic dynamic programming dual heuristic dynamic programming adaptive dynamic programming mixed-mode convergence linear quadratic regulation

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Novel optimal trajectory tracking for nonlinear affine systems with an advanced critic learning structure

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NEURAL NETWORKS 2022年第0期154卷 131-140页

作者： Wang, Ding Zhao, Huiling Zhao, Mingming Ren, Jin 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 Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

In this paper, a critic learning structure based on the novel utility function is developed to solve the optimal tracking control problem with the discount factor of affine nonlinear systems. The utility function is defined as the quadratic form of the error at the next moment, which can not only avoid solving the stable control input, but also effectively eliminate the tracking error. Next, the theoretical derivation of the method under value iteration is given in detail with convergence and stability analysis. Then, the dual heuristic dynamic programming (DHP) algorithm via a single neural network is introduced to reduce the amount of computation. The polynomial is used to approximate the costate function during the DHP implementation. The weighted residual method is used to update the weight matrix. During simulation, the convergence speed of the given strategy is compared with the heuristic dynamic programming (HDP) algorithm. The experiment results display that the convergence speed of the proposed method is faster than the HDP algorithm. Besides, the proposed method is compared with the traditional tracking control approach to verify its tracking performance. The experiment results show that the proposed method can avoid solving the stable control input, and the tracking error is closer to zero than the traditional strategy. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Discount factor dual heuristic dynamic programming Neural networks Optimal tracking control Polynomial Value iteration

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Event-triggered constrained control with DHP implementation for nonaffine discrete-time systems

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INFORMATION SCIENCES 2020年第0期519卷 110-123页

作者： Ha, Mingming Wang, Ding Liu, Derong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

This paper proposes an event-based near-optimal control algorithm for nonaffine discrete-time systems with constrained inputs. The method is derived from the dual heuristic dynamic programming (DHP) technique. The challenge caused by saturating actuators is overcome by using a nonquadratic performance index. Then, the event-based control technique is used to decrease the amount of computation. Meanwhile, the stability analysis is provided. It illustrates that the proposed event-based method can asymptotically stabilize the nonaffine systems by using the Lyapunov method. Furthermore, the stability conditions and the design process of the event-based controller are established. The event-based DHP algorithm is implemented by constructing three neural networks, namely, the model network, the critic network, and the action network. Finally, simulation studies are conducted to demonstrate the applicability and the performance of the proposed method. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Adaptive dynamic programming dual heuristic dynamic programming Event-based technique Nonaffine systems Saturating actuators Stability analysis

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A Nonlinear Critical Surface With Input Constraints on Costate-Adaptive dynamic programming

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第11期70卷 4088-4092页

作者： Zhao, Guijie Sun, Jiayue Yan, Ying Zhang, Huaguang Northeastern Univ Sch Informat Sci & Engn Shenyang 110189 Liaoning Peoples R China Northeastern Univ State Key Laboratoryof Synthet Automat Proc Ind Shenyang 110189 Liaoning Peoples R China

In this brief, a costate-adaptive dynamic programming algorithm is presented to solve a class of nonlinear critical surface problems with input constraints. Different from the traditional-dual heuristic dynamic programming, costate-ADP does not rely on the exact system model, and can incubate the model-neural network through data catalysis, avoiding the super-dimensional calculation in the iterative process. Furthermore, the critical nonlinear surface with input constraints is transformed into the optimal action sequence for solving the non-quadratic HJB equation with discount factor. The costate function replaces the integral term in the repeated iterations. The algorithm is proved to be effective in determining the security properties of a class of structures.

关键词： Adaptive dynamic programming critical surface dual heuristic dynamic programming costate function non-quadratic

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