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Guidance, Navigation and Control 2024年第2期4卷 25-46页

作者： Junrui Guo Xiaoyang Gao Tieshan Li School of Automation Engineering University of Electronic Science and Technology of China Chengdu Sichuan 611731P.R.China

This paper proposes an optimal output feedback tracking control scheme of the quadrotor unmanned aerial vehicle(UAV)attitude system with unmeasured angular velocities and model ***,neural network(NN)is used to approximate the model ***,an NN velocity observer is established to estimate the unmeasured angular ***,a quadrotor output feedback attitude optimal tracking controller is designed,which consists of an adaptive controller designed by backstepping method and an optimal compensation term designed by adaptive dynamic *** signals in the closed-loop system are proved to be ***,numerical simulation example shows that the quadrotor attitude tracking scheme is effective and feasible.

关键词： Quadrotor optimal tracking control neural network velocity observer model uncertainties adaptive dynamic programming

adaptive dynamic programming Based Decentralized Tracking Control for Unknown Large-scale Systems 36

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Adaptive Dynamic Programming Based Decentralized Tracking Co...

Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

第36届中国控制会议

作者： Bo Zhao Derong Liu Yuanchun Li Qinglai Wei Ruizhuo Song The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation Guangdong University of Technology Department of Control Science and Engineering Changchun University of Technology School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (纸本)9781538629185

This paper investigates decentralized tracking control（DTC） problem for unknown large-scale systems via adaptive dynamic *** DTC consists of local desired control and local tracking error *** replacing the actual states of coupled subsystems in interconnection terms with their desired states,the norm-boundedness assumption can be *** on local neural network（NN） observers,unknown subsystems can be *** helps to obtain the local desired control with corresponding desired *** establishing a proper local cost function,the local tracking error control is derived via local critic NN and the identified input gain *** the entire error caused by replacement,identification and critic NN approximation,an adaptive robust term is added to construct the robust local cost function that reflects the entire error,regulation and control ***,the asymptotic stability can be guaranteed for the closed-loop tracking system via Lyapunov stability *** demonstrate the effectiveness of the proposed DTC,simulation on a hard spring connected parallel inverted pendulum system is given.

关键词： adaptive dynamic programming Unknown large-scale systems Decentralized tracking control Neural networks Observer

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Chinese Physics B 2015年第3期24卷 87-94页

作者：魏庆来刘德荣徐延才 The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

A policy iteration algorithm of adaptive dynamic programming（ADP） is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then,the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks,the developed optimal tracking control scheme for chaotic systems is verified by a simulation.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming neuro-dynamic programming

adaptive event-triggered distributed optimal guidance design via adaptive dynamic programming

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Chinese Journal of Aeronautics 2022年第7期35卷 113-127页

作者： Teng LONG Yan CAO Jingliang SUN Guangtong XU School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Key Laboratory of dynamics and Control of Flight Vehicle Ministry of Education ChinaBeijing 100081China Department of Precision Instrument Tsinghua UniversityBeijing 100084China

In this paper,the multi-missile cooperative guidance system is formulated as a general nonlinear multi-agent *** save the limited communication resources,an adaptive eventtriggered optimal guidance law is proposed by designing a synchronization-error-driven triggering condition,which brings together the consensus control with adaptive dynamic programming(ADP)***,the developed event-triggered distributed control law can be employed by finding an approximate solution of event-triggered coupled Hamilton-Jacobi-Bellman(HJB)*** address this issue,the critic network architecture is constructed,in which an adaptive weight updating law is designed for estimating the cooperative optimal cost function ***,the event-triggered closed-loop system is decomposed into two subsystems:the system with flow dynamics and the system with jump *** using Lyapunov method,the stability of this closed-loop system is guaranteed and all signals are ensured to be Uniformly Ultimately Bounded(UUB).Furthermore,the Zeno behavior is *** results are finally provided to demonstrate the effectiveness of the proposed method.

关键词： adaptive dynamic programming Distributed control Event-triggered Guidance and control Multi-agent system

Residential Energy Scheduling for Variable Weather Solar Energy Based on adaptive dynamic programming

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 36-46页

作者： Derong Liu Yancai Xu Qinglai Wei Xinliang Liu Guangdong University of Technology Guangzhou 510006 China The State Key Laboratory of Manage-ment and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China The Bureau of Informationization Development Cyberspace Administration of China Beijing 100010 China IEEE

The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable energy resources, are combined together as a nonlinear, time-varying, indefinite and complex system, which is difficult to manage or optimize. Many nations have already applied the residential real-time pricing to balance the burden on their grid. In order to enhance electricity efficiency of the residential micro grid, this paper presents an action dependent heuristic dynamic programming(ADHDP) method to solve the residential energy scheduling problem. The highlights of this paper are listed below. First,the weather-type classification is adopted to establish three types of programming models based on the features of the solar energy. In addition, the priorities of different energy resources are set to reduce the loss of electrical energy ***, three ADHDP-based neural networks, which can update themselves during applications, are designed to manage the flows of electricity. Third, simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process. The comparison with the particle swarm optimization algorithm further proves that the present method has a promising effect on energy management to save cost.

关键词： Action dependent heuristic dynamic programming adaptive dynamic programming control strategy residential energy management smart grid

A Robust adaptive dynamic programming Principle for Sensorimotor Control with Signal-Dependent Noise

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Journal of Systems Science & Complexity 2015年第2期28卷 261-288页

作者： JIANG Yu JIANG Zhong-Ping Department of Electrical and Computer Engineering Polytechnic School of EngineeringNew York University6 Metrotech Center

As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily *** characteristics of these interactions can be studied using optimization-based models,which assume that the precise knowledge of both the sensorimotor system and its interactive environment is available for the central nervous system(CNS).However,both static and dynamic uncertainties occur inevitably in the daily *** these uncertainties are taken into consideration,the previously developed models based on optimization theory may fail to explain how the CNS can still coordinate human movements which are also robust with respect to the *** order to address this problem,this paper presents a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming(RADP).Sharing some essential features of reinforcement learning,which was originally observed from mammals,the RADP model for sensorimotor control suggests that,instead of identifying the system dynamics of both the motor system and the environment,the CNS computes iteratively a robust optimal control policy using the real-time sensory *** online learning algorithm is provided in this paper,with rigorous convergence and stability ***,it is applied to simulate several experiments reported from the past *** comparing the proposed numerical results with these experimentally observed data,the authors show that the proposed model can reproduce movement trajectories which are consistent with experimental *** addition,the RADP theory provides a unified framework that connects optimality and robustness properties in the sensorimotor system.

关键词： adaptive dynamic programming human motor adaptation robust optimal control.

adaptive fault-tolerant control for non-minimum phase hypersonic vehicles based on adaptive dynamic programming

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Chinese Journal of Aeronautics 2024年第3期37卷 290-311页

作者： Le WANG Ruiyun QI Bin JIANG State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and AstronauticsNanjing 210016China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

In this paper,a novel adaptive Fault-Tolerant Control(FTC)strategy is proposed for non-minimum phase Hypersonic Vehicles(HSVs)that are affected by actuator faults and parameter *** strategy is based on the output redefinition method and adaptive dynamic programming(ADP).The intelligent FTC scheme consists of two main parts:a basic fault-tolerant and stable controller and an ADP-based supplementary *** the basic FTC part,an output redefinition approach is designed to make zero-dynamics stable with respect to the new ***,Ideal Internal dynamic(IID)is obtained using an optimal bounded inversion approach,and a tracking controller is designed for the new output to realize output tracking of the nonminimum phase HSV *** the ADP-based compensation control part,an ActionDependent Heuristic dynamic programming(ADHDP)adopting an actor-critic learning structure is utilized to further optimize the tracking performance of the HSV control ***,simulation results are provided to verify the effectiveness and efficiency of the proposed FTC algorithm.

关键词： Hypersonic vehicle Fault-tolerant control Non-minimum phase system adaptive control Nonlinear control adaptive dynamic programming

Neural-network-based stochastic linear quadratic optimal tracking control scheme for unknown discrete-time systems using adaptive dynamic programming

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Control Theory and Technology 2021年第3期19卷 315-327页

作者： Xin Chen Fang Wang School of Automation China University of GeosciencesWuhan430074HubeiChina Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074HubeiChina

In this paper,a stochastic linear quadratic optimal tracking scheme is proposed for unknown linear discrete-time(DT)systems based on adaptive dynamic programming(ADP)***,an augmented system composed of the original system and the command generator is constructed and then an augmented stochastic algebraic equation is derived based on the augmented ***,to obtain the optimal control strategy,the stochastic case is converted into the deterministic one by system transformation,and then an ADP algorithm is proposed with convergence *** the purpose of realizing the ADP algorithm,three back propagation neural networks including model network,critic network and action network are devised to guarantee unknown system model,optimal value function and optimal control strategy,***,the obtained optimal control strategy is applied to the original stochastic system,and two simulations are provided to demonstrate the effectiveness of the proposed algorithm.

关键词： Stochastic system Optimal tracking control adaptive dynamic programming Neural networks

Optimal regulation of uncertain dynamic systems using adaptive dynamic programming

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Journal of Control and Decision 2014年第3期1卷 226-256页

作者： Hao Xu Qiming Zhao S.Jagannathan Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMOUSA

In this tutorial paper,the finite-horizon optimal adaptive regulation of linear and nonlinear dynamic systems with unknown system dynamics is presented in a forward-in-time manner using adaptive dynamic programming(ADP).An adaptive estimator(AE)is introduced with the idea of Q-learning to relax the requirement of system dynamics in the case of linear system,while neural network-based identifier is utilised for nonlinear *** time-varying nature of the solution to the Bellman/Hamilton–Jacobi–Bellman equation is handled by utilising a time-dependent basis function,while the terminal constraint is incorporated as part of the update law of the AE/Identifier in solving the optimal feedback *** an initial admissible control,the proposed optimal regulation scheme of the uncertain linear and nonlinear system yields a forward-in-time and online solution without using value and/or policy *** adaptive observer is utilised for linear systems in order to relax the need for state availability so that the optimal adaptive control design depends only on the reconstructed ***,the optimal control is covered for nonlinear-networked control systems where in the feedback loop is closed via a communication *** of the proposed approach is verified by simulation *** end result is a variant of a roll-out scheme in ADP wherein an initial admissible policy is selected as the base policy and the control policy is enhanced using a one-time policy improvement at each sampling interval.

关键词： adaptive dynamic programming finite horizon optimal control

Finite horizon optimal control of discrete-time nonlinear systems with unfixed initial state using adaptive dynamic programming

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控制理论与应用（英文版） 2011年第3期9卷 381-390页

作者： Wei, Qinglai Liu, Derong Key Laboratory of Complex Systems and Intelligence Science Institute of Automatio Chinese Academy of Sciences Beijing 100190 China

In this paper,we aim to solve the finite horizon optimal control problem for a class of discrete-time nonlinear systems with unfixed initial state using adaptive dynamic programming(ADP) approach.A new-optimal control algorithm based on the iterative ADP approach is proposed which makes the performance index function converge iteratively to the greatest lower bound of all performance indices within an error according to within finite *** optimal number of control steps can also be obtained by the proposed-optimal control algorithm for the situation where the initial state of the system is *** networks are used to approximate the performance index function and compute the optimal control policy,respectively,for facilitating the implementation of the-optimal control ***,a simulation example is given to show the results of the proposed method.

关键词： adaptive dynamic programming Unfixed initial state Optimal control Finite time Neural networks