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H∞Optimal Output Regulation of Unknown Linear Systems via an Adaptive Dynamic Programming and Internal Model 认领 引用
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作者 Yong-Sheng Ma Jian Sun Yong Xu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1314-1324,共11页
This paper delves into the H∞optimal output regulation problem for continuous-time linear systems with an unknown system model.By integrating the internal model principle with optimal control,we derive an optimal con... This paper delves into the H∞optimal output regulation problem for continuous-time linear systems with an unknown system model.By integrating the internal model principle with optimal control,we derive an optimal control policy and a worst-case disturbance policy through the formulation and solution of a zero-sum game problem.Subsequently,leveraging adaptive dynamic programming,we propose a policy iteration learning algorithm capable of learning both the optimal control policy and the worst-case disturbance policy directly from system data.The existing algorithms necessitate an initial stabilizing policy,a full-rank condition,and the storage of historical data to guarantee algorithm convergence.In contrast,we design a dual policy iteration algorithm equipped with an online learning mechanism,thereby eliminating these additional prerequisites.Simulation results with an antonomous ground vehicle underscore the effectiveness of our proposed algorithm,and its superiority is further demonstrated through comparisons with existing methodologies. 展开更多
关键词 Adaptive dynamic programming internal model principle optimal control output regulation
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From recursion to tabulation:Rethinking how we teach dynamic programming 认领 引用
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作者 Jiang Long 《计算机教育》 2026年第6期302-311,共10页
This paper proposes an intuitive,modular teaching framework for dynamic programming(DP).Beginning with the fundamental concept of recursion,the framework leverages dependency graphs to visually illustrate structural r... This paper proposes an intuitive,modular teaching framework for dynamic programming(DP).Beginning with the fundamental concept of recursion,the framework leverages dependency graphs to visually illustrate structural relationships among subproblems.This visual approach helps students intuitively grasp the underlying logic of tabulation,enabling them to independently determine correct tabulation orders.The plug-and-play design significantly reduces the barrier to learning,while strengthening students'analytical thinking and problem-solving skills.The effectiveness of this method is demonstrated through classroom application using classic examples such as the longest common subsequence(LCS),matrix chain multiplication(MCM),and rod cutting problems. 展开更多
关键词 Dynamic programming Plug-and-play framework Algorithm pedagogy Computer science education
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Residential Energy Scheduling With Solar Energy Based on Dyna Adaptive Dynamic Programming 认领 引用 被引量：1
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作者 Kang Xiong Qinglai Wei Hongyang Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第2期403-413,共11页
Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we pr... Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we propose the Dyna actiondependent heuristic dynamic programming(Dyna-ADHDP)method, which incorporates the ideas of learning and planning from the Dyna framework in action-dependent heuristic dynamic programming. This method defines a continuous action space for precise control of an energy storage system and allows online optimization of algorithm performance during the real-time operation of the residential energy model. Meanwhile, the target network is introduced during the training process to make the training smoother and more efficient. We conducted experimental comparisons with the benchmark method using simulated and real data to verify its applicability and performance. The results confirm the method's excellent performance and generalization capabilities, as well as its excellence in increasing renewable energy utilization and extending equipment life. 展开更多
关键词 Adaptive dynamic programming(ADP) dynamic residential scenarios optimal residential energy management smart grid
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Value Iteration-Based Distributed Adaptive Dynamic Programming for Multi-Player Differential Game With Incomplete Information 认领 引用 被引量：1
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作者 Yun Zhang Yuqi Wang Yunze Cai 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第2期436-447,共12页
In this paper,a distributed adaptive dynamic programming(ADP)framework based on value iteration is proposed for multi-player differential games.In the game setting,players have no access to the information of others&#... In this paper,a distributed adaptive dynamic programming(ADP)framework based on value iteration is proposed for multi-player differential games.In the game setting,players have no access to the information of others'system parameters or control laws.Each player adopts an on-policy value iteration algorithm as the basic learning framework.To deal with the incomplete information structure,players collect a period of system trajectory data to compensate for the lack of information.The policy updating step is implemented by a nonlinear optimization problem aiming to search for the proximal admissible policy.Theoretical analysis shows that by adopting proximal policy searching rules,the approximated policies can converge to a neighborhood of equilibrium policies.The efficacy of our method is illustrated by three examples,which also demonstrate that the proposed method can accelerate the learning process compared with the centralized learning framework. 展开更多
关键词 Distributed adaptive dynamic programming incomplete information multi-player differential game(MPDG) value iteration
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PDP:Parallel Dynamic Programming 认领 引用 被引量：41
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作者 Fei-Yue Wang Jie Zhang +2 位作者 Qinglai Wei Xinhu Zheng Li Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2017年第1期1-5,共5页
Deep reinforcement learning is a focus research area in artificial intelligence.The principle of optimality in dynamic programming is a key to the success of reinforcement learning methods.The principle of adaptive dy... Deep reinforcement learning is a focus research area in artificial intelligence.The principle of optimality in dynamic programming is a key to the success of reinforcement learning methods.The principle of adaptive dynamic programming ADP is first presented instead of direct dynamic programming DP,and the inherent relationship between ADP and deep reinforcement learning is developed.Next,analytics intelligence,as the necessary requirement,for the real reinforcement learning,is discussed.Finally,the principle of the parallel dynamic programming,which integrates dynamic programming and analytics intelligence,is presented as the future computational intelligence.©2014 Chinese Association of Automation. 展开更多
关键词 Parallel dynamic programming Dynamic programming Adaptive dynamic programming Reinforcement learning Deep learning Neural networks Artificial intelligence
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基于ADP的非对称约束系统动态事件触发方法 认领 引用
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作者 薛珊 赵宁 张卫东 《自动化学报》 EI CAS CSCD 北大核心 2026年第6期1209-1220,共12页
本文提出一种基于自适应动态规划的动态事件触发方法(DEM),用于解决具有状态与控制双重非对称约束的非线性连续时间系统最优控制问题.首先,利用非线性映射函数将非对称约束系统的控制问题转化为无约束形式.然后,设计一种静态事件触发方... 本文提出一种基于自适应动态规划的动态事件触发方法(DEM),用于解决具有状态与控制双重非对称约束的非线性连续时间系统最优控制问题.首先,利用非线性映射函数将非对称约束系统的控制问题转化为无约束形式.然后,设计一种静态事件触发方法(SEM),其中触发条件仅与当前状态相关.进一步,开发一种依赖额外内部动态变量的DEM,其触发条件也与系统历史信息相关.事实上,DEM是SEM的进阶方法.理论分析证实DEM在确保系统性能的情况下,能够进一步节省计算和网络资源.最后,介绍基于神经网络的实现方法.在无人水面艇仿真实验环境下,该方法的有效性得到了验证. 展开更多
关键词 自适应动态规划 自适应评价设计 神经网络 事件触发方法 动态事件触发方法
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Model-Free Coordinated Optimal Regulation for Rigidly Connected Dual-PMSM Systems via Adaptive Dynamic Programming 认领 引用
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作者 Jianguo Zhao Linna Zhou +2 位作者 Weinan Gao Hai Wang Chunyu Yang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第10期2138-2149,共12页
In this article,a novel model-free coordinated optimal regulation design methodology is proposed for the rigidly connected dual permanent magnet synchronous motor(PMSM)system via adaptive dynamic programming(ADP).Firs... In this article,a novel model-free coordinated optimal regulation design methodology is proposed for the rigidly connected dual permanent magnet synchronous motor(PMSM)system via adaptive dynamic programming(ADP).First,we adopt the classical master-slave structure to maintain torque synchronization by virtue of field-oriented control.Then,a reducedorder model of the dual-PMSM system is established through the application of singular perturbation theory(SPT),which is of significance to decrease the learning time and computational complexity in the outer speed loop design.Afterwards,we design a coordinated adaptive optimal regulator in framework of ADP to drive the speed of girth gear asymptotic tracking the reference signal and accommodate the load torque disturbance,which is independent of the knowledge of model parameters of the system.According to SPT,we analyze the suboptimality,closed-loop stability,and robustness properties of the obtained controller under mild conditions.Finally,comprehensive experimental studies are provided to verify that the proposed control strategy can achieve the speed regulation and the torque synchronization,as well as ameliorate the transient response. 展开更多
关键词 Adaptive dynamic programming(ADP) optimal control output regulation permanent magnet synchronous motor(PMSM) singular perturbation theory(SPT)
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UAV flight strategy algorithm based on dynamic programming 认领 引用 被引量：8
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作者 ZHANG Zixuan WU Qinhao +2 位作者 ZHANG Bo YI Xiaodong TANG Yuhua 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第6期1293-1299,共7页
Unmanned aerial vehicles(UAVs) may play an important role in data collection and offloading in vast areas deploying wireless sensor networks, and the UAV’s action strategy has a vital influence on achieving applicabi... Unmanned aerial vehicles(UAVs) may play an important role in data collection and offloading in vast areas deploying wireless sensor networks, and the UAV’s action strategy has a vital influence on achieving applicability and computational complexity. Dynamic programming(DP) has a good application in the path planning of UAV, but there are problems in the applicability of special terrain environment and the complexity of the algorithm.Based on the analysis of DP, this paper proposes a hierarchical directional DP(DDP) algorithm based on direction determination and hierarchical model. We compare our methods with Q-learning and DP algorithm by experiments, and the results show that our method can improve the terrain applicability, meanwhile greatly reduce the computational complexity. 展开更多
关键词 motion state space map stratification computational complexity dynamic programming(DP) envirommental adaptability
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Approximate Dynamic Programming for Stochastic Resource Allocation Problems 认领 引用 被引量：5
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作者 Ali Forootani Raffaele Iervolino +1 位作者 Massimo Tipaldi Joshua Neilson 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第4期975-990,共16页
A stochastic resource allocation model, based on the principles of Markov decision processes(MDPs), is proposed in this paper. In particular, a general-purpose framework is developed, which takes into account resource... A stochastic resource allocation model, based on the principles of Markov decision processes(MDPs), is proposed in this paper. In particular, a general-purpose framework is developed, which takes into account resource requests for both instant and future needs. The considered framework can handle two types of reservations(i.e., specified and unspecified time interval reservation requests), and implement an overbooking business strategy to further increase business revenues. The resulting dynamic pricing problems can be regarded as sequential decision-making problems under uncertainty, which is solved by means of stochastic dynamic programming(DP) based algorithms. In this regard, Bellman’s backward principle of optimality is exploited in order to provide all the implementation mechanisms for the proposed reservation pricing algorithm. The curse of dimensionality, as the inevitable issue of the DP both for instant resource requests and future resource reservations,occurs. In particular, an approximate dynamic programming(ADP) technique based on linear function approximations is applied to solve such scalability issues. Several examples are provided to show the effectiveness of the proposed approach. 展开更多
关键词 Approximate dynamic programming(ADP) dynamic programming(DP) Markov decision processes(MDPs) resource allocation problem
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Residential Energy Scheduling for Variable Weather Solar Energy Based on Adaptive Dynamic Programming 认领 引用 被引量：20
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作者 Derong Liu Yancai Xu +1 位作者 Qinglai Wei Xinliang Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2018年第1期36-46,共11页
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 ener... 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 transmissions.Second, 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. 展开更多
关键词 Action dependent heuristic dynamic programming adaptive dynamic programming control strategy residential energy management smart grid
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Using approximate dynamic programming for multi-ESM scheduling to track ground moving targets 认领 引用 被引量：6
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作者 WAN Kaifang GAO Xiaoguang +1 位作者 LI Bo LI Fei 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第1期74-85,共12页
This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain e... This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain environment. For adaptive selection of appropriate ESMs, we generalize an approximate dynamic programming(ADP) framework to the dynamic case. We define the environment model and agent model, respectively. To handle the partially observable challenge, we apply the unsented Kalman filter(UKF) algorithm for belief state estimation. To reduce the computational burden, a simulation-based approach rollout with a redesigned base policy is proposed to approximate the long-term cumulative reward. Meanwhile, Monte Carlo sampling is combined into the rollout to estimate the expectation of the rewards. The experiments indicate that our method outperforms other strategies due to its better performance in larger-scale problems. 展开更多
关键词 sensor scheduling target tracking approximate dynamic programming non-myopic rollout belief state
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Adaptive event-triggered distributed optimal guidance design via adaptive dynamic programming 认领 引用 被引量：11
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作者 Teng LONG Yan CAO +1 位作者 Jingliang SUN Guangtong XU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第7期113-127,共15页
In this paper,the multi-missile cooperative guidance system is formulated as a general nonlinear multi-agent system.To save the limited communication resources,an adaptive eventtriggered optimal guidance law is propos... In this paper,the multi-missile cooperative guidance system is formulated as a general nonlinear multi-agent system.To 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)technique.Then,the developed event-triggered distributed control law can be employed by finding an approximate solution of event-triggered coupled Hamilton-Jacobi-Bellman(HJB)equation.To 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 online.Therefore,the event-triggered closed-loop system is decomposed into two subsystems:the system with flow dynamics and the system with jump dynamics.By 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 avoided.Simulation results are finally provided to demonstrate the effectiveness of the proposed method. 展开更多
关键词 Adaptive dynamic programming Distributed control Event-triggered Guidance and control Multi-agent system
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Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach 认领 引用 被引量：4
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作者 魏庆来 刘德荣 徐延才 《Chinese Physics B》 SCIE EI CAS CSCD 2015年第3期87-94,共8页
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 prob... 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. 展开更多
关键词 adaptive critic designs adaptive dynamic programming approximate dynamic programming neuro-dynamic programming
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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications 认领 引用 被引量：24
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作者 Ding Wang Ning Gao +2 位作者 Derong Liu Jinna Li Frank L.Lewis 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第1期18-36,共19页
Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and ... Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence. 展开更多
关键词 Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)
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Parallel Control for Optimal Tracking via Adaptive Dynamic Programming 认领 引用 被引量：31
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作者 Jingwei Lu Qinglai Wei Fei-Yue Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第6期1662-1674,共13页
This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is int... This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback system.However,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied directly.To address this problem,an augmented system and an augmented performance index function are proposed firstly.Thus,the general nonlinear system is transformed into an affine nonlinear system.The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically.It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function.Moreover,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function online.The stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals.Finally,the effectiveness of the developed optimal parallel control method is verified in two cases. 展开更多
关键词 Adaptive dynamic programming(ADP) nonlinear optimal control parallel controller parallel control theory parallel system tracking control neural network(NN)
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Genetic programming for predictions of effectiveness of rolling dynamic compaction with dynamic cone penetrometer test results 认领 引用 被引量：4
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作者 R.A.T.M.Ranasinghe M.B.Jaksa +1 位作者 F.Pooya Nejad Y.L.Kuo 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2019年第4期815-823,共9页
Rolling dynamic compaction (RDC),which employs non-circular module towed behind a tractor,is an innovative soil compaction method that has proven to be successful in many ground improvement applications.RDC involves r... Rolling dynamic compaction (RDC),which employs non-circular module towed behind a tractor,is an innovative soil compaction method that has proven to be successful in many ground improvement applications.RDC involves repeatedly delivering high-energy impact blows onto the ground surface,which improves soil density and thus soil strength and stiffness.However,there exists a lack of methods to predict the effectiveness of RDC in different ground conditions,which has become a major obstacle to its adoption.For this,in this context,a prediction model is developed based on linear genetic programming (LGP),which is one of the common approaches in application of artificial intelligence for nonlinear forecasting.The model is based on in situ density-related data in terms of dynamic cone penetrometer (DCP) results obtained from several projects that have employed the 4-sided,8-t impact roller (BH-1300).It is shown that the model is accurate and reliable over a range of soil types.Furthermore,a series of parametric studies confirms its robustness in generalizing data.In addition,the results of the comparative study indicate that the optimal LGP model has a better predictive performance than the existing artificial neural network (ANN) model developed earlier by the authors. 展开更多
关键词 Ground improvement Rolling dynamic compaction (RDC) Linear genetic programming (LGP) Dynamic cone penetrometer (DCP) test
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Dynamic programming methodology for multi-criteria group decision-making under ordinal preferences 认领 引用 被引量：3
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作者 Wu Li Guanqi Guo +1 位作者 Chaoyuan Yue Yong Zhao 《Journal of Systems Engineering and Electronics》 SCIE EI 2010年第6期975-980,共6页
A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the ... A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the total inconsistency between the rankings of all alternatives for the group and the ones for every decision maker is defined after the decision maker weights in respect to the criteria are considered. Similarly, the total inconsistency between their final rankings for the group and the ones under every criteria is determined after the criteria weights are taken into account. Then two nonlinear integer programming models minimizing respectively the two total inconsistencies above are developed and then transformed to two dynamic programming models to obtain separately the rankings of all alternatives for the group with respect to each criteria and their final rankings. A supplier selection case illustrated the proposed method, and some discussions on the results verified its effectiveness. This work develops a new measurement of ordinal preferences’ inconsistency in multi-criteria group decision-making （MCGDM） and extends the cook-seiford social selection function to MCGDM considering weights of criteria and decision makers and can obtain unique ranking result. 展开更多
关键词 multi-criteria group decision-making （MCGDM） ordinal preference minimum deviation method dynamic programming Cook-Seiford social selection function.
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Adaptive dynamic programming for finite-horizon optimal control of linear time-varying discrete-time systems 认领 引用 被引量：5
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作者 Bo PANG Tao BIAN Zhong-Ping JIANG 《Control Theory and Technology》 EI CSCD 2019年第1期73-84,共12页
This paper studies data-driven learning-based methods for the finite-horizon optimal control of linear time-varying discretetime systems. First, a novel finite-horizon Policy Iteration (PI) method for linear time-vary... This paper studies data-driven learning-based methods for the finite-horizon optimal control of linear time-varying discretetime systems. First, a novel finite-horizon Policy Iteration (PI) method for linear time-varying discrete-time systems is presented. Its connections with existing in finite-horizon PI methods are discussed. Then, both data-drive n off-policy PI and Value Iteration (VI) algorithms are derived to find approximate optimal controllers when the system dynamics is completely unknown. Under mild conditions, the proposed data-driven off-policy algorithms converge to the optimal solution. Finally, the effectiveness and feasibility of the developed methods are validated by a practical example of spacecraft attitude control. 展开更多
关键词 Optimal control time-varying system adaptive dynamic programming policy iteration (PI) value iteration(VI)
A COMBINED PARAMETRIC QUADRATIC PROGRAMMING AND PRECISE INTEGRATION METHOD BASED DYNAMIC ANALYSIS OF ELASTIC-PLASTIC HARDENING/SOFTENING PROBLEMS 认领 引用 被引量：3
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作者 张洪武 张新伟 《Acta Mechanica Sinica》 SCIE EI CAS 2002年第6期638-648,共11页
The objective of the paper is to develop a new algorithm for numerical solution of dynamic elastic-plastic strain hardening/softening problems. The gradient dependent model is adopted in the numerical model to overcom... The objective of the paper is to develop a new algorithm for numerical solution of dynamic elastic-plastic strain hardening/softening problems. The gradient dependent model is adopted in the numerical model to overcome the result mesh-sensitivity problem in the dynamic strain softening or strain localization analysis. The equations for the dynamic elastic-plastic problems are derived in terms of the parametric variational principle, which is valid for associated, non-associated and strain softening plastic constitutive models in the finite element analysis. The precise integration method, which has been widely used for discretization in time domain of the linear problems, is introduced for the solution of dynamic nonlinear equations. The new algorithm proposed is based on the combination of the parametric quadratic programming method and the precise integration method and has all the advantages in both of the algorithms. Results of numerical examples demonstrate not only the validity, but also the advantages of the algorithm proposed for the numerical solution of nonlinear dynamic problems. 展开更多
关键词 precise integration method parametric quadratic programming method strain localization strain softening dynamic response
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Optimal Control for a Class of Complex Singular System Based on Adaptive Dynamic Programming 认领 引用 被引量：5
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作者 Zhan Shi Zhanshan Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第1期188-197,共10页
This paper presents a new design approach to achieve decentralized optimal control of high-dimension complex singular systems with dynamic uncertainties. Based on robust adaptive dynamic programming(robust ADP) method... This paper presents a new design approach to achieve decentralized optimal control of high-dimension complex singular systems with dynamic uncertainties. Based on robust adaptive dynamic programming(robust ADP) method, controllers for solving the singular systems optimal control problem are designed. The proposed algorithm can work well when the system model is not exactly known but the input and output data can be measured. The policy iteration of each controller only uses their own states and input information for learning,and do not need to know the whole system dynamics. Simulation results on the New England 10-machine 39-bus test system show the effectiveness of the designed controller. 展开更多
关键词 Adaptive dynamic programming (ADP) decentralized control frequency control power system singular systems
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