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IEEE TRANSACTIONS ON NEURAL NETWORKS 1997年第5期8卷 997-1007页

作者： Prokhorov, DV Wunsch, DC Laboratory of Chromatography DEPg.Fac.Quimica Universidad Nacional Autonoma de Mexico Circuito interior Cd Universitaria/CP 04510 Mexico D.F.Mexico

We discuss a variety of adaptive critic designs (ACD's) for neurocontrol. These are suitable for learning in noisy, nonlinear, and nonstationary environments. They have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Our discussion of these origins leads to an explanation of three design families: heuristic dynamic programming (HDP), dual heuristic programming (DHP), and globalized dual heuristic programming (GDHP). The main emphasis is on DHP and GDHP as advanced ACD's. We suggest two new modifications of the original GDHP design that are currently the only working implementations of GDHP. They promise to be useful for many engineering applications in the areas of optimization and optimal control. Based on one of these modifications, we present a unified approach to all ACD's. This leads to a generalized training procedure for ACD's.

关键词： adaptive critic design (ACD) backpropagation control DHP dynamic programming GDHP HDP heuristic dynamic programming neural network neurocontrol reinforcement learning

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A stochastic lot-sizing model with multi-supplier and quantity discounts

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2013年第1期51卷 245-263页

作者： Kang, He-Yau Lee, Amy H. I. Chung Hua Univ Dept Technol Management Hsinchu Taiwan Natl Chin Yi Univ Technol Dept Ind Engn & Management Taipei Taiwan

This research considers a stochastic lot-sizing problem with multi-supplier and quantity discounts. The objectives are to minimise total costs, where the costs include ordering cost, holding cost, purchase cost and shortage cost, and to maximise service level of the system. In this paper, we first formulate the stochastic lot-sizing problem as a multi-objective programming (MOP) model. We then transform the model into a mixed integer programming (MIP) model. Finally, an efficient heuristic dynamic programming (HDP) model is constructed for solving large-scale stochastic lot-sizing problems. An illustrative example with two cases for a touch panel manufacturer is used to illustrate the practicality of these models, and a sensitivity analysis is applied to understand the impact of the changes in parameters to the outcomes. The results demonstrate that the proposed two models are effective and accurate tools for determining the replenishment of touch panels from multiple suppliers for multi-periods.

关键词： stochastic lot-sizing mixed integer programming heuristic dynamic programming multi-objective programming

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Optimal Adaptive Ultra-Local Model-Free Control Based-Extended State Observer for PMSM Driven Single-Axis Servo Mechanism System

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第5期60卷 7728-7745页

作者： El-Sousy, Fayez F. M. Amin, Mahmoud M. Soliman, Ahmed S. Mohammed, Osama A. Prince Sattam bin Abdulaziz Univ Dept Elect Engn Al Kharj 16273 Saudi Arabia Manhattan Coll Dept Elect & Comp Engn 4513 Manhattan Coll Pkwy Riverdale NY 10471 USA Florida Int Univ Dept Elect & Comp Engn Miami FL 33174 USA

This paper proposes an optimal adaptive ultra-local model-free control (OAULMFC) scheme for servo-mechanism actuated through a permanent-magnet synchronous motor drive. Owing to the uncertainties in real-time applications, the model-based controller cannot achieve good dynamic performance. Therefore, an OAULMFC based on ultra-local model is developed. The proposed control scheme requires estimating the total disturbance and the control input gains. Therefore, the composite control structure of the OAULMFC combines an ultra-local model-free controller (ULMFC), a robust adaptive extended state observer (ESO) and an optimal ${{\mathcal{H}}_\infty }$ controller. The ESO is developed to estimate the total disturbance whereas the optimal ${{\mathcal{H}}_\infty }$ controller is adopted as the auxiliary controller to provide the optimal control performance of the servo mechanism. For enhancing the estimation accuracy, an online direct heuristic dynamic programming (DHDP) strategy is utilized for adaptation the ESO parameters and control input gains of the ULMFC. Accordingly, an actor-critic neural-networks with DHDP is adopted to solve the Hamilton-Jacobi-Bellman (HJB) equation online. Concurrently, the actor neural-network provides the optimal control performance. Furthermore, the system stability and convergence conditions via Lyapunov theory is proved. The experimental results confirmed the robustness of the proposed OAULMFC in case of model perturbations and external disturbances compared with the conventional ULMFC.

关键词： Adaptation models Estimation Uncertainty Robustness dynamic programming Adaptive systems Servomechanisms Ultra-local model free-control extended state observer (ESO) PMSM servo-mechanism neural-network heuristic dynamic programming Hamilton-Jacobi-Bellman Lyapunov stability Lyapunov stability

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Stability and monotone convergence of generalised policy iteration for discrete-time linear quadratic regulations

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INTERNATIONAL JOURNAL OF CONTROL 2016年第3期89卷 437-450页

作者： Chun, Tae Yoon Lee, Jae Young Park, Jin Bae Choi, Yoon Ho Yonsei Univ Dept Elect & Elect Engn Seoul 120749 South Korea Kyonggi Univ Dept Elect Engn Suwon Kyonggi Do South Korea

In this paper, we analyse the convergence and stability properties of generalised policy iteration (GPI) applied to discrete-time linear quadratic regulation problems. GPI is one kind of the generalised adaptive dynamic programming methods used for solving optimal control problems, and is composed of policy evaluation and policy improvement steps. To analyse the convergence and stability of GPI, the dynamic programming (DP) operator is defined. Then, GPI and its equivalent formulas are presented based on the notation of DP operator. The convergence of the approximate value function to the exact one in policy evaluation is proven based on the equivalent formulas. Furthermore, the positive semi-definiteness, stability, and the monotone convergence (PI-mode and VI-mode convergence) of GPI are presented under certain conditions on the initial value function. The online least square method is also presented for the implementation of GPI. Finally, some numerical simulations are carried out to verify the effectiveness of GPI as well as to further investigate the convergence and stability properties.

关键词： Adaptive dynamic programming generalised policy iteration heuristic dynamic programming linear quadratic regulation

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On Distributed Implementation of Switch-Based Adaptive dynamic programming

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 7218-7224页

作者： Liu, Di Baldi, Simone Yu, Wenwu Chen, Guanrong Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intelli NL-9747 AG Groningen Netherlands Southeast Univ Sch Math Nanjing 210096 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Switch-based adaptive dynamic programming (ADP) is an optimal control problem in which a cost must be minimized by switching among a family of dynamical modes. When the system dimension increases, the solution to switch-based ADP is made prohibitive by the exponentially increasing structure of the value function approximator and by the exponentially increasing modes. This technical correspondence proposes a distributed computational method for solving switch-based ADP. The method relies on partitioning the system into agents, each one dealing with a lower dimensional state and a few local modes. Each agent aims to minimize a local version of the global cost while avoiding that its local switching strategy has conflicts with the switching strategies of the neighboring agents. A heuristic algorithm based on the consensus dynamics and Nash equilibrium is proposed to avoid such conflicts. The effectiveness of the proposed method is verified via traffic and building test cases.

关键词： Switches Neural networks Games dynamic programming Switched systems Cybernetics Consensus distributed adaptive dynamic programming (ADP) heuristic dynamic programming optimal switching

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Neural-network-based zero-sum game for discrete-time nonlinear systems via iterative adaptive dynamic programming algorithm

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NEUROCOMPUTING 2013年 110卷 92-100页

作者： Liu, Derong Li, Hongliang Wang, Ding Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, we solve the zero-sum game problems for discrete-time affine nonlinear systems with known dynamics via iterative adaptive dynamic programming algorithm. First, a greedy heuristic dynamic programming iteration algorithm is developed to solve the zero-sum game problems, which can be used to solve the Hamilton-Jacobi-Isaacs equation associated with H-infinity optimal regulation control problems. The convergence analysis in terms of value function and control policy is provided. To facilitate the implementation of the algorithm, three neural networks are used to approximate the control policy, the disturbance policy, and the value function, respectively. Then, we extend the algorithm to R, optimal tracking control problems through system transformation. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed scheme. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Adaptive dynamic programming Approximate dynamic programming heuristic dynamic programming Neural networks Zero-sum game H-infinity optimal control

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Adaptive learning control for triggered switched systems based on unknown direction control gain function

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INTERNATIONAL JOURNAL OF CONTROL 2024年第2期97卷 175-186页

作者： Qi, Yiwen Geng, Honglin Xing, Ning Shenyang Aerosp Univ Sch Automat Shenyang 110136 Peoples R China

This paper investigates event-triggered switched heuristic dynamic programming (HDP) control for switched systems based on an unknown direction control gain function (UDCGF). First, the Nussbaum gain function is introduced to describe the unknown control direction, which makes the control behaviour of switched systems more flexible. The proposed variable threshold event-triggering (VTET) condition can significantly reduce computation while maintaining a certain system performance. Then, a detailed Lyapunov stability analysis based on the new tight bound is given, which reflects the asymptotic stability of the switched systems under the proposed switched HDP control method. The system mode-matched action-critic neural networks are employed to approximate the optimal triggered control law and the optimal switching value function, respectively. Subsequently, the boundedness analysis of neural network (NN) weights and the convergence analysis of HDP algorithm are given. At last, a numerical example is provided to verify the effectiveness of the proposed method.

关键词： Switched systems heuristic dynamic programming unknown direction control gain function event-triggering method

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Neural-network-based optimal tracking control scheme for a class of unknown discrete-time nonlinear systems using iterative ADP algorithm

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NEUROCOMPUTING 2014年 125卷 46-56页

作者： Huang, Yuzhu Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, an optimal tracking control scheme is proposed for a class of unknown discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) algorithm. First, in order to obtain the dynamics of the system, an identifier is constructed by a three-layer feedforward neural network (NN). Second, a feedforward neuro-controller is designed to get the desired control input of the system. Third, via system transformation, the original tracking problem is transformed into a regulation problem with respect to the state tracking error. Then, the iterative ADP algorithm based on heuristic dynamic programming is introduced to deal with the regulation problem with convergence analysis. In this scheme, feedforward NNs are used as parametric structures for facilitating the implementation of the iterative algorithm. Finally, simulation results are also presented to demonstrate the effectiveness of the proposed scheme. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Adaptive dynamic programming Convergence analysis heuristic dynamic programming Neural networks Optimal tracking control Reinforcement learning

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Adaptive critic designs: A case study for neurocontrol

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NEURAL NETWORKS 1995年第9期8卷 1367-1372页

作者： Prokhorov, DV Santiago, RA Wunsch, DC Texas Tech University United States BehavHeuristics Inc. United States

For the first time, different adaptive critic designs (ACDs), a conventional proportional integral derivative (PID) regulator and backpropagation of utility are compared for the same control problem-automatic aircraft landing. The original problem proved to contain little challenge since various conventional and neural network techniques had solved it very well. After the problem had been made much more difficult by a change of parameters, increasingly better performance was observed by going from the simplest ACD to more sophisticated designs, dual heuristic programming having been ranked best of all. This case study is of use in general intelligent control problems for it provides an example of the capabilities of different adaptive critic designs.

关键词： adaptive critic heuristic dynamic programming aircraft autolanding neurocontrol neural networks for control and optimization

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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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