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Adaptive dynamic programming for Finite-Horizon Optimal Control of Discrete-Time Nonlinear Systems with ε-Error Bound

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IEEE TRANSACTIONS ON neural NETWORKS 2011年第1期22卷 24-36页

作者： Wang, Fei-Yue Jin, Ning Liu, Derong Wei, Qinglai Chinese Acad Sci Inst Automat Key Lab Complex Syst & Intelligence Sci Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this paper, we study the finite-horizon optimal control problem for discrete-time nonlinear systems using the adaptive dynamic programming (ADP) approach. The idea is to use an iterative ADP algorithm to obtain the optimal control law which makes the performance index function close to the greatest lower bound of all performance indices within an epsilon-error bound. The optimal number of control steps can also be obtained by the proposed ADP algorithms. A convergence analysis of the proposed ADP algorithms in terms of performance index function and control policy is made. In order to facilitate the implementation of the iterative ADP algorithms, neural networks are used for approximating the performance index function, computing the optimal control policy, and modeling the nonlinear system. Finally, two simulation examples are employed to illustrate the applicability of the proposed method.

关键词： Adaptive critic designs adaptive dynamic programming approximate dynamic programming learning control neural control neural dynamic programming optimal control reinforcement learning

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Adaptive dynamic programming for Control: A Survey and Recent Advances

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第1期51卷 142-160页

作者： Liu, Derong Xue, Shan Zhao, Bo Luo, Biao Wei, Qinglai Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This article reviews the recent development of adaptive dynamic programming (ADP) with applications in control. First, its applications in optimal regulation are introduced, and some skilled and efficient algorithms are presented. Next, the use of ADP to solve game problems, mainly nonzero-sum game problems, is elaborated. It is followed by applications in large-scale systems. Note that although the functions presented in this article are based on continuous-time systems, various applications of ADP in discrete-time systems are also analyzed. Moreover, in each section, not only some existing techniques are discussed, but also possible directions for future work are pointed out. Finally, some overall prospects for the future are given, followed by conclusions of this article. Through a comprehensive and complete investigation of its applications in many existing fields, this article fully demonstrates that the ADP intelligent control method is promising in today's artificial intelligence era. Furthermore, it also plays a significant role in promoting economic and social development.

关键词： Adaptive critic designs (ACDs) adaptive dynamic programming approximate dynamic programming intelligent control learning control neural dynamic programming neuro-dynamic programming optimal control reinforcement learning (RL)

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Renewable Energy Management Using Action Dependent Heuristic dynamic programming

Renewable Energy Management Using Action Dependent Heuristic...

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IEEE International Smart Cities Conference (ISC2)

作者： Sterling, Gulnaz Tyler, Benjamin Nazarbayev Univ Dept Comp Sci Astana Kazakhstan

ISBN: (纸本)9781538659595

With increases in global energy demand and the rapid consumption of fossil fuels, the use of green energy and more efficient energy management approaches are receiving serious attention. Our focus is on improving energy resource scheduling in smart buildings and homes to minimize cost, while meeting energy demand. Here, we present an approach using Action Dependent Heuristic dynamic programming (ADHDP) optimization for a smart home set-up using solar panels, wind turbines, and a storage battery. In this work, we trained and evaluated our ADHDP approach using different simulation scenarios with various amounts of available renewable energy. We then demonstrated via computer simulation that our approach is more effective in cost minimization compared to a standard rule-based method. A correlation between optimization improvement and available renewable energy was also confirmed by computer simulation in all scenarios.

关键词： Action Dependent Heuristic dynamic programming Smart Homes Smart Buildings Energy Resource Scheduling Renewable Energy neural dynamic programming

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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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Online Learning Control for Hybrid Electric Vehicle

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Chinese Journal of Mechanical Engineering 2012年第1期25卷 98-106页

作者： LI Weimin XU Guoqing XU Yangsheng Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina

Improvements in hybrid electric vehicle (HEV) fuel economy and emissions heavily depend on an efficient energy management strategy (EMS). However, the uncertainty of future driving conditions generally cannot be easily tackled in EMS design. Most existing EMSs act upon fixed parameters and cannot adapt to varying driving conditions. Therefore, they usually fail to fully explore the potential of these advanced vehicles. In this paper, a novel EMS design procedure based on neural dynamic programming (NDP) is proposed. The NDP is a generic online learning algorithm, which combines stochastic dynamic programming (SDP) and the temporal difference (TD) method. Instead of computing the utility function and optimal control actions through Bellman equations, the NDP algorithm uses two neural networks to approximate them. The weights of these neural networks are updated online by the TD method. It avoids the high computational cost that SDP suffers from and is suitable for real-time implementation. The main advantages of NDP EMS is that it does not rely on prior information related to future driving conditions, and can self-tune with a wide variance in operating conditions. The NDP EMS has been applied to “Qianghua-I”, a prototype of a parallel HEV, using a revolving drum test bench for verification. Experiment results illustrate the potential of the proposed EMS in terms of fuel economy and in keeping state of charge (SOC) deviations at a low level. The proposed research ensures the optimality of NDP EMS, as well as real-time applicability.

关键词： hybrid electric vehicle neural dynamic programming energy management strategy

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A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints

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neural COMPUTING & APPLICATIONS 2013年第2期22卷 219-227页

作者： Wang, Ding Liu, Derong Zhao, Dongbin Huang, Yuzhu Zhang, Dehua Chinese Acad Sci Inst Automat Key Lab Complex Syst & Intelligence Sci Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this paper, a novel neural-network-based iterative adaptive dynamic programming (ADP) algorithm is proposed. It aims at solving the optimal control problem of a class of nonlinear discrete-time systems with control constraints. By introducing a generalized nonquadratic functional, the iterative ADP algorithm through globalized dual heuristic programming technique is developed to design optimal controller with convergence analysis. Three neural networks are constructed as parametric structures to facilitate the implementation of the iterative algorithm. They are used for approximating at each iteration the cost function, the optimal control law, and the controlled nonlinear discrete-time system, respectively. A simulation example is also provided to verify the effectiveness of the control scheme in solving the constrained optimal control problem.

关键词： Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming neural dynamic programming neural networks Optimal control Reinforcement learning

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Reinforcement-Based Robust Variable Pitch Control of Wind Turbines

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IEEE ACCESS 2020年 8卷 20493-20502页

作者： Chen, Peng Han, Dezhi Tan, Fuxiao Wang, Jun Shanghai Maritime Univ Dept Comp Sci & Technol Shanghai 200120 Peoples R China Univ Cent Florida Dept ECE Orlando FL 32816 USA

Due to the influence of wind speed disturbance, there are some uncertain phenomena in the parameters of the nonlinear wind turbine model with time in an actual working environment. In order to mitigate the side effects of uncertainties in speed models of wind turbines, researchers have designed a variety of controllers in recent years. However, traditional control methods require more knowledge of dynamics. Therefore, based on reinforcement learning and system state data, a robust wind turbine controller that adopts adaptive dynamic programming (ADP) is proposed. The ADP algorithm is a combination of Temporal-Difference (TD) algorithm and actor-critic structure, which can guarantee the rotor speed is stable around the rated value to indirectly adjust the wind energy utilization coefficient by changing the pitch angle in the area of high wind speed and achieve online learning in real-time. In addition, the variation of the pitch angle command of the proposed controller is relatively gradual, which can reduce the energy consumption of the variable pitch actuator, and extend the service life of the equipment. Finally, the wind speed model is simulated by combined wind speed based on Weibull distribution, the comprehensive simulation results show that the proposed controller has better control effect than some existing ones.

关键词： neural dynamic programming reinforcement learning robust control wind turbine system

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A self-learning call admission control scheme for CDMA cellular networks

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IEEE TRANSACTIONS ON neural NETWORKS 2005年第5期16卷 1219-1228页

作者： Liu, DR Zhang, Y Zhang, HG Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Northeastern Univ Sch Informat Sci & Engn Liaoning 110004 Peoples R China

In the present paper, a call admission control scheme that can learn from the network environment and user behavior is developed for code division multiple access (CDMA) cellular networks that handle both voice and data services. The idea is built upon a novel learning control architecture with only a single module instead of two or three modules in adaptive critic designs (ACDs). The use of adaptive critic approach for call admission control in wireless cellular networks is new. The call admission controller can perform learning in real-time as well as in offline environments and the controller improves its performance as it gains more experience. Another important contribution in the present work is the choice of utility function for the present self-learning control approach which makes the present learning process much more efficient than existing learning control methods. The performance of our algorithm will be shown through computer simulation and compared with existing algorithms.

关键词： adaptive critic designs (ACDs) approximate dynamic programming call admission control code division multiple access (CDMA) cellular networks neural dynamic programming wireless networks

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Helicopter flight-control reconfiguration for main rotor actuator failures

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JOURNAL OF GUIDANCE CONTROL AND dynamicS 2003年第4期26卷 572-584页

作者： Enns, R Si, J Arizona State Univ Dept Elect Engn Tempe AZ 85287 USA

Although reconfigurable flight control has been well demonstrated on fixed-wing aircraft using existing control surface redundancies, such failure accommodations were widely believed to be impossible for single main rotor helicopters. In fact, on any such existing helicopter, the failure of a main rotor actuator is catastrophic, resulting in a complete loss of control. An actuator geometry that enables reconfigurable helicopter flight control is presented, and, from this, reconfigurable flight-control methods are developed to accommodate main rotor actuator failures. The geometry provides control axes coupling that is key to solving the reconfigurable flight-control problem. Such coupling enables the development of a swashplate reconfiguration strategy, where any two of the three control axes can be controlled at a time, despite a failure in any one of the main rotor actuators. Of most interest is the particular swashplate reconfiguration solution where attitude control (pitch and roll) is retained, whereas vertical control is sacrificed. Vertical control is then achieved by one of two methods: flight to a longitudinal velocity that supports the desired vertical velocity or the use of rotor speed to change the main rotor's thrust to perform limited closed-loop vertical control. These methods are combined to form a robust reconfigurable flight-control method. All of these methods are independent of the underlying flight-control system. Such independence is demonstrated by evaluation of the reconfigurable flight-control methods for two different flight-control systems: a classic proportional-integral-derivative controller and neural dynamic programming, a neural network based controller. All designs are tested by the use of a sophisticated nonlinear validated model of the Apache helicopter.

关键词： Actuators Helicopter Controls Tandem Rotor Helicopters Control Systems McDonnell Douglas neural dynamic programming Proportional Integral Derivative neural Networks Attitude Control Angle of Attack

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Model-Free Distributed Reinforcement Learning State Estimation of a dynamical System Using Integral Value Functions

IEEE OPEN JOURNAL OF CONTROL SYSTEMS

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IEEE OPEN JOURNAL OF CONTROL SYSTEMS 2023年 2卷 70-78页

作者： Salamat, Babak Elsbacher, Gerhard Tonello, Andrea M. Belzner, Lenz TH Ingolstadt AImot Inst D-85049 Ingolstadt Germany Alpen Adria Univ Klagenfurt Inst Embedded Syst Klagenfurt Austria

One of the challenging problems in sensor network systems is to estimate and track the state of a target point mass with unknown dynamics. Recent improvements in deep learning (DL) show a renewed interest in applying DL techniques to state estimation problems. However, the process noise is absent which seems to indicate that the point-mass target must be non-maneuvering, as process noise is typically as significant as the measurement noise for tracking maneuvering targets. In this paper, we propose a continuous-time (CT) model-free or model-building distributed reinforcement learning estimator (DRLE) using an integral value function in sensor networks. The DRLE algorithm is capable of learning an optimal policy from a neural value function that aims to provide the estimation of a target point mass. The proposed estimator consists of two high pass consensus filters in terms of weighted measurements and inverse-covariance matrices and a critic reinforcement learning mechanism for each node in the network. The efficiency of the proposed DRLE is shown by a simulation experiment of a network of underactuated vertical takeoff and landing aircraft with strong input coupling. The experiment highlights two advantages of DRLE: i) it does not require the dynamic model to be known, and ii) it is an order of magnitude faster than the state-dependent Riccati equation (SDRE) baseline.

关键词： Mathematical models dynamical systems Aerodynamics Target tracking Reinforcement learning Noise measurement Riccati equations Aerospace consensus filters deep learning distributed filter dynamical system model neural dynamic programming sensor networks

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