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Manifold Regularized Reinforcement Learning

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第4期29卷 932-943页

作者： Li, Hongliang Liu, Derong Wang, Ding Tencent Inc AI Platform Dept Shenzhen 518057 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper introduces a novel manifold regularized reinforcement learning scheme for continuous Markov decision processes. Smooth feature representations for value function approximation can be automatically learned using the unsupervised manifold regularization method. The learned features are data-driven, and can be adapted to the geometry of the state space. Furthermore, the scheme provides a direct basis representation extension for novel samples during policy learning and control. The performance of the proposed scheme is evaluated on two benchmark control tasks, i.e., the inverted pendulum and the energy storage problem. Simulation results illustrate the concepts of the proposed scheme and show that it can obtain excellent performance.

关键词： adaptive dynamic programming approximate dynamic programming approximate policy iteration (API) manifold regularization reinforcement learning (RL)

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A data-driven optimal control approach for solution purification process

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JOURNAL OF PROCESS CONTROL 2018年 68卷 171-185页

作者： Sun, Bei He, Mingfang Wang, Yalin Gui, Weihua Yang, Chunhua Zhu, Quanmin Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Aalto Univ Sch Chem Engn Dept Chem & Met Engn Espoo 02150 Finland Cent South Univ Forestry & Technol Sch Comp & Informat Engn Changsha 410004 Hunan Peoples R China Univ West England Dept Engn Design & Math Coldharbour Lane Bristol BS16 1QY Avon England

Solution purification holds a critical position in hydrometallurgy. With its inherent complexity and the mixed raw material supply, solution purification process exhibits various working conditions, and has nonlinear, time-varying dynamics. At current stage, a comprehensive and precise model of a solution purification process is still costly to obtain. More specifically, the model structure could be derived by applying physical and chemical principles, while the accurate model parameters cannot be obtained under certain working conditions due to reasons like insufficient data samples. This, in turn, introduces obstacles in achieving the optimal operation. In order to circumvent the modeling difficulty, this paper proposes a 'Process State Space' descriptive system to re-describe the optimal control problem of solution purification process, accordingly establishes a two-layer receding horizon framework for developing a data-driven optimal control of solution purification process. In the optimal control scheme, on the 'optimization' layer, by utilizing the 'multiple-reactors' characteristic of solution purification process, a 'gradient' optimization strategy is proposed to transform the dosage minimization problem into obtaining the optimal variation gradient of the outlet impurity concentrations along the reactors. On the 'control' layer, a model-free input constrained adaptive dynamic programming algorithm is devised and applied to calculate the optimal dosages for each reactor by learning from the real-time production data. Case studies are performed to illustrate the effectiveness and efficiency of the proposed approach. The results and problems need future research are also discussed. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Solution purification process Receding horizon control Data-driven control adaptive dynamic programming Process state space

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Neural dynamic programming for Event-Based Nonlinear adaptive Robust Stabilization 1

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23rd International Conference on Neural Information Processing (ICONIP)

作者： Wang, Ding Ma, Hongwen Liu, Derong Wang, Huidong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Shandong Univ Finance & Econ Sch Management Sci & Engn Jinan 250014 Peoples R China

ISBN: (数字)9783319466873

ISBN: (纸本)9783319466873;9783319466866

In this paper, we develop an event-based adaptive robust stabilization method for continuous-time nonlinear systems with uncertain terms via a self-learning technique called neural dynamic programming. Through system transformation, it is proven that the robustness of the uncertain system can be achieved by designing an event-triggered optimal controller with respect to the nominal system under a suitable triggering condition. Then, the idea of neural dynamic programming is adopted to perform the main controller design task by building and training a critic network. Finally, the effectiveness of the present adaptive robust control strategy is illustrated via a simulation example.

关键词： adaptive dynamic programming adaptive robust stabilization Event-based control Neural dynamic programming Neural network

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Self-learning robust optimal control for continuous-time nonlinear systems with mismatched disturbances

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NEURAL NETWORKS 2018年 99卷 19-30页

作者： Yang, Xiong He, Haibo Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

This paper presents a novel adaptive dynamic programming(ADP)-based self-learning robust optimal control scheme for input-affine continuous-time nonlinear systems with mismatched disturbances. First, the stabilizing feedback controller for original nonlinear systems is designed by modifying the optimal control law of the auxiliary system. It is also demonstrated that this feedback controller can optimize a specified value function. Then, within the framework of ADP, a single critic network is constructed to solve the Hamilton-Jacobi-Bellman equation associated with the auxiliary system optimal control law. To update the critic network weights, an indicator function and a concurrent learning technique are employed. By using the proposed update law for the critic network, the restrictive conditions including the initial admissible control and the persistence of excitation condition are relaxed. Moreover, the stability of the closed-loop auxiliary system is guaranteed in the sense that all the signals are uniformly ultimately bounded. Finally, the applicability of the developed control strategy is illustrated through simulations for an unstable nonlinear plant and a power system. (c) 2017 Elsevier Ltd. All rights reserved.

关键词： adaptive dynamic programming Neural network Reinforcement learning Robust optimal control Mismatched disturbance

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adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第6期29卷 2099-2111页

作者： Luo, Biao Liu, Derong Wu, Huai-Ning Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Beihang Univ Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China

Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition Q((0))(x, a) >= 0. To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.

关键词： adaptive control adaptive dynamic programming constraints critic-only data-based optimal control Q-learning

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Observer-based fuzzy adaptive optimal stabilization control for completely unknown nonlinear interconnected systems

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NEUROCOMPUTING 2018年 313卷 415-425页

作者： Wang, Tiechao Tong, Shaocheng Liaoning Univ Technol Coll Elect Engn Jinzhou 121001 Liaoning Peoples R China

Fuzzy adaptive optimal bounded control problems are first investigated for a class of nonlinear continuous-time interconnected systems whose system internal dynamics and unmatched interconnections are completely unknown, when there exist unavailable states in the subsystems of the interconnected systems. The system states and the interconnection terms of the interconnected system are approximated by using a fuzzy state observer. The decentralized optimal controllers and observer-critic structure are designed according to adaptive dynamic programming and enforcement learning technology. The presented control methods can ensure that the system states and parameter estimation errors of the interconnected systems are ultimately uniformly bounded. A simulation example validates the effectiveness of the presented scheme. (C) 2018 Published by Elsevier B.V.

关键词： Fuzzy logic systems State observer adaptive dynamic programming Unknown nonlinear systems Bounded control

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adaptive critic designs for optimal control of uncertain nonlinear systems with unmatched interconnections

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NEURAL NETWORKS 2018年 105卷 142-153页

作者： Yang, Xiong He, Haibo Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

In this paper, we develop a novel optimal control strategy for a class of uncertain nonlinear systems with unmatched interconnections. To begin with, we present a stabilizing feedback controller for the interconnected nonlinear systems by modifying an array of optimal control laws of auxiliary subsystems. We also prove that this feedback controller ensures a specified cost function to achieve optimality. Then, under the framework of adaptive critic designs, we use critic networks to solve the Hamilton-Jacobi-Bellman equations associated with auxiliary subsystem optimal control laws. The critic network weights are tuned through the gradient descent method combined with an additional stabilizing term. By using the newly established weight tuning rules, we no longer need the initial admissible control condition. In addition, we demonstrate that all signals in the closed-loop auxiliary subsystems are stable in the sense of uniform ultimate boundedness by using classic Lyapunov techniques. Finally, we provide an interconnected nonlinear plant to validate the present control scheme. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： adaptive critic designs adaptive dynamic programming Neural networks Optimal control Unmatched interconnection

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Incremental Model-Based Global Dual Heuristic programming for Flight Control ⁎

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IFAC-PapersOnLine 2019年第29期52卷 7-12页

作者： Bo Sun Erik-Jan van Kampen Department of Control and Operations Delft University of Technology Delft 2629HS The Netherlands

This paper proposes a novel adaptive dynamic programming method, called Incremental model-based Global Dual Heuristic programming, to generate a self-learning adaptive controller, in the absence of sufficient prior knowledge of system dynamics. An incremental technique is employed for online model identification, instead of the artificial neural networks commonly used in conventional Global Dual Heuristic programming. The incremental model has the capability of tackling nonlinearity and uncertainty of the plant, but can also guarantee high precision of online identification without the requirement of offline training. On the basis of the identified model, two neural networks are adopted to facilitate the implementation of the self-learning controller, by approximating the cost-to-go and its derivatives and the control policy, respectively. Both methods are applied to a tracking control problem of a nonlinear aerospace system and the results show that the proposed method outperforms conventional Global Dual Heuristic programming in online learning speed, tracking precision and robustness to variation of initial system states and network weights.

关键词： adaptive dynamic programming adaptive control incremental technique global dual heuristic programming artificial neural network

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Data-driven Shared Steering Control Design for Lane Keeping

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IFAC-PapersOnLine 2018年第13期51卷 155-160页

作者： Huang, Mengzhe Gao, Weinan Wang, Yebin Jiang, Zhong-Ping New York University BrooklynNY11201 United States Georgia Southern University StatesboroGA30460 United States Mitsubishi Electric Research Laboratories CambridgeMA02139 United States

In this paper, we propose a shared steering control strategy for semi-autonomous vehicles by taking into account the interaction between the driver and his/her vehicle. The copilot controller cooperates with the driver and help maintain the vehicle in the central position of a lane. Taking advantage of the small-gain theory, the design procedure does not rely on the perfect knowledge of the model and states for the driver. An adaptive dynamic programming method is introduced to develop a shared controller in real-time using online measurable input-output data from vehicle sensors. The efficacy of the proposed shared steering controller is demonstrated by computer-based simulations. © 2018

关键词： dynamic programming Automobile steering equipment Controllers Design Remotely operated vehicles Steering adaptive dynamic programming Computer based simulation Cooperative driving Human in the loop Semi autonomous vehicles Small gain theory Steering control Steering controllers

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Data-Driven Robust Approximate Optimal Tracking Control for Unknown General Nonlinear Systems Using adaptive dynamic programming Method

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2011年第12期22卷 2226-2236页

作者： Zhang, Huaguang Cui, Lili Zhang, Xin Luo, Yanhong Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China

In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

关键词： adaptive dynamic programming data-driven model neural networks optimal tracking control robust control

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