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37th Chinese Control Conference (CCC)

作者： Li, Lu Chen, Xin Fu, Hao China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China

ISBN: (纸本)9789881563958

This paper focuses on an accelerating method for partitioning the loops in the structure of the adaptive dynamic programming (ADP). ADP contains critic-actor structure which involves the iterations of the value function. When the system needs to be stable, the value function generally needs to iterate thousands of times, the high computation burden which hinders the iterations will be generated. In order to reduce the computation burden, we introduce a hyperparallelepiped based loop partitioning (H-LP) method which splits the iterations of the value function and reduces the communication traffic calculated by the data footprint. The experiment results show that the computation performance will be enhanced when the H-LP method is introduced. The proposed method has an important practical significance.

关键词： Hyperparallelepiped Loop Partitioning adaptive dynamic programming

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Predictive Cruise Control of Connected and Autonomous Vehicles : An adaptive dynamic programming Approach

Predictive Cruise Control of Connected and Autonomous Vehicl...

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IEEE SoutheastCon Conference

作者： Odekunle, Adedapo Gao, Weinan Anayor, Chikamso Wang, Xi Chen, Yunfeng Georgia Southern Univ Dept Elect & Comp Engn Allen E Paulson Coll Engn & Comp Statesboro GA 30460 USA Georgia Southern Univ Dept Civil Engn & Construct Management Allen E Paulson Coll Engn & Comp Statesboro GA 30460 USA

ISBN: (纸本)9781538661338

This paper proposes a novel predictive cruise control method for a platoon of connected and autonomous vehicles. The main objective is to minimize idle time of vehicular platoon using information from the traffic lights. First, the reference velocity is determined for each vehicle in the platoon. Second, a data-driven learning strategy, named adaptive dynamic programming (ADP), is employed to develop an optimal state-feedback controller without any prior knowledge of the platooning system dynamics. This resultant controller regulates the headway, velocity and acceleration of each vehicle to accommodate both safety and trip time reduction goals. A numerical simulation is demonstrated to ascertain the effectiveness of the proposed approach.

关键词： Vehicular platoon data-driven learning strategy adaptive dynamic programming

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Optimal Consensus Control of Unknown Nonlinear Multi-Agent Systems Using adaptive dynamic programming via MRAC 37

Optimal Consensus Control of Unknown Nonlinear Multi-Agent S...

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37th Chinese Control Conference (CCC)

作者： Fu, Hao Chen, Xin Wang, Wei Li, Jinbin Zhang, Yaodong China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China State Grid Hubei Elect Power Res Inst Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9789881563958

This paper introduces an optimal consensus control scheme for nonlinear multi-agent systems with completely unknown dynamics. In general, it is difficult to solve the coupled Hamilton-Jacobi-Bellman (HJB) equations, which the optimal consensus control relies on in multi-agent systems, especially unknown nonlinear systems. For the purpose of solving the problem, we propose an optimal consensus control approach based on the model reference adaptive control (MRAC) and adaptive dynamic programming (ADP). Using the structure of the diagonal recurrent neural network, the identifier and controller are devised to achieve MRAC for every plant of the unknown nonlinear systems, i.e. the reference model serves as a dynamic model of each individual agent. Then, according to reference models of distributed agents, an adaptive dynamic programming (ADP) is introduced to approximate the solution of the coupled HJB equations.

关键词： Multi-agent systems optimal consensus model reference adaptive control adaptive dynamic programming

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Optimal control of a rougher flotation cell using adaptive dynamic programming

Optimal control of a rougher flotation cell using adaptive d...

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5th IFAC Workshop on Mining, Mineral and Metal Processing (MMM)

作者： Sun, B. le Roux, J. D. Jamsa-Jounela, S. L. Craig, I. K. Aalto Univ Sch Chem Engn Espoo Finland Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa Cent S Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China

The operation of a rougher flotation cell affects the performance of mineral processing plant. The dynamics of a rougher flotation cell varies due to the diversity of the feeding conditions. In order to eliminate the dependence of optimal control on a precise model, this paper studies the optimal control of a flotation cell using adaptive dynamic programming which can learn from the operation data and iteratively improve the controller. A classic flotation model is adopted to simulate a rougher flotation cell. The simulation results indicate the effectiveness of the controller. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： froth flotation process modelling optimal control adaptive dynamic programming

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adaptive dynamic programming for Human Postural Balance Control 24th

Adaptive Dynamic Programming for Human Postural Balance Cont...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Mauro, Eric Bian, Tao Jiang, Zhong-Ping NYU Dept Elect & Comp Engn Tandon Sch Engn 5 Metrotech Ctr Brooklyn NY 11201 USA

ISBN: (纸本)9783319700939;9783319700922

This paper provides a basis for studying human postural balance control about upright stance using adaptive dynamic programming (ADP) theory. Previous models of human sensorimotor control rely on a priori knowledge of system dynamics. Here, we provide an alternative framework based on the ADP theory. The main advantage of this new framework is that the system model is no longer required, and an adaptive optimal controller is obtained directly from input and state data. We apply this theory to simulate human balance behavior, and the obtained results are consistent with the experiment data presented in the past literature.

关键词： Optimal control Motor learning adaptive dynamic programming

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adaptive dynamic programming for Direct Current Servo Motor 24th

Adaptive Dynamic Programming for Direct Current Servo Motor

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24th International Conference on Neural Information Processing (ICONIP)

作者： Zhu, Liao Song, Ruizhuo Xie, Yulong Li, Junsong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9783319700878;9783319700861

In this paper, a control method for continuous time Direct Current (DC) servo motor, is presented based on adaptive dynamic programming. The core of this paper is the application of adaptive dynamic programming (ADP) to control the DC servo motor system. The program includes three main steps: (i) The mathematical model of DC servo motor system is established, and the feasibility of solving the problem with ADP is analyzed;(ii) On the basis of introducing the theory of ADP, we propose a solution to the problem;(iii) The simulation of the DC servo motor system is carried out. The contribution of this paper is that ADP, which is one of the most important methods in the field of optimal control, is used to solve the traditional problem. Finally, simulation study is conducted to verify the effectiveness of the presented algorithm.

关键词： Optimization adaptive dynamic programming Policy iteration

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

Adaptive Dynamic Programming Based Decentralized Tracking Co...

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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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Optimization of electricity consumption in office buildings based on adaptive dynamic programming

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SOFT COMPUTING 2017年第21期21卷 6369-6379页

作者： Shi, Guang Wei, Qinglai Liu, Derong 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

In this paper, an optimization method based on adaptive dynamic programming is developed to improve the electricity consumption of rooms in office buildings through optimal battery management. Rooms in office buildings are generally divided into office rooms, computer rooms, storage rooms, meeting rooms, etc., and each category of rooms have different characteristics of electricity consumption, which is divided into electricity consumption from sockets, lights and air-conditioners in this paper. The developed method based on action-dependent heuristic dynamic programming is explained in detail, and different optimization strategies of electricity consumption in different categories of rooms are proposed in accordance with the developed method. Finally, a detailed case study on an office building is given to demonstrate the practical effect of the developed method.

关键词： Office buildings Electricity consumption optimization Battery management Optimal control adaptive dynamic programming Neural networks

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Bounded robust control design for uncertain nonlinear systems using single-network adaptive dynamic programming

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NEUROCOMPUTING 2017年 266卷 128-140页

作者： Huang, Yuzhu Wang, Ding Liu, Derong Natl Res Ctr Gas Turbine & IGCC Technol Syst Control Res Sect Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

This paper is an effort towards developing an optimal learning algorithm to design the bounded robust controller for uncertain nonlinear systems with control constraints using single-network adaptive dynamic programming (ADP). First, the bounded robust control problem is transformed into an optimal control problem of the nominal system by a modified cost function with nonquadratic utility, which is used not only to account for all possible uncertainties, but also to deal with the control constraints. Then based on single-network ADP, an optimal learning algorithm is proposed for the nominal system by a single critic network to approximate the solution of Hamilton-Jacobi-Bellman (HJB) equation. An additional adjusting term is employed to stabilize the system and relax the requirement for an initial stabilizing control. Besides, uniform ultimate boundedness of the closed-loop system is guaranteed by Lyapunov's direct method during the learning process. Moreover, the equivalence of the approximate optimal solution of optimal control problem and the solution of bounded robust control problem is also shown. Finally, four simulation examples are provided to demonstrate the effectiveness of the proposed approach. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Neural networks Optimal control adaptive dynamic programming Bounded robust control Uncertain nonlinear systems

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Robust adaptive dynamic programming based online tracking control algorithm for real wheeled mobile robot with omni-directional vision system

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2017年第6期39卷 832-847页

作者： Nguyen Tan Luy Ind Univ Ho Chi Minh City Ho Chi Minh City Ho Chi Minh Vietnam

This paper proposes a new method to design an online robust adaptive dynamic programming algorithm (RADPA) for a wheeled mobile robot which is equipped with an omni-directional vision system. To integrate kinematic and dynamic controllers into the unique controller, we transform the strict feedback system dynamics into tracking error dynamics. Then, we propose a control scheme which uses only one neural network rather than three proposed in the actor-critic-based control schemes for the two-player zero-sum game problem. A neural network weight update law is designed for approximating the solution of the Hamilton-Jacobi-Isaacs equation without knowing knowledge of internal system dynamics. To implement the scheme, we propose the online RADPA, in which control and disturbance laws are updated simultaneously in an iterative loop. The convergence and stability of the online RADPA are proven by Lyapunov techniques. Simulations and experiments on a wheeled mobile robot testbed are carried out to verify the effectiveness of the proposed algorithm.

关键词： adaptive dynamic programming Hamilton-Jacobi-Isaacs equation neural network wheeled mobile robot omni-directional vision

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