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第37届中国控制会议

作者： Hao Fu Xin Chen Wei Wang Jinbin Li Yaodong Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems State Grid Hubei Electric Power Research Institute

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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Data-Driven adaptive dynamic programming for H2/H∞ Control of Unknown Nonlinear System

Data-Driven Adaptive Dynamic Programming for H2/H∞ Control ...

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第37届中国控制会议

作者： Jun Pu Qingliang Ma Fan Gu Yuandong Li Xi’an Research Institute of High-Tech

In order to solve the practical problems that exact analytic solution of the coupled Hamilton-Jacobi-Isaacs（HJI）equations arising from the mixed 2 H/Hfcontrol of nonlinear systems and the nonlinear system dynamics models is not generally *** to the model-based iterative algorithm,a data-driven approximate dynamic programming algorithm for solving mixed 2 H/Hf control problems is derived by adding known noise into control strategy and disturbance *** Nash equilibrium strategy of nonlinear system is obtained online by using input-output data of nonlinear system,which does not depend on the specific model information of the *** critic neural networks and two action neural networks are used to synchronously update two value functions,control strategy and disturbance strategy *** unknown parameters of neural network are estimated by generalized least *** simulation results verify the feasibility of the algorithm.

关键词： adaptive dynamic programming data-driven 2H/Hf control Optimum control Neural network

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Seasonal Scheduling of Office Electricity Use via adaptive dynamic programming

Seasonal Scheduling of Office Electricity Use via Adaptive D...

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第37届中国控制会议

作者： Guang Shi Bo Zhao Chao Li Xin Jin Derong Liu National Computer Network Emergency Response Technical Team/Coordination Center of China The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation Guangdong University of Technology

In this paper, an adaptive dynamic programming（ADP） based optimization method is proposed to schedule the electricity use of an office, where a battery is considered as the control variable, while solar and wind energies are included as additional energy supplies besides the grid. The electricity demand of an office generally contains socket, lighting and airconditioning demands. Based on the periodic models of electricity price, electricity demand, and solar and wind energies, the optimal control strategies of the battery are determined by the proposed ADP based optimization method, so that the electricity cost from the grid can be saved. Simulation analysis demonstrates that the proposed method can achieve optimal real-time scheduling of office electricity use in different seasons of a year.

关键词： Smart grid electricity scheduling optimal control adaptive dynamic programming

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Optimal Sliding Mode Tracking Control for Reconfigurable Manipulators Based on adaptive dynamic programming

Optimal Sliding Mode Tracking Control for Reconfigurable Man...

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Chinese Automation Congress

作者： Bing Ma Ruiyang Li Bo Dong Miao Yu Fan Zhou Yuanchun Li Department of Control Science and Engineering Changchun University of technology Changchun China CRRC Changchun Railway Vehicles CO. LTD. Changchun China

This paper presents a novel optimal sliding mode tracking control method for reconfigurable manipulators based on the policy iteration (PI) and adaptive dynamic programming (ADP). The designed sliding mode tracking control can suppress the tracking error caused by reconfiguration of the manipulators. Based on ADP and PI algorithm, the Hamiltonian-Jacobi-Bellman (HJB) equation can be solved by constructing a critic neural network and then the approximated optimal sliding mode control policy can be derived directly. Based on the Lyapunov stability theorem, the closed-loop robotic system is proved to be asymptotic stability. Finally, simulations are provided to demonstrate the effectiveness of the proposed method.

关键词： Reconfigurable manipulators adaptive dynamic programming Critic neural network Sliding mode control Optimal control

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adaptive dynamic programming for Stochastic Systems With State and Control Dependent Noise

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2016年第12期61卷 4170-4175页

作者： Bian, Tao Jiang, Yu Jiang, Zhong-Ping NYU Tandon Sch Engn Dept Elect & Comp Engn Control & Networks Lab Brooklyn NY 11201 USA MathWorks Inc Natick MA 01760 USA

In this technical note, the adaptive optimal control problem is investigated for a class of continuous-time stochastic systems subject to multiplicative noise. A novel non-model-based optimal control design methodology is employed to iteratively update the control policy on-line by using directly the data of the system state and input. Both adaptive dynamic programming (ADP) and robust ADP algorithms are developed, along with rigorous stability and convergence analysis. The effectiveness of the obtained methods is illustrated by an example arising from biological sensorimotor control.

关键词： adaptive dynamic programming adaptive optimal control stochastic systems

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A Nearly Optimal Control Approach for Uncertainty Input-Delay Systems Based on adaptive dynamic programming

A Nearly Optimal Control Approach for Uncertainty Input-Dela...

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International Automatic Control Conference (CACS)

作者： Lin, Yu-Chen Chen, Hsin-Chang Peng, Chih-Kai Feng Chia Univ Dept Automat Control Engn Taichung 40724 Taiwan

ISBN: (纸本)9781538639009

This paper concerned with a nearly optimal control approach based on adaptive dynamic programming technique to solve robust control problem of the neutral type time-delay systems, taking parameter uncertainties and input delay into account. Based on the neural network (NN)-based adaptive dynamic programming and Lyapunov-Razumikhin theorems, the robust control design problem can be equivalently transformed into a nearly optimal control problem, and the amount of matched uncertainties are indirectly reflected in the performance index. A nearly optimal control is designed to approximate the costate function of the Hamilton-Jacobi-Isaaca (HJI) equation by NN-based adaptive dynamic programming scheme By algebraic inequalities and appropriate uncertainty descriptions, sufficient conditions are derived under which not only the uncertain input-delay dynamical systems can achieve asymptotic stability, but also acquire the guaranteed level of performance for regulation. Simulation example is performed to demonstrate the effectiveness of the proposed approaches.

关键词： Nearly optimal control adaptive dynamic programming neural network Lyapunov-Razumikhin asymptotic stability

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Zero-Sum Differential Games for Nonlinear Systems Using adaptive dynamic programming with Input Constraint 36

Zero-Sum Differential Games for Nonlinear Systems Using Adap...

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第36届中国控制会议

作者： Jingliang Sun Chunsheng Liu College of Automation Engineering Nanjing University of Aeronautics and Astronautics

ISBN: (纸本)9781538629185

In this paper, the zero-sum differential game problem for a class of nonlinear system with input constraints is investigated via adaptive dynamic programming(ADP). A suitable non-quadratic functional is utilized to embed the control constraints into the differential game problem. Then, the Nash equilibrium solution is found by solving the constrained Hamilton-Jacobi-Isaacs(HJI) equation. The single critic network is constructed to approximate the solution of associated HJI equation online. A robustifying control term is added to the controller to eliminate the effect of residual error, leading to the asymptotically stability of the closed-loop system. Simulation results verify the effectiveness of proposed method by using a simple nonlinear system.

关键词： differential game adaptive dynamic programming asymptotically stability input constraints

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Evolutionary adaptive dynamic programming Algorithm for Converter Gas Scheduling of Steel Industry 6

Evolutionary Adaptive Dynamic Programming Algorithm for Conv...

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6th International Symposium on Advanced Control of Industrial Processes (AdCONIP)

作者： Wang, Tianyu Wang, Linqing Zhao, Jun Wang, Wei Liu, Ying Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China

ISBN: (纸本)9781509043972

It is significant to perform an effective scheduling of byproduct gas system in steel industry for reducing cost and protecting environment. The existing studies largely focused on extracting specific knowledge from human experience or directly optimizing the scheduling performance, which failed to provide a dynamic optimization process for making the scheduling scheme updated online. In this study, an action-dependent heuristic dynamic programming (ADHDP) framework is proposed for the Linz Donawitz converter gas (LDG) scheduling, in which the scheduling amount is calculated based on the gas system states by utilizing a Tagaki-Sugeno-Kang (TSK) fuzzy model, while a utility function is introduced in the critic network considering the time delay of the gas system to evaluate the scheduling performance over time. For achieving online learning process, the concept of a modified evolutionary algorithm is combined with the ADHDP to obtain the near-optimal scheduling policy at each time instance. To demonstrate the performance of the proposed method, the practical data coming from the energy center of a steel plant are employed. The results show that the proposed method can supply the human operators with effective solution for secure and economically justified optimization of the LDG system.

关键词： steel industry energy scheduling adaptive dynamic programming reinforcement learning evolutionary computing

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Robust Control of Uncertain Nonlinear Systems Based on adaptive dynamic programming 24th

Robust Control of Uncertain Nonlinear Systems Based on Adapt...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Na, Jing Zhao, Jun Gao, Guanbin Wang, Ding Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming Yunnan Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9783319700908;9783319700892

In this paper, we propose a new approach to address robust control problem of nonlinear systems with uncertainties based on an adaptive dynamic programming (ADP) algorithm. After reformulating the robust control problem as an optimal control problem, we propose a modified ADP method to solve the derived Hamilton-Jacobi-Bellman (HJB) equation, where the optimal cost function is approximated by online training a critic neural network (NN). Then the approximated optimal control action can be derived to guarantee the stability of the controlled system with uncertainties. The closed-loop system stability and convergence have been proved. A simulation example is provided to illustrate the effectiveness of the method.

关键词： Robust control Optimal control Nonlinear uncertain systems adaptive dynamic programming

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Hamiltonian-Driven adaptive dynamic programming Based on Extreme Learning Machine 14th

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14th International Symposium on Neural Networks (ISNN)

作者： Yang, Yongliang Wunsch, Donald Guo, Zhishan Yin, Yixin Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

ISBN: (纸本)9783319590721;9783319590714

In this paper, a novel frame work of reinforcement learning for continuous time dynamical system is presented based on the Hamiltonian functional and extreme learning machine. The idea of solution search in the optimization is introduced to find the optimal control policy in the optimal control problem. The optimal control search consists of three steps: evaluation, comparison and improvement of arbitrary admissible policy. The Hamiltonian functional plays an important role in the above framework, under which only one critic is required in the adaptive critic structure. The critic network is implemented by the extreme learning machine. Finally, simulation study is conducted to verify the effectiveness of the presented algorithm.

关键词： Reinforcement learning adaptive dynamic programming Extreme learning machine Hamiltonian functional Optimization

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