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Chinese Automation Congress (CAC)

作者： Yu, Di Luo, Huafeng Beijing Informat Sci & Technol Univ Sch Automat Beijing Peoples R China Qingdao Univ Sci & Technol Coll Electromech Engn Qingdao Peoples R China

ISBN: (纸本)9781728113128

A new optimal distributed control scheme is developed for the containment control problem of nonlinear multi-agent graphical games. adaptive dynamic programming method is applied to obtain the approximate optimal control policy of each follower which make the followers converge and keep moving within the convex hull of leaders and simultaneously achieve Nash equilibrium. The performance index of each follower is defined based on local neighborhood information. Moreover, an model-free integral reinforcement learning algorithm is proposed to update the value function and the control policy without knowing the system dynamics so as to avoid to solve the coupled Hamilton-Jacobi-Bellman(HJB) equations, which is implemented by actor-critic structure and least-square method. Convergence analysis of the algorithm and asymptotical stability of the whole network are provided. Simulation results show the effectiveness of the proposed control scheme.

关键词： graphical games multi-agent networks integral reinforcement learning containment control

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A Single-NN Iterative adaptive dynamic programming Algorithm for Continuous-Time Nonlinear Zero-Sum Games 37

A Single-NN Iterative Adaptive Dynamic Programming Algorithm...

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37th Chinese Control Conference (CCC)

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

ISBN: (纸本)9789881563958

This paper establishes an approximate optimal critic learning algorithm based on single-network adaptive dynamic programming (ADP) aiming at solving for continuous-time 2-player zero-sum games(ZSG). However, the situation where the accurate dynamics is influenced by disturbance will occur from time to time. Because neural network(NN) is used in this paper, we have to face the approximation error, which will disturb the control. In order to surmount this problem, we use online data to calculate the weights of NN, and design robust controller to stabilize the disturbed nonlinear system. In other way, we used policy iteration and integral reinforcement learning to settle the Hamilton-Jacobi-Isaacs equation. And through the least-squares method, the NN weights are solved. Based on the theoretical analysis, this algorithm is a derivation from Gauss-Newton method, which can solve an optimization problem without disturbance. Thus it will converge to the optimal value. Because large quantities of online data are used, the process will accurately converge optimal control. Simulation results can verify that it's realizable to deal with disturbed nonlinear ZSG.

关键词： adaptive dynamic programming (ADP) zero-sum game (ZSG) single NN least-squares method

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adaptive reinforcement learning-Based Routing Protocol for Wireless Multihop Networks 14

Adaptive Reinforcement Learning-Based Routing Protocol for W...

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14th International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering, APEIE 2018

作者： Dugaev, Dmitrii A. Matveev, Ivan G. Siemens, Eduard Shuvalov, Viatcheslav P. Faculty of Electrical Mechanical and Industrial Engineering Anhalt University of Applied Sciences Koethen Germany Novosibirsk Russia

ISBN: (纸本)9781538670545

This paper presents a research on a topic of development of an adaptive packet routing scheme for wireless multihop networks, based on reinforcement learning optimization algorithm. A brief overview of classical approaches for data routing in multihop networks is provided, emphasizing main drawbacks of such algorithms, caused by ineffective hop count routing metric used in traditional multihop routing algorithms. Then, an approach based on reinforcement learning theory is presented, that has a potential to select more effective routes, relying on feedback information from neighboring nodes. An algorithm based on reinforcement learning optimization function is proposed, as well as additional functions are introduced for initial route weights distribution and dynamic route probability selection, depending from the current packet loss ratio (PLR) and receive signal strength indicator (RSSI) factors. The elaborated adaptive routing scheme then has been tested in real wireless multihop topology, where a programming implementation of the proposed algorithm - RLRP protocol, showed better routing performance characteristics in terms of PLR and RRT (Route Recovery Time), compared to a traditional improved proactive scheme of wireless multihop routing, implemented in widely used B.A.T.M.A.N. (Better Approach to Mobile Ad hoc Networking) protocol. © 2018 ieee.

关键词： Routing protocols

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Supplementary Frequency Control for Multi-machine Power System Based on adaptive dynamic programming 15th

Supplementary Frequency Control for Multi-machine Power Syst...

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15th International symposium on Neural Networks (ISNN)

作者： Shi, Zhan Wang, Zhanshan Luo, Yanhong Ye, Dan Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9783319925370;9783319925363

Power system is a nonlinear high-dimension complex system with dynamic uncertainties, how to carry out frequency regulation effectively in the event of disturbance is a difficult problem to be solved urgently. In this paper, a robust adaptive dynamic programming (robust ADP) based supplementary frequency controller for each generator is designed to solve multi-machine power system frequency regulation problem. The control policy iteration of each subsystem controller only uses their own state and input information for learning, without knowing the whole system dynamics. MATLAB and PSAT are used to establish the New England 10-machine 39-bus test system to verify the effective of the proposed control scheme. The simulation results show that the designed controller can effectively adapt to changes in the power system and make the system frequency recover to the reference value quickly.

关键词： Power system Frequency control Distributed control

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Model-Free Linear Discrete-Time System H_∞ Control Using Input-Output Data

Model-Free Linear Discrete-Time System H<sub>∞</sub> Contro...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Fan, Jialu Li, Zhen Jiang, Yi Chai, Tianyou Lewis, Frank L. Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Liaoning Peoples R China Univ Texas Arlington UTA Res Inst Arlington TX 76118 USA

ISBN: (纸本)9781538671740

In this paper, a data-driven output feedback H-infinity control algorithm for discrete-time linear systems with unknown dynamics has been proposed. At first, the standard model-based state feedback Hoo controller is formulated. Then, considering the state is not measurable and the system dynamic is unknown for some systems, we reformulate the value function of the Hoo control problem based on historical input and output data. Using the novel value function and adaptive dynamic programming approach, an online data-driven output feedback H-infinity control algorithm for linear discrete-time systems is proposed. Different from on-policy reinforcement learning based model-free control algorithms, the proposed algorithm can eliminate the influence of probing noise to guarantee unbiased solutions. A simulation example is employed to verify the effectiveness of the proposed control algorithm.

关键词： H-infinity Control reinforcement learning Off-policy Data-Driven

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learning-based Walking Assistance Control Strategy for a Lower Limb Exoskeleton with Hemiplegia Patients

Learning-based Walking Assistance Control Strategy for a Low...

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25th ieee/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Huang, Rui Peng, Zhinan Cheng, Hong Hu, Jiangping Qiu, Jing Zou, Chaobin Chen, Qiming Univ Elect Sci & Technol China Sch Automat Engn Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538680940

Lower exoskeleton has gained considerable interests in walking assistance applications for both paraplegia and hemiplegia patients. In walking assistance of hemiplegia patients, the exoskeleton should have the ability to control the affected leg to follow the unaffected leg's motion naturally. One critical issue of walking assistance for hemiplegia patients is how to adapt the controller of both lower limbs with different patients. This paper presents a novel learning-based walking assistance control strategy for lower exoskeleton with hemiplegia patients. In the proposed control strategy, we modeled the control system of lower exoskeleton with hemiplegia patient as a Leader-Follower Multi-Agent System (LF-MAS). In order to adapt different patients with different conditions, reinforcement learning framework is utilized to adapt controllers online. In reinforcement learning framework with LF-MAS, we employed a Policy Iteration adaptive dynamic programming (PI-ADP) algorithm, which aims to achieve better tracking control performance for lower exoskeleton with hemiplegia patient. We demonstrate the efficiency of proposed learning-based walking assistance control strategy in an exoskeleton system with healthy subjects who simulate hemiplegia patients. Experimental results indicate that the proposed control strategy can adapt different pilots with good tracking performance.

关键词： Walking Assistance Strategy Leader-Follower Multi-Agent System reinforcement learning Lower Exoskeleton Hemiplegia

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Gr-GDHP: A New Architecture for Globalized Dual Heuristic dynamic programming

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ieee TRANSACTIONS ON CYBERNETICS 2017年第10期47卷 3318-3330页

作者： Zhong, Xiangnan Ni, Zhen He, Haibo Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA South Dakota State Univ Dept Elect Engn & Comp Sci Brooking SD 57007 USA

Goal representation globalized dual heuristic dynamic programming (Gr-GDHP) method is proposed in this paper. A goal neural network is integrated into the traditional GDHP method providing an internal reinforcement signal and its derivatives to help the control and learning process. From the proposed architecture, it is shown that the obtained internal reinforcement signal and its derivatives can be able to adjust themselves online over time rather than a fixed or predefined function in literature. Furthermore, the obtained derivatives can directly contribute to the objective function of the critic network, whose learning process is thus simplified. Numerical simulation studies are applied to show the performance of the proposed Gr-GDHP method and compare the results with other existing adaptive dynamic programming designs. We also investigate this method on a ball-and-beam balancing system. The statistical simulation results are presented for both the Gr-GDHP and the GDHP methods to demonstrate the improved learning and controlling performance.

关键词： adaptive dynamic programming (ADP) globalized dual heuristic dynamic programming (GDHP) goal representation neural network reinforcement learning (RL)

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Optimal Tracking Control of Heterogeneous Multi-agent Systems with Switching Topology via Actor-Critic Neural Networks 37

Optimal Tracking Control of Heterogeneous Multi-agent System...

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37th Chinese Control Conference (CCC)

作者： Peng, Zhinan Hu, Jiangping Ghosh, Bijoy K. Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA

ISBN: (纸本)9789881563958

In this paper, an optimal tracking control problem is solved for high-order heterogeneous multi-agent systems with time-varying interaction networks within the framework of reinforcement learning. First, the optimal tracking control problem is formulated as a leader-follower multi-agent system. Second, a policy iteration based adaptive dynamic programming (ADP) algorithm is proposed to compute the performance index and the control law. Furthermore, the convergence to the optimal solutions is analyzed for the proposed algorithm. Third, an actor-critic neural network is applied to approximate the iterative performance index function and the control law, which implement the policy iteration algorithm online without using the knowledge of the system dynamics. Finally, some simulation results are presented to demonstrate the proposed optimal tracking control strategy.

关键词： Optimal tracking control multi-agent systems adaptive dynamic programming actor-critic neural network

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Self-learning Optimal Control for Uncertain Nonlinear Systems via Online Updated Cost Function 33

Self-Learning Optimal Control for Uncertain Nonlinear System...

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33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Zhao, Bo Shi, Guang Li, Chao Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9781538672556

This paper presents an online updated cost function based self-learning optimal control scheme for uncertain nonlinear systems. By establishing an online updated cost function with the help of disturbance observer, the Hamilton-Jacobi-Bellman equation is solved by constructing a critic neural network, whose weight vector is tuned by self-learning algorithm. And then, the optimal control scheme is derived indirectly. Based on Lyapunov stability analysis, the closed-loop system with the proposed scheme is guaranteed to be stable. The simulation results show the effectiveness of the developed self-learning optimal control scheme. The cost function reflects the system uncertainties in real time, which implies that this method relaxes the assumptions on available upper-bounds and matching condition for system dynamics in compared with many existing methods.

关键词： adaptive dynamic programming Uncertain nonlinear systems Optimal control Disturbance observer Neural networks reinforcement learning

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2018 ieee International Conference on Simulation, Modeling, and programming for Autonomous Robots, SIMPAR 2018

2018 IEEE International Conference on Simulation, Modeling, ...

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2018 ieee International Conference on Simulation, Modeling, and programming for Autonomous Robots, SIMPAR 2018

ISBN: (纸本)9781538659748

The proceedings contain 26 papers. The topics discussed include: comparison of control methods: learning robotics manipulation with contact dynamics;DoShiCo challenge: domain shift in control prediction;surface/subsurface mapping with an integrated rover-GPR system, a simulation approach;reactive path planning for collaborative robot using configuration space skeletonization;reinforcement learning for non-prehensile manipulation: transfer from simulation to physical system;learning from outside the viability kernel: why we should build robots that can fail with grace;analysis of a simple model for post-impact dynamics active compliance in humanoids falls with nonlinear optimization;nonlinear model predictive control with adaptive time-mesh refinement;dynamic programming accelerated evolutionary planning for constrained robotic missions;increased visibility sampling for probabilistic roadmaps;an open-source architecture for simulation, execution and analysis of real-time robotics systems;the control toolbox - an open-source C++ library for robotics, optimal and model predictive control;simulating differential games with improved fidelity to better inform cooperative & adversarial two vehicle UAV flight;the sleepwalker framework: verification and validation of autonomous vehicles by mixed reality lidar stimulation;dynamics simulation for an upper-limb human-exoskeleton assistance system in a latent-space controlled tool manipulation task;CU-brick cable-driven robot for automated construction of complex brick structures: from simulation to hardware realisation;and ROS-health: an open-source framework for neurorobotics.

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