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Policy iteration based Q-learning for linear nonzero-sum quadratic differential games

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Science China(Information Sciences) 2019年第5期62卷 195-213页

作者： Xinxing LI zhihong peng Li LIANG Wenzhong ZHA School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System Information Science Academy China Electronics Technology Group Corporation

In this paper, a policy iteration-based Q-learning algorithm is proposed to solve infinite horizon linear nonzero-sum quadratic differential games with completely unknown dynamics. The Q-learning algorithm, which employs off-policy reinforcement learning(RL), can learn the Nash equilibrium and the corresponding value functions online, using the data sets generated by behavior policies. First, we prove equivalence between the proposed off-policy Q-learning algorithm and an offline PI algorithm by selecting specific initially admissible polices that can be learned online. Then, the convergence of the off-policy Qlearning algorithm is proved under a mild rank condition that can be easily met by injecting appropriate probing noises into behavior policies. The generated data sets can be repeatedly used during the learning process, which is computationally effective. The simulation results demonstrate the effectiveness of the proposed Q-learning algorithm.

关键词： adaptive dynamic programming ADP Q-learning reinforcement learning RL linear nonzerosum quadratic differential games policy iteration PI off-policy

A memetic algorithm for path planning of curvature-constrained UAVs performing surveillance of multiple ground targets

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Chinese Journal of Aeronautics 2014年第3期27卷 622-633页

作者： Zhang Xing Chen Jie Xin Bin peng zhihong School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

The problem of generating optimal paths for curvature-constrained unmanned aerial vehicles （UAVs） performing surveillance of multiple ground targets is addressed in this paper. UAVs are modeled as Dubins vehicles so that the constraints of UAVs＇ minimal turning radius can be taken into account. In view of the effective surveillance range of the sensors equipped on UAVs, the problem is formulated as a Dubins traveling salesman problem with neighborhood （DTSPN）. Considering its prohibitively high computational complexity, the Dubins paths in the sense of terminal heading relaxation are introduced to simplify the calculation of the Dubins distance, and a boundary-based encoding scheme is proposed to determine the visiting point of every target neighborhood. Then, an evolutionary algorithm is used to derive the optimal Dubins tour. To further enhance the quality of the solutions, a local search strategy based on approximate gradient is employed to improve the visiting points of target neighborhoods. Finally, by a minor modification to the individual encoding, the algorithm is easily extended to deal with other two more sophisticated DTSPN variants （multi-UAV scenario and multiple groups of targets scenario）. The performance of the algorithm is demonstrated through comparative experiments with other two state-of-the-art DTSPN algorithms identified in literature. Numerical simulations exhibit that the algorithm proposed in this paper can find high-quality solutions to the DTSPN with lower computational cost and produce significantly improved performance over the other algorithms.

关键词： Approximate gradient Dubins traveling salesmanproblem with neighborhood Local search Memetic algorithm Unmanned aerial vehicles

Smooth quadrotor trajectory generation for tracking a moving target in cluttered environments

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Science China(Information Sciences) 2021年第7期64卷 132-147页

作者： Lele XI zhihong peng Lei JIAO Ben M.CHEN School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System peng Cheng Laboratory Department of Mechanical and Automation Engineering The Chinese University of Hong Kong

In this paper, we present a trajectory generation method of a quadrotor, based on the optimal smoothing B-spline, for tracking a moving target with consideration of relative tracking pattern or limited field of view of the onboard sensor in cluttered environments. Compared to existing methods, safe flying zone,vehicle physical limits, and smoothness are fully considered to guarantee flight safety, kinodynamic feasibility, and tracking performance. To tackle the cluttered environments, a parallel particle swarm optimization algorithm is applied to find the feasible waypoints that the generated trajectory should be as close to as possible, with consideration of the target’s future state as well as obstacles to trade off the tracking performance and flight safety. Then, a sequential motion planning method, considering the above constraints, is applied and embedded into a cost function for solving the problem of robust tracking trajectory generation for the quadrotor via a convex optimization approach. The feasibility and effectiveness of the proposed method are verified by numerical simulations.

关键词： quadrotor target tracking trajectory generation B-spline convex optimization

Unsupervised robust recursive least-squares algorithm for impulsive noise filtering

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Science China(Information Sciences) 2013年第4期56卷 203-212页

作者： CHEN Jie MA Tao CHEN WenJie peng zhihong School of Automation Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision(Beijing Institute of Technology) Ministry of Education

A robust recursive least-squares(RLS) adaptive filter against impulsive noise is proposed for the situation of an unknown desired *** minimizing a saturable nonlinear constrained unsupervised cost function instead of the conventional least-squares function,a possible impulse-corrupted signal is prevented from entering the filter’s weight updating ***,a multi-step adaptive filter is devised to reconstruct the observed "impulse-free" noisy sequence,and whenever impulsive noise is detected,the impulse contaminated samples are replaced by predictive *** on simulation and experimental results,the proposed unsupervised robust recursive least-square adaptive filter performs as well as conventional RLS filters in "impulse-free" circumstances,and is effective in restricting large disturbances such as impulsive noise when the RLS and the more recent unsupervised adaptive filter fails.

关键词： unsupervised adaptive filtering impulsive noise suppression recursive least-squares algorithm

A multi-mode multi-skill project scheduling reformulation for reconnaissance mission planning

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Science China(Information Sciences) 2022年第6期65卷 267-268页

作者： Junqi CAI zhihong peng Sijian LIAO Shuxin DING School of Automation Beijing Institute of Technology Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited

Dear editor,Planning multi-agents scheduling to conduct reconnaissance missions is the core of reconnaissance decision support system. A reconnaissance mission comprises several tasks and a network representing precedence relationships between them, where each task represents an area in which the agents perform information gathering.

关键词： reconnaissance RCPSP multi-mode multi-skill scheduling multi agent system

Online adaptive Q-learning method for fully cooperative linear quadratic dynamic games

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Science China(Information Sciences) 2019年第12期62卷 164-177页

作者： Xinxing LI zhihong peng Lei JIAO Lele XI Junqi CAI School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

A model-based offline policy iteration(PI) algorithm and a model-free online Q-learning algorithm are proposed for solving fully cooperative linear quadratic dynamic games. The PI-based adaptive Q-learning method can learn the feedback Nash equilibrium online using the state samples generated by behavior policies, without sending inquiries to the system model. Unlike the existing Q-learning methods, this novel Q-learning algorithm executes both policy evaluation and policy improvement in an adaptive *** prove the convergence of the offline PI algorithm by proving its equivalence to Newton’s method while solving the game algebraic Riccati equation(GARE). Furthermore, we prove that the proposed Q-learning method will converge to the Nash equilibrium under a small learning rate if the method satisfies certain persistence of excitation conditions, which can be easily met by suitable behavior policies. Our simulation results demonstrate the good performance of the proposed online adaptive Q-learning algorithm.

关键词： adaptive dynamic programming reinforcement learning Q-learning fully cooperative linear quadratic dynamic games policy iteration off-policy

DESIGN OF AN EXPERT CONTROL SYSTEM FOR LEACHING PROCESS

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中国有色金属学会会刊：英文版 1999年第1期9卷 139-146页

作者： Wu, Min peng, zhihong Gui, Weihua Nakano, Michio Jinhua, She Central South Univ of Technology Changsha China

ＩＮＴＲＯＤＵＣＴＩＯＮＴｈｅｍａｉｎｐｒｏｃｅｓｅｓｉｎｚｉｎｃｈｙｄｒｏｍｅｔａｌｕｒｇｙａｒｅｌｅａｃｈｉｎｇ，ｐｕｒｉｆｉｃａｔｉｏｎａｎｄｅｌｅｃｔｒｏｌｙｓｉｓ［１，２］．Ｔｈｅｌｅａｃｈｉｎｇｉｎｖｏｌ... 详细信息

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关键词： leaching process expert control system mathematical model rule model

FORMATION AND SOLUBILITY OF POTASSIUM ALUMINOSILICATE

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中国有色金属学会会刊：英文版 1998年第1期8卷 121-123页

作者： Liu, Guihua Li, Xiaobin Zhang, Chuanfu peng, zhihong He, Boquan Central South Univ of Technology Changsha China

ＦＯＲＭＡＴＩＯＮＡＮＤＳＯＬＵＢＩＬＩＴＹＯＦＰＯＴＡＳＳＩＵＭＡＬＵＭＩＮＯＳＩＬＩＣＡＴＥ①ＬｉｕＧｕｉｈｕａ，ＬｉＸｉａｏｂｉｎ，ＺｈａｎｇＣｈｕａｎｆｕａｎｄＰｅｎｇＺｈｉｈｏｎｇＤｅｐａｒｔｍｅｎｔｏｆＮ... 详细信息

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关键词： potassium aluminosilicate structure solubility

Estimation of time series noise covariance using correlation technology

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控制理论与应用（英文版） 2011年第2期9卷 165-170页

作者： Ma, Tao Chen, Jie peng, zhihong Chen, Wenjie School of Automation Beijing Institute of Technology Beijing 100081 China Beijing Institute of Technology Beijing 100081 China

Covariance of clean signal and observed noise is necessary for extracting clean signal from a time *** is transferred to calculate the covariance of observed noise and clean signal's MA process,when the clean signal is described by an autoregressive moving average (ARMA) *** the correlations of the innovations data from observed time series to form a least-squares problem,a concisely autocovariance least-square (CALS) method has been proposed to estimate the *** also extended our work to the case of unknown MA process *** between Odelson's autocovariance least-square (ALS) estimation algorithm and the proposed CALS method show that the CALS method could get a much more exact and compact estimation of the covariance than ALS and its extended form.

关键词： Time series Correlation technology Covariance estimation Least-square method