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Policy iteration-based adaptive optimal control for Markov jump systems:a transition-probability-free asynchronous approach: 《Science China(Information Sciences)》2024年第11期359-360,共2页Weidi CHENG Chengcheng REN Shuping HE Changyin SUN; supported by the National Natural Science Foundation of China(Grant No.62073001);the Anhui Provincial Key Research and Development Project(Grant No.2022i01020013);the University Synergy Innovation Program of Anhui Province(Grant No.GXXT-2021-010);the Anhui Province Graduate Education Quality Project(Grant No.2023xscx027)。; The controller and filter design problems of Markov jump systems(MJSs)have gained significant attention over the past few decades.These studies include various aspects,including stochastic stabilization[1],optimal tra...; 关键词：optimal ITERATION STABILIZATION

An online value iteration method for linear-quadratic mean field social control with unknown dynamics: 《Science China(Information Sciences)》2024年第4期34-35,共2页Bing-Chang WANG Shumei LI Ying CAO; supported by National Natural Science Foundation of China(Grant No.62122043)。; Mean field(MF)models have been widely applied to economics,control theory,and other fields.Its prominent feature is that the individual influence on the overall population is negligible and the impact of the entire sy...; 关键词：QUADRATIC ITERATION FIELD

Segment-wise learning control for trajectory tracking of robot manipulators under iteration-dependent periods: 《Science China(Information Sciences)》2024年第3期151-162,共12页Fan ZHANG Deyuan MENG Kaiquan CAI; supported in part by National Natural Science Foundation of China (Grant Nos.U2333215,62273018);National Key Research and Development Program of China (Grant No.2021YFB2601703);Science and Technology on Space Intelligent Control Laboratory (Grant No.HTKJ2022KL502006)。; This paper is concerned with the amplitude boundedness problem of adaptive iterative learning control(AILC)for robot manipulators operating with iteration-dependent periods.By introducing virtual memory slots for stor...; 关键词：amplitude boundedness iteration-dependent period iterative learning control robot manipulator segment-wise virtual memory slot

A novel policy iteration algorithm for solving the optimal consensus control problem of a discrete-time multiagent system with unknown dynamics: 《Science China(Information Sciences)》2023年第8期265-266,共2页Wenkai XU Li WANG Shiwen SUN Chengyi XIA &Zengqiang CHEN; supported by National Natural Science Foundation of China(Grant Nos.61403280,61773286);the support from 131 Innovative Talents Program of Tianjin。; At present,an increasing number of researchers have noticed the importance of optimal consensus control(OCC)of multiagent systems(MASs)because of their rich practical applications in various areas[1–4].; 关键词：POLICY OPTIMAL ITERATION

Online adaptive Q-learning method for fully cooperative linear quadratic dynamic games被引量：7: 《Science China(Information Sciences)》2019年第12期148-161,共14页Xinxing LI Zhihong PENG Lei JIAO Lele XI Junqi CAI; supported by Key Program of National Natural Science Foundation of China(Grant No.U1613225); 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 ...; 关键词：adaptive DYNAMIC programming reinforcement learning Q-LEARNING fully COOPERATIVE linear QUADRATIC DYNAMIC GAMES policy ITERATION off-policy

Policy iteration based Q-learning for linear nonzero-sum quadratic differential games被引量：6: 《Science China(Information Sciences)》2019年第5期195-213,共19页Xinxing LI Zhihong PENG Li LIANG Wenzhong ZHA; supported by National Natural Science Foundation of China (Grant No. 61203078);the Key Project of Shenzhen Robotics Research Center NSFC (Grant No. U1613225); 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 empl...; 关键词：adaptive dynamic programming ADP Q-LEARNING reinforcement learning RL LINEAR nonzerosum QUADRATIC differential games policy ITERATION PI off-policy

Towards dataflow based graph processing被引量：3: 《Science China(Information Sciences)》2017年第12期270-272,共3页Hai JIN Pengcheng YAO Xiaofei LIAO; supported by National High Technology Research and Development Program of China(863 Program)(Grant No.2015AA015303); Modern graph processing is widely used for solving a vast variety of real-world problems,e.g.,web sites ranking[1]and community detection[2].To better adapt and express the procedure of graph iteration,a wide spectrum...; 关键词：CONCURRENT PARTITION ITERATION RANKING instructions branch ADAPT processor scheduling operations

Iterative learning control approach for consensus of multi-agent systems with regular linear dynamics被引量：3: 《Science China(Information Sciences)》2017年第7期264-266,共3页Qin FU Panpan GU Xiangdong LI Jianrong WU; supported by National Natural Science Foundation of China (Grant No. 11371013);Natural Science Foundation of Suzhou University of Science and Technology in 2016; Dear editor,Iterative learning control（ILC）has a wellestablished research history,as shown in[1,2].By generating a correct control signal from the previous control execution,it can achieve perfect tracking performan...; 关键词：consensus perfect execution editor generating iteration irregular correct satisfy iterative

Iterative spherical simplex unscented particle filter for CNS/Redshift integrated navigation system被引量：5: 《Science China(Information Sciences)》2017年第4期109-119,共11页Kui FU Guangqiong ZHAO Xiajing LI Zhong-Liang TANG Wei HE; supported by National Basic Research Program of China (973 Program) (Grant No. 2014CB744206); We propose an improved Unscented Particle Filter（UPF） algorithm for the Celestial Navigation System/Redshift（CNS/Redshift） integrated navigation system. The algorithm adopts the iterated spherical simplex unscente...; 关键词：CNS/Redshift navigation system UPF spherical simplex ITERATION

Robust and fast iterative sparse recovery method for space-time adaptive processing: 《Science China(Information Sciences)》2016年第6期195-207,共13页Xiaopeng YANG Yuze SUN Tao ZENG Teng LONG; supported by 111 Project of China (Grant No. B14010);National Natural Science Foundation of China (Grant Nos. 61225005, 61120106004); Conventional space-time adaptive processing（STAP） requires large numbers of independent and identically distributed（i.i.d） training samples to ensure the performance of clutter suppression, which is hard to be ach...; 关键词：space-time adaptive processing（STAP） sparse recovery robust iteration computational complexity

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