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Cymru","company_I111599522","Jiangnan University","江南大学",[186,194,195,187],"{\"ror\":\"04mkzax54\",\"openalex\":\"I111599522\",\"wikidata\":\"https://www.wikidata.org/wiki/Q6191676\"}","2026-05-14T00:48:17.237862162","2026-06-01T05:00:13.414623908","Wuxi, China","https://www.jiangnan.edu.cn","Jiāngnán Dàxué","Southern Yangtze University","company_I4210094879","Shandong Institute of Automation",[197,203],"{\"ror\":\"00qdtba35\",\"openalex\":\"I4210094879\"}","2026-05-14T01:04:19.322851188","2026-06-01T06:07:17.364767143","RESEARCH_INSTITUTE","山东省科学院自动化研究所成","company_I151746483","University of Waterloo",[214,205,215,216],"{\"ror\":\"01aff2v68\",\"openalex\":\"I151746483\",\"wikidata\":\"Q1049470\"}","openalex,graphrag","2026-05-14T00:33:45.258294546","2026-06-01T04:32:33.316353911","Waterloo, Canada","CA","https://uwaterloo.ca/","UWaterloo","Université de waterloo","Waterloo","company_I4396570548","Defence Science and 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tracking control for switched stochastic nonlinear systems with unknown actuator dead-zone","{\"openalex\":\"W1054396882\",\"doi\":\"10.1016/j.automatica.2015.07.022\"}","2026-05-20T03:57:13.194760301","2026-05-20T03:57:13.19879592","doi","10.1016/j.automatica.2015.07.022","arxivId","openalexId","W1054396882","abstractText","publishedDate","2015-08-01","publishedYear",2015,"venue","Automatica",355,"authors","keywords",[270,271,272],"pdfUrl","topVenue","Adaptive Control of Nonlinear Systems","Stability and Control of Uncertain Systems","Adaptive Dynamic Programming Control","paper_W2161356965","Kalman filtering for continuous-time uncertain systems with Markovian jumping parameters","{\"openalex\":\"W2161356965\",\"doi\":\"10.1109/9.780431\"}","2026-05-19T23:55:25.181858958","2026-05-19T23:55:25.182445195","10.1109/9.780431","W2161356965","Studies the problem of Kalman filtering for a class of uncertain linear continuous-time systems with Markovian jumping parameters. The system under consideration is subjected to time-varying norm-bounded parameter uncertainties in the state and measurement equations. Stochastic quadratic stability of the above system is analyzed. A state estimator is designed such that the covariance of the estimation error is guaranteed to be within a certain bound for all admissible uncertainties, which is in terms of solutions of two sets of coupled algebraic Riccati equations.","1999-01-01",1999,"IEEE Transactions on Automatic Control",370,[286,271,287],"Fault Detection and Control Systems","Target Tracking and Data Fusion in Sensor Networks","paper_W2003456468","Distributed Finite-Time Containment Control for Double-Integrator Multiagent Systems","{\"openalex\":\"W2003456468\",\"doi\":\"10.1109/tcyb.2013.2288980\"}","2026-05-19T23:30:37.442903036","2026-05-19T23:35:33.338423926","10.1109/tcyb.2013.2288980","W2003456468","In this paper, the distributed finite-time containment control problem for double-integrator multiagent systems with multiple leaders and external disturbances is discussed. In the presence of multiple dynamic leaders, by utilizing the homogeneous control technique, a distributed finite-time observer is developed for the followers to estimate the weighted average of the leaders' velocities at first. Then, based on the estimates and the generalized adding a power integrator approach, distributed finite-time containment control algorithms are designed to guarantee that the states of the followers converge to the dynamic convex hull spanned by those of the leaders in finite time. Moreover, as a special case of multiple dynamic leaders with zero velocities, the proposed containment control algorithms also work for the case of multiple stationary leaders without using the distributed observer. Simulations demonstrate the effectiveness of the proposed control algorithms.","2013-11-19",2013,"IEEE Transactions on Cybernetics",371,[301,270,272],"Distributed Control Multi-Agent Systems","paper_W2343977510","Observer-Based Fault Detection for Nonlinear Systems With Sensor Fault and Limited Communication Capacity","{\"openalex\":\"W2343977510\",\"doi\":\"10.1109/tac.2015.2503566\"}","2026-05-19T14:51:52.326922762","2026-05-19T14:51:52.32676132","10.1109/tac.2015.2503566","W2343977510","In this technical note, a new fault detection design scheme is proposed for interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems with sensor fault based on a novel fuzzy observer. The parameter uncertainties can be captured by the membership functions of the IT2 fuzzy model. The premise variables of the plant are perfectly shared by the fuzzy observer. A stochastic process between the plant and the observer is considered in the system. A fault sensitive performance is established, and then sufficient conditions are obtained for determining the fuzzy observer gains. Finally, simulation results are provided to verify the effectiveness of the presented scheme.","2015-11-24",403,[286,313,314],"Fuzzy Logic and Control Systems","Fuzzy Systems and Optimization","paper_W2539264244","Analysis and synthesis of networked control systems: A survey of recent advances and challenges","{\"openalex\":\"W2539264244\",\"doi\":\"10.1016/j.isatra.2016.09.026\"}","2026-05-19T12:35:29.195121843","2026-05-19T12:35:29.195026847","10.1016/j.isatra.2016.09.026","W2539264244","2016-10-21",2016,"ISA Transactions",412,[271,327,328],"Network Time Synchronization Technologies","Petri Nets in System Modeling","paper_W2240923019","Adaptive Neural Fault-Tolerant Control of a 3-DOF Model Helicopter System","{\"openalex\":\"W2240923019\",\"doi\":\"10.1109/tsmc.2015.2426140\"}","2026-05-19T11:53:30.26140138","2026-05-19T11:53:53.195368084","10.1109/tsmc.2015.2426140","W2240923019","In this paper, an adaptive neural fault-tolerant control scheme is proposed for the three degrees of freedom model helicopter, subject to system uncertainties, unknown external disturbances, and actuator faults. To tackle system uncertainty and nonlinear actuator faults, a neural network disturbance observer is developed based on the radial basis function neural network. The unknown external disturbance and the unknown neural network approximation errors are treated as a compound disturbance that is estimated by another nonlinear disturbance observer. A disturbance observer-based adaptive neural fault-tolerant control scheme is then developed to track the desired system output in the presence of system uncertainty, external disturbance, and actuator faults. The stability of the whole closed-loop system is analyzed using the Lyapunov method, which guarantees the convergence of all closed-loop signals. Finally, the simulation results are presented to illustrate the effectiveness of the new control design techniques.","2015-05-11","IEEE Transactions on Systems Man and Cybernetics Systems",415,[270,272,286],"paper_W2005595169","Observer and Command-Filter-Based Adaptive Fuzzy Output Feedback Control of Uncertain Nonlinear Systems","{\"openalex\":\"W2005595169\",\"doi\":\"10.1109/tie.2015.2418317\"}","2026-05-19T11:11:47.199437296","2026-05-19T11:11:47.199353574","10.1109/tie.2015.2418317","W2005595169","In this paper, observer and command-filter-based adaptive fuzzy output feedback control is proposed for a class of strict-feedback systems with parametric uncertainties and unmeasured states. First, fuzzy logic systems are used to approximate the unknown and nonlinear functions. Next, a fuzzy state observer is developed to estimate the immeasurable states. Then, command-filtered backstepping control is designed to avoid the explosion of complexity in the backstepping design, and compensating signals are introduced to remove the effect of the errors caused by command filters. The proposed method guarantees that all signals in the closed-loop systems are bounded. The main contributions of this paper are the proposed control method can overcome two problems of linear in the unknown system parameter and explosion of complexity in backstepping-design methods and it does not require that all of the states of the system are measured directly. Finally, two examples are provided to illustrate its effectiveness.","2015-04-03","IEEE Transactions on Industrial Electronics",418,[270,313,353],"Advanced Control Systems Design","paper_W2342560477","Fault Detection Filtering for Nonlinear Switched Stochastic Systems","{\"openalex\":\"W2342560477\",\"doi\":\"10.1109/tac.2015.2465091\"}","2026-05-16T03:31:15.504522251","2026-05-21T21:21:06.199866785","10.1109/tac.2015.2465091","W2342560477","In this note, the fault detection filtering problem is solved for nonlinear switched stochastic system in the T-S fuzzy framework. Our attention is concentrated on the construction of a robust fault detection technique to the nonlinear switched system with Brownian motion. Based on observer-based fault detection fuzzy filter as a residual generator, the proposed fault detection is formulated as a fuzzy filtering problem. By the utilization of the average dwell time technique and the piecewise Lyapunov function technique, the fuzzy-parameter-dependent fault detection filters are designed that guarantee the resulted error system to be mean-square exponential stable with a weighted H _∞ error performance. Then, the corresponding solvability condition for the fault detection fuzzy filter is also established by the linearization procedure technique. Finally, simulation has been presented to show the effectiveness of the proposed fault detection technique.","2015-08-12",507,[314,271,313],"paper_W2093837911","Asynchronous l2–l∞ filtering for discrete-time stochastic Markov jump systems with randomly occurred sensor nonlinearities","{\"openalex\":\"W2093837911\",\"doi\":\"10.1016/j.automatica.2013.09.041\"}","2026-05-15T17:41:16.41316702","2026-05-21T12:39:35.184156395","10.1016/j.automatica.2013.09.041","W2093837911","2013-10-24",564,[271,375,376],"Matrix Theory and Algorithms","Neural Networks Stability and Synchronization","paper_W2792882341","Finite-time command filtered backstepping control for a class of nonlinear systems","{\"openalex\":\"W2792882341\",\"doi\":\"10.1016/j.automatica.2018.03.033\"}","2026-05-14T20:28:12.190358983","2026-05-20T20:43:01.185969409","10.1016/j.automatica.2018.03.033","W2792882341","2018-03-25",2018,837,[270,388,272],"Iterative Learning Control Systems",{"_60":390,"_62":391},[],[392],{"_65":393,"_67":394,"_69":395,"_71":396},1784476482069,{"_74":398,"_76":77,"_78":393,"_79":-5,"_80":26,"_81":26,"_82":26,"_83":-5,"_84":-5,"_85":86,"_35":87,"_88":89},[73,397],"[\"portal\",\"industries\"]","industries",[399,400,401,402,403,404,405,406,407,408,409,410],{"_15":472,"_29":473,"_31":-5,"_32":474,"_33":475,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":414,"_42":-5,"_44":-5,"_415":416,"_417":-5,"_418":77,"_419":420,"_421":476,"_423":26,"_424":86,"_425":26,"_426":-5,"_58":473,"_59":475},{"_15":468,"_29":469,"_31":-5,"_32":-5,"_33":470,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":414,"_42":-5,"_44":-5,"_415":416,"_417":-5,"_418":77,"_419":420,"_421":471,"_423":26,"_424":86,"_425":26,"_426":-5,"_58":469,"_59":470},{"_15":462,"_29":463,"_31":-5,"_32":464,"_33":465,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":414,"_42":-5,"_44":-5,"_415":416,"_417":-5,"_418":77,"_419":420,"_421":466,"_423":26,"_424":86,"_425":26,"_426":-5,"_58":463,"_59":465},{"_15":458,"_29":459,"_31":-5,"_32":-5,"_33":460,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":414,"_42":-5,"_44":-5,"_415":416,"_417":-5,"_418":77,"_419":420,"_421":461,"_423":26,"_424":86,"_425":26,"_426":-5,"_58":459,"_59":460},{"_15":454,"_29":455,"_31":-5,"_32":-5,"_33":456,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":414,"_42":-5,"_44":-5,"_415":416,"_417":-5,"_418":77,"_419":420,"_421":457,"_423":26,"_424":86,"_425":26,"_426":-5,"_58":455,"_59":456},{"_15":448,"_29":449,"_31":-5,"_32":450,"_33":451,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":414,"_42":-5,"_44":-5,"_415":416,"_417":-5,"_418":77,"_419":420,"_421":452,"_423":26,"_424":86,"_425":26,"_426":-5,"_58":449,"_59":451},{"_15":443,"_29":444,"_31":-5,"_32":-5,"_33":445,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":414,"_42":-5,"_44":-5,"_415":416,"_417":-5,"_418":77,"_419":420,"_421":446,"_423":26,"_424":447,"_425":26,"_426":-5,"_58":444,"_59":445},{"_15":439,"_29":440,"_31":-5,"_32":-5,"_33":441,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":414,"_42":-5,"_44":-5,"_415":416,"_417":-5,"_418":77,"_419":420,"_421":442,"_423":26,"_424":86,"_425":26,"_426":-5,"_58":440,"_59":441},{"_15":435,"_29":436,"_31":-5,"_32":-5,"_33":437,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":414,"_42":-5,"_44":-5,"_415":416,"_417":-5,"_418":77,"_419":420,"_421":438,"_423":26,"_424":86,"_425":26,"_426":-5,"_58":436,"_59":437},{"_15":431,"_29":432,"_31":-5,"_32":-5,"_33":433,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":414,"_42":-5,"_44":-5,"_415":416,"_417":-5,"_418":77,"_419":420,"_421":434,"_423":26,"_424":86,"_425":26,"_426":-5,"_58":432,"_59":433},{"_15":427,"_29":428,"_31":-5,"_32":-5,"_33":429,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":414,"_42":-5,"_44":-5,"_415":416,"_417":-5,"_418":77,"_419":420,"_421":430,"_423":26,"_424":86,"_425":26,"_426":-5,"_58":428,"_59":429},{"_15":411,"_29":412,"_31":-5,"_32":-5,"_33":413,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":414,"_42":-5,"_44":-5,"_415":416,"_417":-5,"_418":77,"_419":420,"_421":422,"_423":26,"_424":86,"_425":26,"_426":-5,"_58":412,"_59":413},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","children","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[453],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[467],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[477,478],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_481":24},"SITE_BASE_URL"]

Peng Shi 人物

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