Suchergebnisse - dynamic programming/optimal control: approximate dynamic programming*

Adaptive dynamic programming optimal tracking control of multi-UAVs based on zero-sum game

Autoren: Shuaibin Guan Xingjian Fu

Quelle: Automatika, Vol 66, Iss 3, Pp 491-502 (2025)

Schlagwörter: Multi-UAVs, zero-sum game, adaptive dynamic programming, neural networks, Control engineering systems. Automatic machinery (General), TJ212-225, Automation, T59.5

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/0005-1144; https://doaj.org/toc/1848-3380

Zugangs-URL: https://doaj.org/article/7c099400bb254ccca045c4126ab62b87

Adaptive dynamic programming optimal tracking control of multi-UAVs based on zero-sum game.

Autoren: Guan, Shuaibin Fu, Xingjian

Quelle: Automatika: Journal for Control, Measurement, Electronics, Computing & Communications. Aug2025, Vol. 66 Issue 3, p491-502. 12p.

Schlagwörter: Zero sum games, Sliding mode control, Dynamic programming, Game theory, Adaptive control systems, Tracking algorithms

A Cost-Shaping Linear Program for Average-Cost Approximate Dynamic Programming with Performance Guarantees

Autoren: de Farias, Daniela Pucci Van Roy, Benjamin

Quelle: Mathematics of Operations Research, 2006 Aug 01. 31(3), 597-620.

Zugangs-URL: https://www.jstor.org/stable/25151747

Automata theory meets approximate dynamic programming: Optimal control with temporal logic constraints

Autoren: Papusha, Ivan Fu, Jie Topcu, Ufuk et al.

Quelle: 2016 IEEE 55th Conference on Decision and Control (CDC). :434-440

Schlagwörter: 0209 industrial biotechnology, 13. Climate action, 02 engineering and technology

Zugangs-URL: https://ieeexplore.ieee.org/document/7798307/
https://dblp.uni-trier.de/db/conf/cdc/cdc2016.html#PapushaFTM16
http://authors.library.caltech.edu/73437/
http://ieeexplore.ieee.org/document/7798307/

On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming

Autoren: de Farias, Daniela Pucci Van Roy, Benjamin

Quelle: Mathematics of Operations Research, 2004 Aug 01. 29(3), 462-478.

Zugangs-URL: https://www.jstor.org/stable/30035661

An Infinite-Dimensional Linear Programming Algorithm for Deterministic Semi-Markov Decision Processes on Borel Spaces

Autoren: Klabjan, Diego Adelman, Daniel

Quelle: Mathematics of Operations Research, 2007 Aug 01. 32(3), 528-550.

Zugangs-URL: https://www.jstor.org/stable/25151808

Benchmarking a Scalable Approximate Dynamic Programming Algorithm for Stochastic Control of Grid-Level Energy Storage.

Autoren: Salas, Daniel F. Powell, Warren B.

Quelle: INFORMS Journal on Computing. Winter2018, Vol. 30 Issue 1, p106-123. 18p.

Performance Loss Bounds for Approximate Value Iteration with State Aggregation

Autoren: Van Roy, Benjamin

Quelle: Mathematics of Operations Research, 2006 May 01. 31(2), 234-244.

Zugangs-URL: https://www.jstor.org/stable/25151721

Region-based approximation in approximate dynamic programming.

Autoren: Sardarmehni, Tohid Song, Xingyong

Quelle: International Journal of Control; Feb2024, Vol. 97 Issue 2, p306-315, 10p

Schlagwörter: DYNAMIC programming, VALUE capture, CLOSED loop systems

Control of a Buck DC/DC Converter Using Approximate Dynamic Programming and Artificial Neural Networks.

Autoren: Dong, Weizhen Li, Shuhui Fu, Xingang et al.

Quelle: IEEE Transactions on Circuits & Systems. Part I: Regular Papers; Apr2021, Vol. 68 Issue 4, p1760-1768, 9p

Schlagwörter: ARTIFICIAL neural networks, DYNAMIC programming, RECURRENT neural networks, PREDICTIVE control systems, ONLINE education, COST functions, TIME perspective

Output‐feedback stochastic model predictive control of chance‐constrained nonlinear systems.

Autoren: Zhang, Jingyu Ohtsuka, Toshiyuki

Quelle: IET Control Theory & Applications (Wiley-Blackwell); Jul2023, Vol. 17 Issue 10, p1283-1294, 12p

Schlagwörter: NONLINEAR systems, DYNAMIC programming, STOCHASTIC models, PREDICTION models, STOCHASTIC control theory, STOCHASTIC programming, STOCHASTIC systems

Adaptive Identifier-Critic-Based Optimal Tracking Control for Nonlinear Systems With Experimental Validation.

Autoren: Na, Jing Lv, Yongfeng Zhang, Kaiqiang et al.

Quelle: IEEE Transactions on Systems, Man & Cybernetics. Systems; Jan2022, Vol. 52 Issue 1, p459-472, 14p

Schlagwörter: TRACKING control systems, DYNAMIC programming, STEADY-state responses, HELICOPTERS, SYSTEM dynamics, NONLINEAR dynamical systems, ARTIFICIAL satellite tracking

Infinite horizon optimal control of affine nonlinear discrete switched systems using two-stage approximate dynamic programming.

Autoren: Cao, Ning Zhang, Huaguang Luo, Yanhong et al.

Quelle: International Journal of Systems Science; Sep2012, Vol. 43 Issue 9, p1673-1682, 10p

Schlagwörter: CONTROL theory (Engineering), DISCRETE systems, NONLINEAR systems, ITERATIVE methods (Mathematics), APPROXIMATION theory, DYNAMIC programming, MULTIVARIATE analysis, PROBLEM solving, ALGORITHMS

An Information-Based Approximation Scheme for Stochastic Optimization Problems in Continuous Time

Autoren: Kuhn, Daniel

Quelle: Mathematics of Operations Research, 2009 May 01. 34(2), 428-444.

Zugangs-URL: https://www.jstor.org/stable/40538391

Approximate Optimal Motion Planning to Avoid Unknown Moving Avoidance Regions.

Autoren: Deptula, Patryk Chen, Hsi-Yuan Licitra, Ryan A. et al.

Quelle: IEEE Transactions on Robotics; Apr2020, Vol. 36 Issue 2, p414-430, 17p

Schlagwörter: OBSTACLE avoidance (Robotics), DYNAMIC programming, MOTION, NONLINEAR systems, PRIOR learning, PATTERNS (Mathematics)

Relaxations of Weakly Coupled Stochastic Dynamic Programs.

Autoren: Adelman, Daniel Mersereau, Adam J.

Quelle: Operations Research. May/Jun2008, Vol. 56 Issue 3, p712-727. 16p. 1 Diagram, 4 Charts, 3 Graphs.

Schlagwörter: *LINEAR programming, *DYNAMIC programming, *MATHEMATICAL optimization, STOCHASTIC programming, DECOMPOSITION method, RELAXATION methods (Mathematics)

Reinforcement Learning-Based Cooperative Optimal Output Regulation via Distributed Adaptive Internal Model.

Autoren: Gao, Weinan Mynuddin, Mohammed Wunsch, Donald C. et al.

Quelle: IEEE Transactions on Neural Networks & Learning Systems; Oct2022, Vol. 33 Issue 10, p5229-5240, 12p

Schlagwörter: MACHINE learning, REINFORCEMENT learning, MULTIAGENT systems, DYNAMIC programming, ADAPTIVE control systems, SYSTEM dynamics, SYMMETRIC matrices

Sensitivity Analysis of a Dynamic Fleet Management Model Using Approximate Dynamic Programming.

Autoren: Topaloglu, Huseyin Powell, Warren B.

Quelle: Operations Research. Mar/Apr2007, Vol. 55 Issue 2, p319-331. 13p. 4 Diagrams, 2 Charts.

Schlagwörter: *OPERATIONS research, *DYNAMIC programming, *STOCHASTIC processes, *FREIGHT & freightage, *TRANSPORTATION, *REVENUE management

Reductions of Approximate Linear Programs for Network Revenue Management.

Autoren: Vossen, Thomas W. M. Zhang, Dan

Quelle: Operations Research. Nov/Dec2015, Vol. 63 Issue 6, p1352-1371. 20p. 2 Charts.

Schlagwörter: *LINEAR programming, *DYNAMIC programming, *APPROXIMATION theory, *ECONOMIC demand, PIECEWISE linear topology

Bayesian Exploration for Approximate Dynamic Programming.

Autoren: Ryzhov, Ilya O. Mes, Martijn R. K. Powell, Warren B. et al.

Quelle: Operations Research; Jan/Feb2019, Vol. 67 Issue 1, p198-214, 17p, 4 Graphs

Schlagwörter: DYNAMIC programming, BAYESIAN analysis, STOCHASTIC approximation, RESOURCE allocation, TRANSPORTATION, FINANCE