Search Results - (dynamic OR dynamika) programming/optimal control: approximate (dynamic OR dynamika) programming

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

Authors: de Farias, Daniela Pucci Van Roy, Benjamin

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

Access URL: https://www.jstor.org/stable/25151747

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

Authors: de Farias, Daniela Pucci Van Roy, Benjamin

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

Access URL: https://www.jstor.org/stable/30035661

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

Authors: Klabjan, Diego Adelman, Daniel

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

Access URL: https://www.jstor.org/stable/25151808

Performance Loss Bounds for Approximate Value Iteration with State Aggregation

Authors: Van Roy, Benjamin

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

Access URL: https://www.jstor.org/stable/25151721

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

Authors: Shuaibin Guan Xingjian Fu

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

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

File Description: electronic resource

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

Access URL: https://doaj.org/article/7c099400bb254ccca045c4126ab62b87

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

Authors: Guan, Shuaibin Fu, Xingjian

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

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

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

Authors: Kuhn, Daniel

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

Access URL: https://www.jstor.org/stable/40538391

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

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

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

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

Access 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/

Continuous-Time Markov Decision Processes with Discounted Rewards: The Case of Polish Spaces

Authors: Guo, Xianping

Source: Mathematics of Operations Research, 2007 Feb 01. 32(1), 73-87.

Access URL: https://www.jstor.org/stable/25151772

Region-based approximation in approximate dynamic programming.

Authors: Sardarmehni, Tohid Song, Xingyong

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

Subject Terms: DYNAMIC programming, VALUE capture, CLOSED loop systems

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

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

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

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

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

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

Subject Terms: 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.

Authors: Zhang, Jingyu Ohtsuka, Toshiyuki

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

Subject Terms: 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.

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

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

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

Approximate Optimal Motion Planning to Avoid Unknown Moving Avoidance Regions.

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

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

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

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

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

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

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

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

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

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

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

Método de error de Bellman con ponderación de volumen para mallado adaptativo en programación dinámica aproximada. (Spanish)

Alternate Title: Volume-weighted Bellman error method for adaptive meshing in approximate dynamic programming. (English)

Authors: Armesto, Leopoldo Sala, Antonio

Source: Revista Iberoamericana de Automática e Informática Industrial; 2022, Vol. 19 Issue 1, p37-47, 11p

Bayesian Exploration for Approximate Dynamic Programming.

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

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

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

On the Taylor Expansion of Value Functions.

Authors: Braverman, Anton Gurvich, Itai Huang, Junfei

Source: Operations Research; Mar/Apr2020, Vol. 68 Issue 2, p631-654, 24p, 2 Charts, 5 Graphs

Subject Terms: TAYLOR'S series, HAMILTON-Jacobi-Bellman equation, DYNAMIC programming, PARTIAL differential equations