Topic
Optimal substructure
About: Optimal substructure is a research topic. Over the lifetime, 550 publications have been published within this topic receiving 24580 citations.
Papers published on a yearly basis
1 / 2
Papers
Book•
1 May 1995TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
Abstract: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization. The treatment focuses on basic unifying themes, and conceptual foundations. It illustrates the versatility, power, and generality of the method with many examples and applications from engineering, operations research, and other fields. It also addresses extensively the practical application of the methodology, possibly through the use of approximations, and provides an extensive treatment of the far-reaching methodology of Neuro-Dynamic Programming/Reinforcement Learning.
12,992 citations
Book•
1 Jan 1987TL;DR: The optimal control problem is illustrated with examples of large, sparse nonlinear programming and a comparison of optimal control problems in the context of discrete-time programming.
Abstract: Preface 1. Introduction to nonlinear programming 2. Large, sparse nonlinear programming 3. Optimal control preliminaries 4. The optimal control problem 5. Optimal control examples Appendix A. Software Bibliography, Index.
1,670 citations
TL;DR: (1996).
Abstract: (1996). Dynamic Programming and Optimal Control. Volume 1. Journal of the Operational Research Society: Vol. 47, No. 6, pp. 833-834.
789 citations
1 Aug 2008
TL;DR: This paper aims to solve the infinite-time optimal tracking control problem for a class of discrete-time nonlinear systems using the greedy heuristic dynamic programming (HDP) iteration algorithm, and defines a new type of performance index.
Abstract: In this paper, we aim to solve the infinite-time optimal tracking control problem for a class of discrete-time nonlinear systems using the greedy heuristic dynamic programming (HDP) iteration algorithm. A new type of performance index is defined because the existing performance indexes are very difficult in solving this kind of tracking problem, if not impossible. Via system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then, the greedy HDP iteration algorithm is introduced to deal with the regulation problem with rigorous convergence analysis. Three neural networks are used to approximate the performance index, compute the optimal control policy, and model the nonlinear system for facilitating the implementation of the greedy HDP iteration algorithm. An example is given to demonstrate the validity of the proposed optimal tracking control scheme.
495 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2022 | 1 |
| 2021 | 2 |
| 2019 | 2 |
| 2018 | 7 |
| 2017 | 20 |
| 2016 | 18 |