Adaptive dynamic programming

By J. Murray, C. Cox, G. Lendaris, R. Saeks. IEEE Trans. Syst. Man Cybern. Part C, 2002

687 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d1e459cdbd3e7b7a20fd32e74b40f1d6e655f44f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Derong Liu
2. Frank L. Lewis
3. Qinglai Wei
4. Ding Wang
5. Huaguang Zhang
6. Haibo He
7. Hamidreza Modares
8. Kyriakos G. Vamvoudakis

In Control and Systems Engineering

1. Derong Liu
2. Frank L. Lewis
3. Ding Wang
4. Qinglai Wei
5. Haibo He
6. Kyriakos G. Vamvoudakis
7. Hamidreza Modares
8. Hado van Hasselt

In Electrical and Electronic Engineering

1. Frank L. Lewis
2. Derong Liu
3. Huaguang Zhang
4. Qinglai Wei
5. Ding Wang
6. Kyriakos G. Vamvoudakis
7. Hamidreza Modares
8. Draguna L. Vrabie

Citations in the corpus

• Adaptive Dynamic Programming for Control: A Survey and Recent Advances (2021, background): “[6] explained that the PI algorithm for”
• Adaptive optimal control for continuous-time linear systems based on policy iteration (2009, positive): “(9) and (10) formulate a new policy iteration algorithm motivated by the work of Murray et al. (2002).”
• Adaptive Critic Nonlinear Robust Control: A Survey (2017, background): “Among them, complex industrial systems, such as power systems [30], [44], [67], [84], [129]–[133], [158], [181]–[186], mechanical systems [32], [36], [41], [44], [71], [123], [125], [131], [187], [188], and intelligent transportation systems [189], [190] are the most common application areas.”
• Adaptive Dynamic Programming: An Introduction (2009, methodology): “[2], [3], [5], [8], [11]–[13], [21], [22], [25], [30], [31], [34], [35], [40], [46], [49], [52], [54], [55], [63], [70], [76], [80], [83], [95], [96], [99], [100].”
• Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach (2005, background): “Finally, it would be interesting to show how one can solve for the HJB equation when the system dynamicsf, g are unknown as discussed in Murray et al. (2002).”
• Reinforcement learning and adaptive dynamic programming for feedback control (2009, methodology): “Note that the value update in the critic is performed by solving (46) or (51) using standard adaptive control techniques, namely RLS.”
• Computational Intelligence in Urban Traffic Signal Control: A Survey (2012, background): “categories [41]: heuristic dynamic programming (HDP), dual heuristic programming (DHP), and globalized DHP (GDHP).”
• Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems (2014, methodology): “For continuous-time policy iteration algorithms [17], according to the derivative of the difference for the iterative performance index functions along with time variable t , it is proven that the iterative performance index functions are nonincreasingly convergent to the optimal solution of the HJB”
• Online adaptive optimal control for continuous-time Markov jump linear systems using a novel policy iteration algorithm (2015, neutral): “For an overview of contributions for linear systems, see [21][5].”