Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems

By Draguna L. Vrabie, Frank L. Lewis. Neural Networks, 2009

643 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/f056b896ef139385e0e9e9a5105357cb44a1cda2

How researchers see this paper

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

In Artificial Intelligence

In Computational Theory and Mathematics

In Control and Systems Engineering

Citations in the corpus

Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone (2016, methodology): “In [16], an adaptive direct optimal NN control with infinite horizon cost based on a reinforcement learning scheme was presented for unknown nonlinear continuous-time systems.”
Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics (2012, background): “Some related work has appeared in Vamvoudakis and Lewis (2016), and Vrabie and Lewis (2009, 2016), which are developed using neural networks (Ge, Lee, & Harris, 1998), and also in our recent work Jiang and Jiang (2017) proposing a framework of robust-ADP using the ISS small-gain theorem (Jiang,…”
Linear Quadratic Tracking Control of Partially-Unknown Continuous-Time Systems Using Reinforcement Learning (2014, methodology): “The Proposed Partially-Unknown IRL Algorithm for Solving the LQT Problem To obviate the need for complete knowledge of the system dynamics, the IRL algorithm [14], [23] can be extended to the LQT problem.”
Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming (2012, neutral): “…Balakrishnan, Ding, & Lewis, 2008; Dierks, Thumati, & Jagannathan, 2009; Jagannathan & He, 2008; Vamvoudakis & Lewis, 2010; Venayagamoorthy, Harley, & Wunsch, 2002; Venayagamoorthy, Wunsch, & Harley, 2000; Vrabie & Lewis, 2009; Yen & Delima, 2005; Zhang, Luo, & Liu, 2009; Zhang, Wei, & Liu, 2011).”
Observer-Based Neuro-Adaptive Optimized Control of Strict-Feedback Nonlinear Systems With State Constraints (2021, methodology): “In particular, based on universal approximators (e.g., NNs and fuzzy logic systems), numerous intelligent adaptive optima control schemes have been presented in recent years [21]–[29].”
Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems (2014, methodology): “The contributions of this paper are introducing for the first time the use of the experience replay to the IRL (Vrabie & Lewis, 2009) algorithms and incorporating the actuator limitations into control design.”
Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning (2014, methodology): “Later, inspired by Vrabie and Lewis (2009) and Vrabie et al. (2009), some online PI algorithms were presented to solve the optimal regulation problem for completely unknown linear systems (Jiang & Jiang, 2012; Lee, Park, & Choi, 2012).”
A novel actor-critic-identifier architecture for approximate optimal control of uncertain nonlinear systems (2013, methodology): “The integral RL technique in Vrabie and Lewis (2009) leads to a hybrid continuoustime/discrete-time controller with two time-scale actor–critic learning process, whereas the approach in Vamvoudakis and Lewis (2010), although continuous-time, requires complete knowledge of system dynamics.”