Learning‐based T‐sHDP() for optimal control of a class of nonlinear discrete‐time systems
From MaRDI portal
Publication:6085183
DOI10.1002/rnc.5847zbMath1528.49025OpenAlexW3208900289MaRDI QIDQ6085183
Weibo Liu, Luyang Yu, Yurong Liu, Fawaz E. Alsaadi
Publication date: 2 December 2023
Published in: International Journal of Robust and Nonlinear Control (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/rnc.5847
value iterationheuristic dynamic programming (HDP)eligibility traces (ET)learning-based optimal control
Dynamic programming in optimal control and differential games (49L20) Nonlinear systems in control theory (93C10) Discrete-time control/observation systems (93C55)
Cites Work
- Unnamed Item
- Unnamed Item
- Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems
- Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics
- Reinforcement \(Q\)-learning for optimal tracking control of linear discrete-time systems with unknown dynamics
- \(\mathrm{H}_\infty\) control of linear discrete-time systems: off-policy reinforcement learning
- Non-zero sum Nash Q-learning for unknown deterministic continuous-time linear systems
- Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem
- Adaptive optimal control for continuous-time linear systems based on policy iteration
- Finite-horizon filtering for a class of nonlinear time-delayed systems with an energy harvesting sensor
- Chance-constrained \(H_\infty\) control for a class of time-varying systems with stochastic nonlinearities: the finite-horizon case
- Multi-step heuristic dynamic programming for optimal control of nonlinear discrete-time systems
- Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach
- Linear Quadratic Tracking Control of Partially-Unknown Continuous-Time Systems Using Reinforcement Learning
- Optimal control for discrete‐time systems with actuator saturation
- H∞ tracking control for linear discrete‐time systems via reinforcement learning
- A Novel Framework for Backstepping-Based Control of Discrete-Time Strict-Feedback Nonlinear Systems With Multiplicative Noises
- Online optimal tracking control of continuous-time linear systems with unknown dynamics by using adaptive dynamic programming
This page was built for publication: Learning‐based T‐sHDP() for optimal control of a class of nonlinear discrete‐time systems
