Data-based reinforcement learning approximate optimal control for an uncertain nonlinear system with control effectiveness faults

DOI10.1016/j.automatica.2020.108922zbMath1440.93137OpenAlexW3016201194MaRDI QIDQ1987267

J. Willard Curtis, Patryk Deptula, Emily A. Doucette, Zachary I. Bell, Warren E. Dixon

Publication date: 14 April 2020

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2020.108922

zbMATH Keywords

exponential stabilityreinforcement learningapproximate dynamic programminguncertain nonlinear systemapproximate optimal control

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Control/observation systems with incomplete information (93C41) Dynamic programming (90C39) Existence theories for optimal control problems involving ordinary differential equations (49J15) Iterative learning control (93B47)

Related Items (6)

Distributed optimized dynamic event-triggered control for unknown heterogeneous nonlinear MASs with input-constrained ⋮ Temporal logic guided safe model-based reinforcement learning: a hybrid systems approach ⋮ Interference compensation discrete control based on memory data for a class of nonlinear systems ⋮ Model-based reinforcement learning for approximate optimal control with temporal logic specifications ⋮ Data-based reinforcement learning approximate optimal control for an uncertain nonlinear system with control effectiveness faults ⋮ Safe exploration in model-based reinforcement learning using control barrier functions

Cites Work

• Unnamed Item
• Unnamed Item
• Efficient model-based reinforcement learning for approximate online optimal control
• Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems
• Model-based reinforcement learning for approximate optimal regulation
• Nonlinear control of engineering systems. A Lyapunov-based approach.
• Reinforcement learning for optimal feedback control. A Lyapunov-based approach
• A novel actor-critic-identifier architecture for approximate optimal control of uncertain nonlinear systems
• Data-based reinforcement learning approximate optimal control for an uncertain nonlinear system with control effectiveness faults
• Observer based adaptive dynamic programming for fault tolerant control of a class of nonlinear systems
• Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics
• A calculus for computing Filippov's differential inclusion with application to the variable structure control of robot manipulators
• Combined MRAC for Unknown MIMO LTI Systems With Parameter Convergence
• Robust Adaptive Dynamic Programming
• Integral concurrent learning: Adaptive control with parameter convergence using finite excitation
• Concurrent Learning for Parameter Estimation Using Dynamic State-Derivative Estimators
• Differential equations with discontinuous right-hand side

This page was built for publication: Data-based reinforcement learning approximate optimal control for an uncertain nonlinear system with control effectiveness faults