Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning

In this paper, a new formulation for the optimal tracking control problem (OTCP) of continuous-time nonlinear systems is presented. This formulation extends the integral reinforcement learning (IRL) technique, a method for solving optimal regulation problems, to learn the solution to the OTCP. Unlik...

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Veröffentlicht in:	Automatica (Oxford) Jg. 50; H. 7; S. 1780 - 1792
Hauptverfasser:	Modares, Hamidreza, Lewis, Frank L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.07.2014 Elsevier
Schlagworte:	Algorithmics. Computability. Computer arithmetics Applied sciences Artificial intelligence Computer science; control theory; systems Control system synthesis Control theory. Systems Drift Dynamical systems Dynamics Exact sciences and technology Input constrainers Integral reinforcement learning Mathematical analysis Neural networks Nonlinear dynamics Optimal control Optimal tracking control Optimization Reinforcement Theoretical computing Tracking Optimal tracking control Integral reinforcement learning Neural networks Input constrainers Unknown input Tracking Non linear control Control synthesis Continuous time Online algorithm Dynamical system Control constraint Uncertain system Optimal control (mathematics) Mathematical programming Bellman equation Stability Reinforcement learning Tracking task Neural network Non linear system Constrained optimization Persistence Hamilton Jacobi equation Optimal control Optimal solution Problem solving
ISSN:	0005-1098, 1873-2836
Online-Zugang:	Volltext
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