Paper Info
Title
Neural network-based finite-horizon approximately optimal control of uncertain affine nonlinear continuous-time systems
Abstract
This paper develops a novel neural network (NN) based finite-horizon approximate optimal control of nonlinear continuous-time systems in affine form when the system dynamics are complete unknown. First an online NN identifier is proposed to learn the dynamics of the nonlinear continuous-time system. Subsequently, a second NN is utilized to learn the time-varying solution, or referred to as value function, of the Hamilton-Jacobi-Bellman (HJB) equation in an online and forward in time manner. Then, by using the estimated time-varying value function from the second NN and control coefficient matrix from the NN identifier, an approximate optimal control input is computed. To handle time varying value function, a NN with constant weights and time-varying activation function is considered and a suitable NN update law is derived based on normalized gradient descent approach. Further, in order to satisfy terminal constraint and ensure stability within the fixed final time, two extra terms, one corresponding to terminal constraint, and the other to stabilize the nonlinear system are added to the novel update law of the second NN. No initial stabilizing control is required. A uniformly ultimately boundedness of the closed-loop system is verified by using standard Lyapunov theory.
Year
DOI
Venue
2014
10.1109/ACC.2014.6858693
ACC
Keywords
DocType
ISSN
uncertain affine nonlinear continuous-time systems,neural network,terminal constraint,uncertain systems,optimal control,neurocontrollers,transfer functions,closed-loop system,time-varying value function,stabilization,continuous time systems,nonlinear control systems,finite-horizon,gradient methods,standard Lyapunov theory,approximate optimal control,time-varying activation function,neural network-based finite-horizon approximately optimal control,Hamilton-Jacobi-Bellman equation,system dynamics,online NN identifier,stability,normalized gradient descent approach,HJB equation,closed loop systems,Lyapunov methods,control coefficient matrix
Conference
0743-1619
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Hao Xu121414.63
Qiming Zhao2313.26
Travis Dierks339723.62
Sarangapani Jagannathan4113694.89