TY - JOUR
T1 - Event-triggered intelligent critic control with input constraints applied to a nonlinear aeroelastic system
AU - Sun, Bo
AU - Wang, Xuerui
AU - van Kampen, Erik Jan
PY - 2022
Y1 - 2022
N2 - In this paper, we establish an event-triggered intelligent control scheme with a single critic network, to cope with the optimal stabilization problem of nonlinear aeroelastic systems. The main contribution lies in the design of a novel triggering condition with input constraints, avoiding the Lipschitz assumption on the inverse hyperbolic tangent function. Based on an improved weight updating criterion that eliminates the requirement of initial admissible control, the control law is obtained approximately by online training of a single critic network. The Lyapunov stability and the Zeno phenomenon of the closed-loop system are analysed. The feasibility of the established algorithm is verified by applying it to an optimal stabilization task of a nonlinear aeroelastic system. The results reveal that the developed approach can handle input-constrained optimal control problems, with performance comparable to the time-based method that updates control inputs at each instant, while reducing the computational and communication's load.
AB - In this paper, we establish an event-triggered intelligent control scheme with a single critic network, to cope with the optimal stabilization problem of nonlinear aeroelastic systems. The main contribution lies in the design of a novel triggering condition with input constraints, avoiding the Lipschitz assumption on the inverse hyperbolic tangent function. Based on an improved weight updating criterion that eliminates the requirement of initial admissible control, the control law is obtained approximately by online training of a single critic network. The Lyapunov stability and the Zeno phenomenon of the closed-loop system are analysed. The feasibility of the established algorithm is verified by applying it to an optimal stabilization task of a nonlinear aeroelastic system. The results reveal that the developed approach can handle input-constrained optimal control problems, with performance comparable to the time-based method that updates control inputs at each instant, while reducing the computational and communication's load.
KW - Adaptive dynamic programming
KW - Artificial neural network
KW - Event-triggered control
KW - Input constraints
KW - Nonlinear aeroelastic system
UR - http://www.scopus.com/inward/record.url?scp=85121118009&partnerID=8YFLogxK
U2 - 10.1016/j.ast.2021.107279
DO - 10.1016/j.ast.2021.107279
M3 - Article
AN - SCOPUS:85121118009
SN - 1270-9638
VL - 120
JO - Aerospace Science and Technology
JF - Aerospace Science and Technology
M1 - 107279
ER -