TY - CHAP
T1 - Robust Data-Driven Passivity-Based Control of Underactuated Systems via Neural Approximators and Bayesian Inference
AU - Sirichotiyakul, Wankun
AU - Ashenafi, Nardos Ayele
AU - Satici, Aykut C.
N1 - Sirichotiyakul, Wankun; Ashenafi, Nardos Ayele; and Satici, Aykut C. (2022). "Robust Data-Driven Passivity-Based Control of Underactuated Systems via Neural Approximators and Bayesian Inference". In 2022 American Control Conference (ACC) (pp. 3266-3272). IEEE. https://doi.org/10.23919/ACC53348.2022.9867143
PY - 2022/1/1
Y1 - 2022/1/1
N2 - We synthesize controllers for underactuated robotic systems using data-driven approaches. Inspired by techniques from classical passivity theory, the control law is parametrized by the gradient of an energy-like (Lyapunov) function, which is represented by a neural network. With the control task encoded as the objective of the optimization, we systematically identify the optimal neural net parameters using gradient-based techniques. The proposed method is validated on the cart-pole swing-up task, both in simulation and on a real system. Additionally, we address questions about controller’s robustness against model uncertainties and measurement noise, using a Bayesian approach to infer a probability distribution over the parameters of the controller. The proposed robustness improvement technique is demonstrated on the simple pendulum system.
AB - We synthesize controllers for underactuated robotic systems using data-driven approaches. Inspired by techniques from classical passivity theory, the control law is parametrized by the gradient of an energy-like (Lyapunov) function, which is represented by a neural network. With the control task encoded as the objective of the optimization, we systematically identify the optimal neural net parameters using gradient-based techniques. The proposed method is validated on the cart-pole swing-up task, both in simulation and on a real system. Additionally, we address questions about controller’s robustness against model uncertainties and measurement noise, using a Bayesian approach to infer a probability distribution over the parameters of the controller. The proposed robustness improvement technique is demonstrated on the simple pendulum system.
KW - control systems
KW - neural networks
KW - probability distribution
KW - robustness
KW - training
KW - uncertainty
UR - https://scholarworks.boisestate.edu/mecheng_facpubs/195
UR - https://doi.org/10.23919/ACC53348.2022.9867143
UR - http://www.scopus.com/inward/record.url?scp=85138495347&partnerID=8YFLogxK
U2 - 10.23919/ACC53348.2022.9867143
DO - 10.23919/ACC53348.2022.9867143
M3 - Chapter
T3 - 0743-1619
SP - 3266
EP - 3272
BT - 2022 American Control Conference (ACC)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 American Control Conference, ACC 2022
Y2 - 8 June 2022 through 10 June 2022
ER -