Consensus complementarity control for multi-contact mpc
We propose a hybrid model predictive control algorithm, consensus complementarity
control, for systems that make and break contact with their environment. Many state-of-the …
control, for systems that make and break contact with their environment. Many state-of-the …
Real-time multi-contact model predictive control via admm
A Aydinoglu, M Posa - 2022 International Conference on …, 2022 - ieeexplore.ieee.org
We propose a hybrid model predictive control algorithm, consensus complementarity control
(C3), for systems that make and break contact with their environment. Many state-of-the-art …
(C3), for systems that make and break contact with their environment. Many state-of-the-art …
Revisiting pgd attacks for stability analysis of large-scale nonlinear systems and perception-based control
Many existing region-of-attraction (ROA) analysis tools find difficulty in addressing feedback
systems with large-scale neural network (NN) policies and/or high-dimensional sensing …
systems with large-scale neural network (NN) policies and/or high-dimensional sensing …
Robust stability of neural-network-controlled nonlinear systems with parametric variability
S Talukder, R Kumar - IEEE Transactions on Systems, Man …, 2023 - ieeexplore.ieee.org
Stability certification and identification of a safe and stabilizing initial set are two important
concerns in ensuring operational safety, stability, and robustness of dynamical systems. With …
concerns in ensuring operational safety, stability, and robustness of dynamical systems. With …
Verification in the loop: Correct-by-construction control learning with reach-avoid guarantees
In the current control design of safety-critical autonomous systems, formal verification
techniques are typically applied after the controller is designed to evaluate whether the …
techniques are typically applied after the controller is designed to evaluate whether the …
Closed‐loop stability analysis of deep reinforcement learning controlled systems with experimental validation
Trained deep reinforcement learning (DRL) based controllers can effectively control
dynamic systems where classical controllers can be ineffective and difficult to tune …
dynamic systems where classical controllers can be ineffective and difficult to tune …
Robust control theory based stability certificates for neural network approximated nonlinear model predictive control
HH Nguyen, T Zieger, RD Braatz, R Findeisen - IFAC-PapersOnLine, 2021 - Elsevier
Abstract Model predictive control requires the real-time solution of an optimal control
problem, which can be challenging on computationally limited systems. Approximating the …
problem, which can be challenging on computationally limited systems. Approximating the …
Torque-Bounded Admittance Control With Implicit Euler Realization of Set-Valued Operators
When a robot collides with environments of unknown stiffness, the resultant torque
saturation can cause the conventional admittance control to exhibit unsafe behaviors such …
saturation can cause the conventional admittance control to exhibit unsafe behaviors such …
Locomotion as a risk-mitigating behavior in uncertain environments: A rapid planning and few-shot failure adaptation approach
We want robots to complete assigned tasks even when unexpected task pressures arise,
either from the robot or the environment. This paper presents a method of both learning …
either from the robot or the environment. This paper presents a method of both learning …
Revisiting PGD Attacks for Stability Analysis of High-Dimensional Nonlinear Systems and Perception-Based Control
Many existing region-of-attraction (ROA) analysis tools find difficulty in addressing feedback
systems with large-scale neural network (NN) policies and/or high-dimensional sensing …
systems with large-scale neural network (NN) policies and/or high-dimensional sensing …