7 papers · 1 filter
Stability of Control Lyapunov Function Guided Reinforcement Learning
Zachary Olkin, William D. Compton, Aaron D. Ames
Reinforcement learning (RL) has become the de facto method for achieving locomotion on humanoid robots in practice, yet stability analysis of the corresponding control policies is…
Compatibility of Multiple Control Barrier Functions for Constrained Nonlinear Systems
Max H. Cohen, Eugene Lavretsky, Aaron D. Ames
Control barrier functions (CBFs) are a powerful tool for the constrained control of nonlinear systems; however, the majority of results in the literature focus on systems subject t…
Layered Nonlinear Model Predictive Control for Robust Stabilization of Hybrid Systems
Zachary Olkin, Aaron D. Ames
Computing the receding horizon optimal control of nonlinear hybrid systems is typically prohibitively slow, limiting real-time implementation. To address this challenge, we propose…
Learning for Layered Safety-Critical Control with Predictive Control Barrier Functions
William D. Compton, Max H. Cohen, Aaron D. Ames
Safety filters leveraging control barrier functions (CBFs) are highly effective for enforcing safe behavior on complex systems. It is often easier to synthesize CBFs for a Reduced…
Safety-Critical Controller Synthesis with Reduced-Order Models
Max H. Cohen, Noel Csomay-Shanklin, William D. Compton +2
Reduced-order models (ROMs) provide lower dimensional representations of complex systems, capturing their salient features while simplifying control design. Building on previous wo…
Bilevel Optimization for Real-Time Control with Application to Locomotion Gait Generation
Zachary Olkin, Aaron D. Ames
Model Predictive Control (MPC) is a common tool for the control of nonlinear, real-world systems, such as legged robots. However, solving MPC quickly enough to enable its use in re…