Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions

TL;DR

This paper introduces a novel model-based learning method for synthesizing robust nonlinear controllers with safety and stability guarantees using neural Lyapunov-barrier functions, applicable to various complex robotic systems.

Contribution

It proposes a new robust control framework that generalizes Lyapunov functions under model uncertainty, improving safety and stability in nonlinear control tasks.

Findings

Controllers match or outperform robust MPC in simulations

Significant reduction in computational costs

Effective in diverse nonlinear control scenarios

Abstract

Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models. We develop a model-based learning approach to synthesize robust feedback controllers with safety and stability guarantees. We take inspiration from robust convex optimization and Lyapunov theory to define robust control Lyapunov barrier functions that generalize despite model uncertainty. We demonstrate our approach in simulation on problems including car trajectory tracking, nonlinear control with obstacle avoidance, satellite rendezvous with safety constraints, and flight control with a learned ground effect model. Simulation results show that our approach yields controllers that match or exceed the capabilities of robust MPC while reducing computational costs by an order of magnitude.