Neural Differentiable Integral Control Barrier Functions for Unknown Nonlinear Systems with Input Constraints
In this paper, we propose a deep learning based control synthesis framework for fast and online computation of controllers that guarantees the safety of general nonlinear control systems with unknown dynamics in the presence of input constraints. Towards this goal, we propose a framework for simultaneously learning the unknown system dynamics, which can change with time due to external disturbances, and an integral control law for trajectory tracking based on imitation learning. Simultaneously, we learn corresponding safety certificates, which we refer to as Neural Integral Control Barrier Functions (Neural ICBF's), that automatically encode both the state and input constraints into a single scalar-valued function and enable the design of controllers that can guarantee that the state of the unknown system will never leave a safe subset of the state space. Finally, we provide numerical simulations that validate our proposed approach and compare it with classical as well as recent learning based methods from the relevant literature.
Code (0)
등록된 구현이 없습니다.
Tasks
Imitation LearningSimilar Papers 제목 키워드 기반
Differentiable Optimization Layered Safety-Critical Control for Risk-Aware Navigation via Conformal Prediction
Risk-aware navigation in unknown environments is a fundamental challenge for autonomous vehicles operating in complex urban systems. To address this issue, this paper presents a differentiable optimization layered safety…
Autonomous VehiclesBarrier Integral Control for Global Asymptotic Stabilization of Uncertain Nonlinear Systems under Smooth Feedback and Transient Constraints
This paper addresses the problem of asymptotic stabilization for high-order control-affine MIMO nonlinear systems with unknown dynamic terms. We introduce Barrier Integral Control, a novel algorithm designed to confine t…
Safe Importance Sampling in Model Predictive Path Integral Control
We introduce the notion of importance sampling under embedded barrier state control, titled Safety Controlled Model Predictive Path Integral Control (SC-MPPI). For robotic systems operating in an environment with multipl…
Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processes
This paper focuses on the controller synthesis for unknown, nonlinear systems while ensuring safety constraints. Our approach consists of two steps, a learning step that uses Gaussian processes and a controller synthesis…
Gaussian ProcessesLearning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments
Control barrier functions (CBFs) have become a popular tool to enforce safety of a control system. CBFs are commonly utilized in a quadratic program formulation (CBF-QP) as safety-critical constraints. A class $\mathcal{…