93,838 research outputs found
Adaptive Safety with Control Barrier Functions
Adaptive Control Lyapunov Functions (aCLFs) were introduced 20 years ago, and provided a Lyapunov-based methodology for stabilizing systems with parameter uncertainty. The goal of this paper is to revisit this classic formulation in the context of safety-critical control. This will motivate a variant of aCLFs in the context of safety: adaptive Control Barrier Functions (aCBFs). Our proposed approach adaptively achieves safety by keeping the system’s state within a safe set even in the presence of parametric model uncertainty. We unify aCLFs and aCBFs into a single control methodology for systems with uncertain parameters in the context of a Quadratic Program (QP) based framework. We validate the ability of this unified framework to achieve stability and safety in an Adaptive Cruise Control (ACC) simulation
Adaptive Safety with Control Barrier Functions
Adaptive Control Lyapunov Functions (aCLFs) were introduced 20 years ago, and provided a Lyapunov-based methodology for stabilizing systems with parameter uncertainty. The goal of this paper is to revisit this classic formulation in the context of safety-critical control. This will motivate a variant of aCLFs in the context of safety: adaptive Control Barrier Functions (aCBFs). Our proposed approach adaptively achieves safety by keeping the system’s state within a safe set even in the presence of parametric model uncertainty. We unify aCLFs and aCBFs into a single control methodology for systems with uncertain parameters in the context of a Quadratic Program (QP) based framework. We validate the ability of this unified framework to achieve stability and safety in an Adaptive Cruise Control (ACC) simulation
Control Barrier Function Based Quadratic Programs for Safety Critical Systems
Safety critical systems involve the tight coupling between potentially
conflicting control objectives and safety constraints. As a means of creating a
formal framework for controlling systems of this form, and with a view toward
automotive applications, this paper develops a methodology that allows safety
conditions -- expressed as control barrier functions -- to be unified with
performance objectives -- expressed as control Lyapunov functions -- in the
context of real-time optimization-based controllers. Safety conditions are
specified in terms of forward invariance of a set, and are verified via two
novel generalizations of barrier functions; in each case, the existence of a
barrier function satisfying Lyapunov-like conditions implies forward invariance
of the set, and the relationship between these two classes of barrier functions
is characterized. In addition, each of these formulations yields a notion of
control barrier function (CBF), providing inequality constraints in the control
input that, when satisfied, again imply forward invariance of the set. Through
these constructions, CBFs can naturally be unified with control Lyapunov
functions (CLFs) in the context of a quadratic program (QP); this allows for
the achievement of control objectives (represented by CLFs) subject to
conditions on the admissible states of the system (represented by CBFs). The
mediation of safety and performance through a QP is demonstrated on adaptive
cruise control and lane keeping, two automotive control problems that present
both safety and performance considerations coupled with actuator bounds
Disturbance Observers for Robust Safety-critical Control with Control Barrier Functions
This work provides formal safety guarantees for control systems with
disturbance. A disturbance observer-based robust safety-critical controller is
proposed, that estimates the effect of the disturbance on safety and utilizes
this estimate with control barrier functions to attain provably safe dynamic
behavior. The observer error bound - which consists of transient and
steady-state parts - is quantified, and the system is endowed with robustness
against this error via the proposed controller. An adaptive cruise control
problem is used as illustrative example through simulations including real
disturbance data.Comment: 6 pages, 5 figure
Safe Control of Euler-Lagrange Systems with Limited Model Information
This paper presents a new safe control framework for Euler-Lagrange (EL)
systems with limited model information, external disturbances, and measurement
uncertainties. The EL system is decomposed into two subsystems called the proxy
subsystem and the virtual tracking subsystem. An adaptive safe controller based
on barrier Lyapunov functions is designed for the virtual tracking subsystem to
ensure the boundedness of the safe velocity tracking error, and a safe
controller based on control barrier functions is designed for the proxy
subsystem to ensure controlled invariance of the safe set defined either in the
joint space or task space. Theorems that guarantee the safety of the proposed
controllers are provided. In contrast to existing safe control strategies for
EL systems, the proposed method requires much less model information and can
ensure safety rather than input-to-state safety. Simulation results are
provided to illustrate the effectiveness of the proposed method.Comment: Accepted to IEEE CDC 2023 and this is the extended versio
Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints
Physical human-robot collaboration (pHRC) requires both compliance and safety
guarantees since robots coordinate with human actions in a shared workspace.
This paper presents a novel fixed-time adaptive neural control methodology for
handling time-varying workspace constraints that occur in physical human-robot
collaboration while also guaranteeing compliance during intended force
interactions. The proposed methodology combines the benefits of compliance
control, time-varying integral barrier Lyapunov function (TVIBLF) and
fixed-time techniques, which not only achieve compliance during physical
contact with human operators but also guarantee time-varying workspace
constraints and fast tracking error convergence without any restriction on the
initial conditions. Furthermore, a neural adaptive control law is designed to
compensate for the unknown dynamics and disturbances of the robot manipulator
such that the proposed control framework is overall fixed-time converged and
capable of online learning without any prior knowledge of robot dynamics and
disturbances. The proposed approach is finally validated on a simulated
two-link robot manipulator. Simulation results show that the proposed
controller is superior in the sense of both tracking error and convergence time
compared with the existing barrier Lyapunov functions based controllers, while
simultaneously guaranteeing compliance and safety
Safe Control Synthesis via Input Constrained Control Barrier Functions
This paper introduces the notion of an Input Constrained Control Barrier
Function (ICCBF), as a method to synthesize safety-critical controllers for
non-linear control affine systems with input constraints. The method identifies
a subset of the safe set of states, and constructs a controller to render the
subset forward invariant. The feedback controller is represented as the
solution to a quadratic program, which can be solved efficiently for real-time
implementation. Furthermore, we show that ICCBFs are a generalization of Higher
Order Control Barrier Functions, and thus are applicable to systems of
non-uniform relative degree. Simulation results are presented for the adaptive
cruise control problem, and a spacecraft rendezvous problem.Comment: 8 pages, 3 figures, submitted to Conference on Decision and Control
202
Robust Control Barrier Functions with Uncertainty Estimation
This paper proposes a safety controller for control-affine nonlinear systems
with unmodelled dynamics and disturbances to improve closed-loop robustness.
Uncertainty estimation-based control barrier functions (CBFs) are utilized to
ensure robust safety in the presence of model uncertainties, which may depend
on control input and states. We present a new uncertainty/disturbance estimator
with theoretical upper bounds on estimation error and estimated outputs, which
are used to ensure robust safety by formulating a convex optimization problem
using a high-order CBF. The possibly unsafe nominal feedback controller is
augmented with the proposed estimator in two frameworks (1) an uncertainty
compensator and (2) a robustifying reformulation of CBF constraint with respect
to the estimator outputs. The former scheme ensures safety with performance
improvement by adaptively rejecting the matched uncertainty. The second method
uses uncertainty estimation to robustify higher-order CBFs for safety-critical
control. The proposed methods are demonstrated in simulations of an uncertain
adaptive cruise control problem and a multirotor obstacle avoidance situation
Auxiliary-Variable Adaptive Control Barrier Functions for Safety Critical Systems
This paper studies safety guarantees for systems with time-varying control
bounds. It has been shown that optimizing quadratic costs subject to state and
control constraints can be reduced to a sequence of Quadratic Programs (QPs)
using Control Barrier Functions (CBFs). One of the main challenges in this
method is that the CBF-based QP could easily become infeasible under tight
control bounds, especially when the control bounds are time-varying. The
recently proposed adaptive CBFs have addressed such infeasibility issues, but
require extensive and non-trivial hyperparameter tuning for the CBF-based QP
and may introduce overshooting control near the boundaries of safe sets. To
address these issues, we propose a new type of adaptive CBFs called
Auxiliary-Variable Adaptive CBFs (AVCBFs). Specifically, we introduce an
auxiliary variable that multiplies each CBF itself, and define dynamics for the
auxiliary variable to adapt it in constructing the corresponding CBF
constraint. In this way, we can improve the feasibility of the CBF-based QP
while avoiding extensive parameter tuning with non-overshooting control since
the formulation is identical to classical CBF methods. We demonstrate the
advantages of using AVCBFs and compare them with existing techniques on an
Adaptive Cruise Control (ACC) problem with time-varying control bounds.Comment: 8 pages, 4 figure
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