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Found 77 papers, 20 papers with code
Secure Safety Filter Design for Sampled-data Nonlinear Systems under Sensor Spoofing Attacks
This paper presents a secure safety filter design for nonlinear systems under sensor spoofing attacks.
Control Barrier Function Synthesis for Nonlinear Systems with Dual Relative Degree
Control barrier functions (CBFs) are a powerful tool for synthesizing safe control actions; however, constructing CBFs remains difficult for general nonlinear systems.
Layered Nonlinear Model Predictive Control for Robust Stabilization of Hybrid Systems
Computing the receding horizon optimal control of nonlinear hybrid systems is typically prohibitively slow, limiting real-time implementation.
Computationally Efficient Safe Control of Linear Systems under Severe Sensor Attacks
Cyber-physical systems are prone to sensor attacks that can compromise safety.
Learning for Layered Safety-Critical Control with Predictive Control Barrier Functions
This paper introduces \emph{predictive CBFs} to address this gap by leveraging rollouts of the FoM to define a predictive robustness term added to the RoM CBF condition.
Rectified Control Barrier Functions for High-Order Safety Constraints
This paper presents a novel approach for synthesizing control barrier functions (CBFs) from high relative degree safety constraints: Rectified CBFs (ReCBFs).
Zero-Order Control Barrier Functions for Sampled-Data Systems with State and Input Dependent Safety Constraints
We propose a novel zero-order control barrier function (ZOCBF) for sampled-data systems to ensure system safety.
Minimizing Conservatism in Safety-Critical Control for Input-Delayed Systems via Adaptive Delay Estimation
The main idea is to reduce the maximum delay estimation error bound so that the state prediction error bound is monotonically non-increasing.
Safety-Critical Controller Synthesis with Reduced-Order Models
Reduced-order models (ROMs) provide lower dimensional representations of complex systems, capturing their salient features while simplifying control design.
Dynamic Tube MPC: Learning Tube Dynamics with Massively Parallel Simulation for Robust Safety in Practice
Safe navigation of cluttered environments is a critical challenge in robotics.
A Contract Theory for Layered Control Architectures
Autonomous systems typically leverage layered control architectures with a combination of discrete and continuous models operating at different timescales.
Bilevel Optimization for Real-Time Control with Application to Locomotion Gait Generation
In this paper, we extend this idea to a bilevel control framework where a "high-level" optimization program modifies a controller parameter of a "low-level" MPC problem which generates the control inputs and desired state trajectory.
Hierarchical Event-Triggered Systems: Safe Learning of Quasi-Optimal Deadline Policies
We present a hierarchical architecture to improve the efficiency of event-triggered control (ETC) in reducing resource consumption.
Safety of Linear Systems under Severe Sensor Attacks
For the offline phase, we derive conditions on safe sets for all possible sensor attacks that may be encountered during system deployment.
Constructive Nonlinear Control of Underactuated Systems via Zero Dynamics Policies
Stabilizing underactuated systems is an inherently challenging control task due to fundamental limitations on how the control input affects the unactuated dynamics.
Preferential Multi-Objective Bayesian Optimization
As a direct consequence, this result provides, to our knowledge, the first convergence guarantee for dueling Thompson sampling in the PBO setting.
Constructive Safety-Critical Control: Synthesizing Control Barrier Functions for Partially Feedback Linearizable Systems
Certifying the safety of nonlinear systems, through the lens of set invariance and control barrier functions (CBFs), offers a powerful method for controller synthesis, provided a CBF can be constructed.
Rollover Prevention for Mobile Robots with Control Barrier Functions: Differentiator-Based Adaptation and Projection-to-State Safety
This paper develops rollover prevention guarantees for mobile robots using control barrier function (CBF) theory, and demonstrates the method experimentally.
Bounding Stochastic Safety: Leveraging Freedman's Inequality with Discrete-Time Control Barrier Functions
In contrast, this paper utilizes Freedman's inequality in the context of discrete-time control barrier functions (DTCBFs) and c-martingales to provide stronger (less conservative) safety guarantees for stochastic systems.
Collision Avoidance and Geofencing for Fixed-wing Aircraft with Control Barrier Functions
The proposed CBF-based controllers showcase the capability of safely executing simultaneous collision avoidance and geofencing, as demonstrated by simulations on the kinematic model and a high-fidelity dynamical model.
Generative Modeling of Residuals for Real-Time Risk-Sensitive Safety with Discrete-Time Control Barrier Functions
A key source of brittleness for robotic systems is the presence of model uncertainty and external disturbances.
Safety-Critical Control of Nonholonomic Vehicles in Dynamic Environments using Velocity Obstacles
This paper considers collision avoidance for vehicles with first-order nonholonomic constraints maintaining nonzero forward speeds, moving within dynamic environments.
Characterizing Smooth Safety Filters via the Implicit Function Theorem
Optimization-based safety filters, such as control barrier function (CBF) based quadratic programs (QPs), have demonstrated success in controlling autonomous systems to achieve complex goals.
Composing Control Barrier Functions for Complex Safety Specifications
The increasing complexity of control systems necessitates control laws that guarantee safety w. r. t.
On the Safety of Connected Cruise Control: Analysis and Synthesis with Control Barrier Functions
Finally, we combine the analysis of safety measures and the corresponding stability charts to synthesize safety-critical connected cruise controllers using CBFs.
Input-to-State Stability in Probability
Input-to-State Stability (ISS) is fundamental in mathematically quantifying how stability degrades in the presence of bounded disturbances.
Intermittent Safety Filters for Event-Triggered Safety Maneuvers with Application to Satellite Orbit Transfers
Our first trigger in the scheme monitors safety constraints encoded by barrier functions, and thereby ensures safety without the need to alter the nominal controller--and when the boundary of the safety constraint is approached, the controller drives the system to the region where control actions are not needed.
Sample-and-Hold Safety with Control Barrier Functions
A common assumption on the deployment of safeguarding controllers on the digital platform is that high sampling frequency translates to a small violation of safety.
Lipschitz Continuity of Signal Temporal Logic Robustness Measures: Synthesizing Control Barrier Functions from One Expert Demonstration
By filtering control inputs to maintain the positivity of this function, we ensure that the system trajectory satisfies the desired STL specification.
Parameterized Barrier Functions to Guarantee Safety under Uncertainty
In this context, robust control barrier functions -- in a variety of forms -- have been used to obtain safety guarantees for uncertain systems.
Safety-Critical Control with Bounded Inputs via Reduced Order Models
Guaranteeing safe behavior on complex autonomous systems -- from cars to walking robots -- is challenging due to the inherently high dimensional nature of these systems and the corresponding complex models that may be difficult to determine in practice.
Robust Safety under Stochastic Uncertainty with Discrete-Time Control Barrier Functions
To this end, we leverage Control Barrier Functions (CBFs) which guarantee that a robot remains in a ``safe set'' during its operation -- yet CBFs (and their associated guarantees) are traditionally studied in the context of continuous-time, deterministic systems with bounded uncertainties.
End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions
Imitation learning (IL) is a learning paradigm which can be used to synthesize controllers for complex systems that mimic behavior demonstrated by an expert (user or control algorithm).
Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data
Recent advances in safety-critical risk-aware control are predicated on apriori knowledge of the disturbances a system might face.
Safety-Critical Controller Verification via Sim2Real Gap Quantification
To develop more useful models, we quantify the inaccuracy with which a given model represents a system of interest, so that we may leverage this quantity to facilitate controller synthesis and verification.
Disturbance Observers for Robust Safety-critical Control with Control Barrier Functions
This work provides formal safety guarantees for control systems with disturbance.
Safe Drone Flight with Time-Varying Backup Controllers
TBCs reduce conservatism when compared to traditional backup controllers and can be directly applied to multi-agent coordination to guarantee safety.
Control Barrier Functionals: Safety-critical Control for Time Delay Systems
The theory of control barrier functions, that provides formal safety guarantees for delay-free systems, is extended to systems with state delay.
Input-to-State Safety with Input Delay in Longitudinal Vehicle Control
Safe longitudinal control is discussed for a connected automated truck traveling behind a preceding connected vehicle.
Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verification
The dramatic increase of autonomous systems subject to variable environments has given rise to the pressing need to consider risk in both the synthesis and verification of policies for these systems.
Stability and Safety through Event-Triggered Intermittent Control with Application to Spacecraft Orbit Stabilization
Motivated by the event-triggered control paradigm, where state-dependent triggers are utilized in a sample-and-hold context, we generalize this concept to include state triggers where the controller is off thereby creating a framework for intermittent control.
Multi-Rate Planning and Control of Uncertain Nonlinear Systems: Model Predictive Control and Control Lyapunov Functions
Modern control systems must operate in increasingly complex environments subject to safety constraints and input limits, and are often implemented in a hierarchical fashion with different controllers running at multiple time scales.
Safe Backstepping with Control Barrier Functions
Complex control systems are often described in a layered fashion, represented as higher-order systems where the inputs appear after a chain of integrators.
Safe Control for Nonlinear Systems with Stochastic Uncertainty via Risk Control Barrier Functions
To this end, we introduce risk control barrier functions (RCBFs), which are compositions of barrier functions and dynamic, coherent risk measures.
Safety of Sampled-Data Systems with Control Barrier Functions via Approximate Discrete Time Models
Existing design paradigms do not address the gap between theory (controller design with continuous time models) and practice (the discrete time sampled implementation of the resulting controllers); this can lead to poor performance and violations of safety for hardware instantiations.
A Scenario Approach to Risk-Aware Safety-Critical System Verification
With the growing interest in deploying robots in unstructured and uncertain environments, there has been increasing interest in factoring risk into safety-critical control development.
A Barrier-Based Scenario Approach to Verify Safety-Critical Systems
In this letter, we detail our randomized approach to safety-critical system verification.
LyaNet: A Lyapunov Framework for Training Neural ODEs
Our approach, called LyaNet, is based on a novel Lyapunov loss formulation that encourages the inference dynamics to converge quickly to the correct prediction.
Onboard Safety Guarantees for Racing Drones: High-speed Geofencing with Control Barrier Functions
This paper details the theory and implementation behind practically ensuring safety of remotely piloted racing drones.
Test and Evaluation of Quadrupedal Walking Gaits through Sim2Real Gap Quantification
This dual approach has the added benefit of quantifying the Sim2Real Gap between a system simulator and its hardware counterpart.
Safety-Critical Control with Input Delay in Dynamic Environment
Endowing nonlinear systems with safe behavior is increasingly important in modern control.
Disturbance Bounds for Signal Temporal Logic Task Satisfaction: A Dynamics Perspective
This letter offers a novel approach to Test and Evaluation of pre-existing controllers from a control barrier function and dynamics perspective.
Interactive multi-modal motion planning with Branch Model Predictive Control
Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors of the uncontrolled agents must be considered.
Learning Performance Bounds for Safety-Critical Systems
As a result, the test and evaluation ideal would be to verify the efficacy of a system simulator and use this verification result to make a statement on true system performance.
Risk-Averse Decision Making Under Uncertainty
In this paper, we consider the problem of designing policies for MDPs and POMDPs with objectives and constraints in terms of dynamic coherent risk measures, which we refer to as the constrained risk-averse problem.
Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State
The increasing complexity of modern robotic systems and the environments they operate in necessitates the formal consideration of safety in the presence of imperfect measurements.
Backup Control Barrier Functions: Formulation and Comparative Study
The backup control barrier function (CBF) was recently proposed as a tractable formulation that guarantees the feasibility of the CBF quadratic programming (QP) via an implicitly defined control invariant set.
Iterative Model Predictive Control for Piecewise Systems
First, we present an algorithm that leverages a feasible trajectory that completes the task to construct a control policy which guarantees that state and input constraints are recursively satisfied and that the closed-loop system reaches the goal state in finite time.
Risk-Averse Stochastic Shortest Path Planning
We consider the stochastic shortest path planning problem in MDPs, i. e., the problem of designing policies that ensure reaching a goal state from a given initial state with minimum accrued cost.
Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control
We present a straightforward and efficient way to control unstable robotic systems using an estimated dynamics model.
Unified Multi-Rate Control: from Low Level Actuation to High Level Planning
In this paper we present a hierarchical multi-rate control architecture for nonlinear autonomous systems operating in partially observable environments.
Constrained Risk-Averse Markov Decision Processes
We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints.
Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains.
Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots
This paper presents a framework that leverages both control theory and machine learning to obtain stable and robust bipedal locomotion without the need for manual parameter tuning.
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
ROIAL learns Bayesian posteriors that predict each exoskeleton user's utility landscape across four exoskeleton gait parameters.
Reactive motion planning with probabilistic safety guarantees
Motion planning in environments with multiple agents is critical to many important autonomous applications such as autonomous vehicles and assistive robots.
Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions
The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions.
Safety-Critical Control of Compartmental Epidemiological Models with Measurement Delays
We introduce a methodology to guarantee safety against the spread of infectious diseases by viewing epidemiological models as control systems and by considering human interventions (such as quarantining or social distancing) as control input.
Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties
Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles.
Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users' preferences over a high-dimensional gait parameter space.
Recurrent Neural Network Control of a Hybrid Dynamic Transfemoral Prosthesis with EdgeDRNN Accelerator
Lower leg prostheses could improve the life quality of amputees by increasing comfort and reducing energy to locomote, but currently control methods are limited in modulating behaviors based upon the human's experience.
Partially Observable Games for Secure Autonomy
Technology development efforts in autonomy and cyber-defense have been evolving independently of each other, over the past decade.
Risk-Averse Planning Under Uncertainty
We consider the problem of designing policies for partially observable Markov decision processes (POMDPs) with dynamic coherent risk objectives.
Dynamic Walking with Compliance on a Cassie Bipedal Robot
The control of bipedal robotic walking remains a challenging problem in the domains of computation and experiment, due to the multi-body dynamics and various sources of uncertainty.
Robotics
A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability
The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective.
Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics.
Traversing Environments Using Possibility Graphs for Humanoid Robots
Locomotion for legged robots poses considerable challenges when confronted by obstacles and adverse environments.
