NSF CAREER: Perceivability: Enabling Safe and Secure Autonomy via Synergistic Control, Observation and Learning
CAREER: Perceivability: Enabling Safe and Secure Autonomy via Synergistic Control, Observation and Learning (NSF Award #1942907)
Autonomous systems such as drones and self-driving cars must learn about their surroundings while staying safe, even when sensors are imperfect and communication cannot be fully trusted. This project introduces perceivability, a structural property describing whether a system can safely build knowledge about its environment over time, given its dynamics, sensing, communication, learning method, and time limits. If an environment is not perceivable, the project asks which system parameters could change to make it so, and how control and observation can work together to gather knowledge more safely.
The research is organized around four tasks:
- Foundations of perceivability: clarity as a measure of information gain, and reachability-based characterizations of perceivable sets under constrained system dynamics.
- Enhancing perceivability through synergistic control and observation: estimation and control over finite time horizons, including control barrier functions that respect input constraints and state-estimation errors, and the gatekeeper framework for online safety verification of nonlinear systems.
- Experimental demonstration and case studies: multi-agent dynamic coverage of spatio-temporal environments, certifiably-correct mapping for safe navigation despite odometry drift, and flight experiments with a custom-built quadrotor.
- Education and outreach: course development (Multi-Robot Systems, ROB 416/516), open-source hardware and software (github.com/dasc-lab), and K-12 summer activities.
People
Principal Investigator
Current Members
Alumni
Joseph Breeden
Role: PhD Aerospace (2019 - 2024)
Now: Senior Principal GNC Engineer @ Northrop Grumman
Papers
- Provably Safe Stein Variational Clarity-Aware Informative PlanningAutonomous robots are increasingly deployed for information-gathering tasks in environments that vary across space and time. Planning informative and safe trajectories in such settings is challenging because information decays when regions are not revisited. Most existing planners model information as static or uniformly decaying, ignoring environments where the decay rate varies spatially; those that model non-uniform decay often overlook how it evolves along the robot's motion, and almost all treat safety as a soft penalty. In this paper, we address these challenges. We model uncertainty in the environment using clarity, a normalized representation of differential entropy from our earlier work that captures how information improves through new measurements and decays over time when regions are not revisited. Building on this, we present Stein Variational Clarity-Aware Informative Planning, a framework that embeds clarity dynamics within trajectory optimization and enforces safety through a low-level filtering mechanism based on our earlier gatekeeper framework for safety verification. The planner performs Bayesian inference-based learning via Stein variational inference, refining a distribution over informative trajectories while filtering each nominal Stein informative trajectory to ensure safety. Hardware experiments and simulations across environments with varying decay rates and obstacles demonstrate consistent safety and reduced information deficits.
- A Formal gatekeeper Framework for Safe Dual Control with Active ExplorationPlanning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations, yet ignores uncertainty reduction and leads to overly conservative behavior. Actively reducing uncertainty on-the-fly during a nominal mission defines the dual control problem. Most approaches address this by adding a weighted exploration term to the cost, tuned to trade off the nominal objective and uncertainty reduction, but without formal consideration of when exploration is beneficial. Moreover, safety is enforced in some methods but not in others. We propose a framework that integrates robust planning with active exploration under formal guarantees as follows: The key innovation and contribution is that exploration is pursued only when it provides a verifiable improvement without compromising safety. To achieve this, we utilize our earlier work on gatekeeper as an architecture for safety verification, and extend it so that it generates both safe and informative trajectories that reduce uncertainty and the cost of the mission, or keep it within a user-defined budget. The methodology is evaluated via simulation case studies on the online dual control of a quadrotor under parametric uncertainty.
- Adaptive Control Allocation for Underactuated Time-Scale Separated Non-Affine SystemsMany robotic systems are underactuated, meaning not all degrees of freedom can be directly controlled due to lack of actuators, input constraints, or state-dependent actuation. This property, compounded by modeling uncertainties and disturbances, complicates the control design process for trajectory tracking. In this work, we propose an adaptive control architecture for uncertain, nonlinear, underactuated systems with input constraints. Leveraging time-scale separation, we construct a reduced-order model where fast dynamics provide virtual inputs to the slower subsystem and use dynamic control allocation to select the optimal control inputs given the non-affine dynamics. To handle uncertainty, we introduce a state predictor-based adaptive law, and through singular perturbation theory and Lyapunov analysis, we prove stability and bounded tracking of reference trajectories. The proposed method is validated on a VTOL quadplane with nonlinear, state-dependent actuation, demonstrating its utility as a unified controller across various flight regimes, including cruise, landing transition, and hover.
- Autonomy Architectures for Safe Planning in Unknown Environments Under Budget ConstraintsMission planning can often be formulated as a constrained control problem under multiple path constraints (i.e., safety constraints) and budget constraints (i.e., resource expenditure constraints). In a priori unknown environments, verifying that an offline solution will satisfy the constraints for all time can be difficult, if not impossible. We present ReRoot, a novel sampling-based framework that enforces safety and budget constraints for nonlinear systems in unknown environments. The main idea is that ReRoot grows multiple reverse RRT* trees online, starting from renewal sets, i.e., sets where the budget constraints are renewed. The dynamically feasible backup trajectories guarantee safety and reduce resource expenditure, which provides a principled backup policy when integrated into the gatekeeper safety verification architecture. We demonstrate our approach in simulation with a fixed-wing UAV in a GNSS-denied environment with a budget constraint on localization error that can be renewed at visual landmarks.
- Multi-Robot Allocation for Information Gathering in Non-Uniform Spatiotemporal EnvironmentsAutonomous robots are increasingly deployed to estimate spatiotemporal fields (e.g., wind, temperature, gas concentration) that vary across space and time. We consider environments divided into non-overlapping regions with distinct spatial and temporal dynamics, termed non-uniform spatiotemporal environments. Gaussian Processes (GPs) can be used to estimate these fields. The GP model depends on a kernel that encodes how the field co-varies in space and time, with its spatial and temporal lengthscales defining the correlation. Hence, when these lengthscales are incorrect or do not correspond to the actual field, the estimates of uncertainty can be highly inaccurate. Existing GP methods often assume one global lengthscale or update only periodically; some allow spatial variation but ignore temporal changes. To address these limitations, we propose a two-phase framework for multi-robot field estimation. Phase 1 uses a variogram-driven planner to learn region-specific spatial lengthscales. Phase 2 employs an allocation strategy that reassigns robots based on the current uncertainty, and updates sampling as temporal lengthscales are refined. For encoding uncertainty, we utilize clarity, an information metric from our earlier work. We evaluate the proposed method across diverse environments and provide convergence analysis for spatial lengthscale estimation, along with dynamic regret bounds quantifying the gap to the oracle's allocation sequence
- How to Adapt Control Barrier Functions? A Learning-Based Approach with Applications to a VTOL QuadplaneIEEE CDC 2025
abstract / project page / code / web demo / arxiv /In this paper, we present a novel theoretical framework for online adaptation of Control Barrier Function (CBF) parameters, i.e., of the class K functions included in the CBF condition, under input constraints. We introduce the concept of locally validated CBF parameters, which are adapted online to guarantee finite-horizon safety, based on conditions derived from Nagumo's theorem and tangent cone analysis. To identify these parameters online, we integrate a learning-based approach with an uncertainty-aware verification process that account for both epistemic and aleatoric uncertainties inherent in neural network predictions. Our method is demonstrated on a VTOL quadplane model during challenging transition and landing maneuvers, showcasing enhanced performance while maintaining safety. - Reformulations of Quadratic Programs for Lipschitz ContinuityOptimization-based controllers often lack regularity guarantees, such as Lipschitz continuity, when multiple constraints are present. When used to control a dynamical system, these conditions are essential to ensure the existence and uniqueness of the system's trajectory. Here we propose a general method to convert a cf{QP} into a cf{SOCP}, which is shown to be Lipschitz continuous. Key features of our approach are that (i)~the regularity of the resulting formulation does not depend on the structural properties of the constraints, such as the linear independence of their gradients; and (ii)~it admits a closed-form solution under some assumptions, which is not available for general cp{QP} with multiple constraints, enabling faster computation. We support our method with rigorous analysis and examples.
- Adaptive Ergodic Search with Energy-Aware Scheduling for Persistent Multi-Robot MissionsAutonomous robots are increasingly deployed for long-term information-gathering tasks, which pose two key challenges: planning informative trajectories in environments that evolve across space and time, and ensuring persistent operation under energy constraints. This paper presents a unified framework, mEclares, that addresses both challenges through adaptive ergodic search and energy-aware scheduling in multi-robot systems. Our contributions are two-fold: (1) we model real-world variability using stochastic spatiotemporal environments, where the underlying information evolves unpredictably due to process uncertainty. To guide exploration, we construct a target information spatial distribution (TISD) based on clarity, a metric that captures the decay of information in the absence of observations and highlights regions of high uncertainty; and (2) we introduce Robustmesch (Rmesch), an online scheduling method that enables persistent operation by coordinating rechargeable robots sharing a single mobile charging station. Unlike prior work, our approach avoids reliance on preplanned schedules, static or dedicated charging stations, and simplified robot dynamics. Instead, the scheduler supports general nonlinear models, accounts for uncertainty in the estimated position of the charging station, and handles central node failures. The proposed framework is validated through real-world hardware experiments, and feasibility guarantees are provided under specific assumptions.
- Certifiably-Correct Mapping for Safe Navigation Despite Odometry DriftRSS 2025
abstract / project page / video / code / arxiv /Accurate perception, state estimation and mapping are essential for safe robotic navigation as planners and controllers rely on these components for safety-critical decisions. However, existing mapping approaches often assume perfect pose estimates, an unrealistic assumption that can lead to incorrect obstacle maps and therefore collisions. This paper introduces a framework for certifiably-correct mapping that ensures that the obstacle map correctly classifies obstacle-free regions despite the odometry drift in vision-based localization systems (VIO/SLAM). By deflating the safe region based on the incremental odometry error at each timestep, we ensure that the map remains accurate and reliable locally around the robot, even as the overall odometry error with respect to the inertial frame grows unbounded. Our contributions include two approaches to modify popular obstacle mapping paradigms, (I) Safe Flight Corridors, and (II) Signed Distance Fields. We formally prove the correctness of both methods, and describe how they integrate with existing planning and control modules. Simulations using the Replica dataset highlight the efficacy of our methods compared to state-of-the-art techniques. Real-world experiments with a robotic rover show that, while baseline methods result in collisions with previously mapped obstacles, the proposed framework enables the rover to safely stop before potential collisions. - meSch: Multi-Agent Energy-Aware Scheduling for Task PersistenceThis paper develops a scheduling protocol for a team of autonomous robots that operate on long-term persistent tasks. The proposed framework, called meSch, accounts for the limited battery capacity of the robots and ensures that the robots return to charge their batteries one at a time at the single charging station. The protocol is applicable to general nonlinear robot models under certain assumptions, does not require robots to be deployed at different times, and can handle robots with different discharge rates. We further consider the case when the charging station is mobile and its state information is subject to uncertainty. The feasibility of the algorithm in terms of ensuring persistent charging is given under certain assumptions, while the efficacy of meSch is validated through simulation and hardware experiments.
- Learning to Refine Input Constrained Control Barrier Functions via Uncertainty-Aware Online Parameter AdaptationIEEE ICRA 2025
abstract / project page / video / code / arxiv /Control Barrier Functions (CBFs) have become powerful tools for ensuring safety in nonlinear systems. However, finding valid CBFs that guarantee persistent safety and feasibility remains an open challenge, especially in systems with input constraints. Traditional approaches often rely on manually tuning the parameters of the class K functions of the CBF conditions a priori. The performance of CBF-based controllers is highly sensitive to these fixed parameters, potentially leading to overly conservative behavior or safety violations. To overcome these issues, this paper introduces a learning-based optimal control framework for online adaptation of Input Constrained CBF (ICCBF) parameters in discrete-time nonlinear systems. Our method employs a probabilistic ensemble neural network to predict the performance and risk metrics, as defined in this work, for candidate parameters, accounting for both epistemic and aleatoric uncertainties. We propose a two-step verification process using Jensen-Renyi Divergence and distributionally-robust Conditional Value at Risk to identify valid parameters. This enables dynamic refinement of ICCBF parameters based on current state and nearby environments, optimizing performance while ensuring safety within the verified parameter set. Experimental results demonstrate that our method outperforms both fixed-parameter and existing adaptive methods in robot navigation scenarios across safety and performance metrics. - Multi-Agent Clarity-Aware Dynamic Coverage with Gaussian ProcessesThis paper presents two algorithms for multi-agent dynamic coverage in spatiotemporal environments, where the coverage algorithms are informed by the method of data assimilation. In particular, we show that by explicitly modeling the environment using a Gaussian Process (GP) model, and considering the sensing capabilities and the dynamics of a team of robots, we can design an estimation algorithm and multi-agent coverage controller that explores and estimates the state of the spatiotemporal environment. The uncertainty of the estimate is quantified using clarity, an information-theoretic metric, where higher clarity corresponds to lower uncertainty. By exploiting the relationship between GPs and Stochastic Differential Equations (SDEs) we quantify the increase in clarity of the estimated state at any position due to a measurement taken from any other position. We use this relationship to design two new coverage controllers, both of which scale well with the number of agents exploring the domain, assuming the robots can share the map of the clarity over the spatial domain via communication. We demonstrate the algorithms through a realistic simulation of a team of robots collecting wind data over a region in Austria.
- gatekeeper: Online Safety Verification and Control for Nonlinear Systems in Dynamic EnvironmentsThis paper presents the gatekeeper algorithm, a real-time and computationally-lightweight method to ensure that nonlinear systems can operate safely in dynamic environments despite limited perception. gatekeeper integrates with existing path planners and feedback controllers by introducing an additional verification step that ensures that proposed trajectories can be executed safely, despite nonlinear dynamics subject to bounded disturbances, input constraints and partial knowledge of the environment. Our key contribution is that (A) we propose an algorithm to recursively construct committed trajectories, and (B) we prove that tracking the committed trajectory ensures the system is safe for all time into the future. The method is demonstrated on a complicated firefighting mission in a dynamic environment, and compares against the state-of-the-art techniques for similar problems.
@inproceedings{agrawal2024gatekeeper, title={gatekeeper: Online safety verification and control for nonlinear systems in dynamic environments}, author={Agrawal, Devansh and Chen, Ruichang and Panagou, Dimitra}, booktitle={{IEEE Transactions on Robotics}, year={2024}, volume={40}, number={}, pages={4358-4375}, organization={IEEE} } - Eclares: Energy-Aware Clarity-Driven Ergodic SearchPlanning informative trajectories while considering the spatial distribution of the information over the environment, as well as constraints such as the robot’s limited battery capacity, makes the long-time horizon persistent coverage problem complex. Ergodic search methods consider the spatial distribution of environmental information while optimizing robot trajectories; however, current methods lack the ability to construct the target information spatial distribution for environments that vary stochastically across space and time. Moreover, current coverage methods dealing with battery capacity constraints either assume simple robot and battery models or are computationally expensive. To address these problems, we propose a framework called Eclares, in which our contribution is two-fold. 1) First, we propose a method to construct the target information spatial distribution for ergodic trajectory optimization using clarity, an information measure bounded between [0, 1]. The clarity dynamics allow us to capture information decay due to a lack of measurements and to quantify the maximum attainable information in stochastic spatiotemporal environments. 2) Second, instead of directly tracking the ergodic trajectory, we introduce the energy-aware (eware) filter, which iteratively validates the ergodic trajectory to ensure that the robot has enough energy to return to the charging station when needed. The proposed eware filter is applicable to nonlinear robot models and is computationally lightweight. We demonstrate the working of the framework through a simulation case study.
@inproceedings{naveed2024eclares, title={Eclares: Energy-aware clarity-driven ergodic search}, author={Naveed, Kaleb Ben and Agrawal, Devansh and Vermillion, Christopher and Panagou, Dimitra}, booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)}, pages={14326--14332}, year={2024}, organization={IEEE} } - Advances in the Theory of Control Barrier Functions: Addressing practical challenges in safe control synthesis for autonomous and robotic systemsThis tutorial paper presents recent work of the authors that extends the theory of Control Barrier Functions (CBFs) to address practical challenges in the synthesis of safe controllers for autonomous systems and robots. We present novel CBFs and methods that handle safety constraints (i) with time and input constraints under disturbances, (ii) with high-relative degree under disturbances and input constraints, and (iii) that are affected by adversarial inputs and sampled-data effects. We then present novel CBFs and adaptation methods that prevent loss of validity of the CBF, as well as methods to tune the parameters of the CBF online to reduce conservatism in the system response. We also address the pointwise-only optimal character of CBF-induced control inputs by introducing a CBF formulation that accounts for future trajectories, as well as implementation challenges such as how to preserve safety when using output feedback control and zero-order-hold control. Finally we consider how to synthesize non-smooth CBFs when discontinuous inputs and multiple constraints are present.
@article{garg2024advances, title={Advances in the Theory of Control Barrier Functions: Addressing practical challenges in safe control synthesis for autonomous and robotic systems}, author={Garg, Kunal and Usevitch, James and Breeden, Joseph and Black, Mitchell and Agrawal, Devansh and Parwana, Hardik and Panagou, Dimitra}, journal={Annual Reviews in Control}, volume={57}, pages={100945}, year={2024}, publisher={Elsevier} } - Safety-Aware Trajectory Tracking using High-Order Control Barrier FunctionsIEEE CDC 2024
abstract /A novel method on safety-aware trajectory tracking is presented in this work. The proposed scheme employs Control Barrier Functions (CBFs) along with a latent-space representation over the barrier and its derivatives. While such representations have also been employed in the literature, the innovation of the proposed method lies in formulating a novel tracking problem on the latent space, enabling for the shape of linear class−K functions that are included in the safety condition induced by the control barrier function to be chosen constructively. Additionally, the aforementioned safety condition is modified based on the latent-space tracking through the addition of an extra term. This enables maintaining safety guarantees and is demonstrated to provide decreased tracking errors compared to the nominal control barrier function scheme. Numerical simulations validate the efficacy of the proposed scheme, as well as its ability for online implementation. - gatekeeper: Online safety verification and control for nonlinear systems in dynamic environmentsThis paper presents the gatekeeper algorithm, a real-time and computationally-lightweight method to ensure that nonlinear systems can operate safely in dynamic environments despite limited perception. gatekeeper integrates with existing path planners and feedback controllers by introducing an additional verification step that ensures that proposed trajectories can be executed safely, despite nonlinear dynamics subject to bounded disturbances, input constraints and partial knowledge of the environment. Our key contribution is that (A) we propose an algorithm to recursively construct committed trajectories, and (B) we prove that tracking the committed trajectory ensures the system is safe for all time into the future. The method is demonstrated on a complicated firefighting mission in a dynamic environment, and compares against the state-of-the-art techniques for similar problems.
@inproceedings{agrawal2023gatekeeper, title={gatekeeper: Online safety verification and control for nonlinear systems in dynamic environments}, author={Agrawal, Devansh and Chen, Ruichang and Panagou, Dimitra}, booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, pages={259--266}, year={2023}, organization={IEEE} } - Sensor-based Planning and Control for Robotic Systems: Introducing Clarity and PerceivabilityIn this letter, we first introduce an information measure, termed clarity , motivated by information entropy, and show that it has intuitive properties relevant to dynamic coverage control and informative path planning. Clarity defines on a scale of [0,1] the quality of the information that we have about a variable of interest in an environment. Clarity lower bounds the expected estimation error of any estimator, and is used as the information metric in the notion of perceivability , which is defined later on and is the primary contribution of this letter. Perceivability captures whether a given robotic (or more generally, sensing and control) system has sufficient sensing and actuation capabilities to gather desired information about an environment. We show that perceivability relates to the reachability of an augmented system, which encompasses the robot dynamics and the clarity about the environment, and we derive the corresponding Hamilton-Jacobi-Bellman equations. Thus, we provide an algorithm to measure an environment’s perceivability, and obtain optimal controllers that maximize information gain. In simulations, we demonstrate how clarity is a useful concept for planning trajectories, how perceivability can be determined using reachability analysis, and how a Control Barrier Function controller can be used to design controllers to maintain a desired level of information.
@article{agrawal2023sensor, title={Sensor-based planning and control for robotic systems: Introducing clarity and perceivability}, author={Agrawal, Devansh R and Panagou, Dimitra}, journal={IEEE Control Systems Letters}, year={2023}, publisher={IEEE} } - A Constructive Method for Designing Safe Multirate Controllers for Differentially-Flat SystemsThis paper introduces the notion of an Input Constrained Control Barrier Function (ICCBF), as a method to synthesize safety-critical controllers for nonlinear 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.
@ARTICLE{9655322, author={Agrawal, Devansh R. and Parwana, Hardik and Cosner, Ryan K. and Rosolia, Ugo and Ames, Aaron D. and Panagou, Dimitra}, journal={IEEE Control Systems Letters}, title={A Constructive Method for Designing Safe Multirate Controllers for Differentially-Flat Systems}, year={2022}, volume={6}, number={}, pages={2138-2143}, doi={10.1109/LCSYS.2021.3136465} } - Safe and robust observer-controller synthesis using control barrier functionsThis letter addresses the synthesis of safety-critical controllers using estimate feedback. We propose an observer-controller interconnection to ensure that the nonlinear system remains safe despite bounded disturbances on the system dynamics and measurements that correspond to partial state information. The co-design of observers and controllers is critical, since even in undisturbed cases, observers and controllers designed independently may not render the system safe. We propose two approaches to synthesize observer-controller interconnections. The first approach utilizes Input-to-State Stable observers, and the second uses Bounded Error observers. Using these stability and boundedness properties of the observation error, we construct novel Control Barrier Functions that impose inequality constraints on the control inputs which, when satisfied, certifies safety. We propose quadratic program-based controllers to satisfy these constraints, and prove Lipschitz continuity of the derived controllers. Simulations and experiments on a quadrotor demonstrate the efficacy of the proposed methods.
@article{agrawal2022safe, title={Safe and robust observer-controller synthesis using control barrier functions}, author={Agrawal, Devansh R and Panagou, Dimitra}, journal={IEEE Control Systems Letters}, volume={7}, pages={127--132}, year={2022}, publisher={IEEE} } - Control Barrier Functions in Sampled-Data Systems.This letter presents conditions for ensuring forward invariance of safe sets under sampled-data system dynamics with piecewise-constant controllers and fixed time-steps. First, we introduce two different metrics to compare the conservativeness of sufficient conditions on forward invariance under piecewise-constant controllers. Then, we propose three approaches for guaranteeing forward invariance, two motivated by continuous-time barrier functions, and one motivated by discrete-time barrier functions. All proposed conditions are control affine, and thus can be incorporated into quadratic programs for control synthesis. We show that the proposed conditions are less conservative than those in earlier studies, and show via simulation how this enables the use of barrier functions that are impossible to implement with the desired time-step using existing methods.
@article{DBLP:journals/csysl/BreedenGP22, author = {Joseph Breeden and Kunal Garg and Dimitra Panagou}, title = {Control Barrier Functions in Sampled-Data Systems}, journal = {{IEEE} Control. Syst. Lett.}, volume = {6}, pages = {367--372}, year = {2022}, url = {https://doi.org/10.1109/LCSYS.2021.3076127}, doi = {10.1109/LCSYS.2021.3076127}, timestamp = {Tue, 13 Jul 2021 13:26:13 +0200}, biburl = {https://dblp.org/rec/journals/csysl/BreedenGP22.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } - Multi-Rate Control Design Under Input Constraints via Fixed-Time Barrier Functions.In this letter, we introduce the notion of periodic safety, which requires that the system trajectories periodically visit a subset of a forward-invariant safe set, and utilize it in a multi-rate framework where a high-level planner generates a reference trajectory that is tracked by a low-level controller under input constraints. We introduce the notion of fixed-time barrier functions which is leveraged by the proposed low-level controller in a quadratic programming framework. Then, we design a model predictive control policy for high-level planning with a bound on the rate of change for the reference trajectory to guarantee that periodic safety is achieved. We demonstrate the effectiveness of the proposed strategy on a simulation example, where the proposed fixed-time stabilizing low-level controller shows successful satisfaction of control objectives, whereas an exponentially stabilizing low-level controller fails.
@article{DBLP:journals/csysl/GargCRAP22, author = {Kunal Garg and Ryan K. Cosner and Ugo Rosolia and Aaron D. Ames and Dimitra Panagou}, title = {Multi-Rate Control Design Under Input Constraints via Fixed-Time Barrier Functions}, journal = {{IEEE} Control. Syst. Lett.}, volume = {6}, pages = {608--613}, year = {2022}, url = {https://doi.org/10.1109/LCSYS.2021.3084322}, doi = {10.1109/LCSYS.2021.3084322}, timestamp = {Thu, 16 Sep 2021 18:02:01 +0200}, biburl = {https://dblp.org/rec/journals/csysl/GargCRAP22.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } - Safe Control Synthesis via Input Constrained Control Barrier FunctionsThis paper introduces the notion of an Input Constrained Control Barrier Function (ICCBF), as a method to synthesize safety-critical controllers for nonlinear 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.
@INPROCEEDINGS{9682938, author={Agrawal, Devansh R. and Panagou, Dimitra}, booktitle={2021 60th IEEE Conference on Decision and Control (CDC)}, title={Safe Control Synthesis via Input Constrained Control Barrier Functions}, year={2021}, volume={}, number={}, pages={6113-6118}, doi={10.1109/CDC45484.2021.9682938} }