Safety-critical control systems, such as autonomous vehicles and robotics, must operate reliably in complex, dynamic environments where ensuring both stability and strict adherence to safety constraints is essential. Here, safety refers to maintaining state forward invariance within a desired safe set. The control barrier function (CBF)-based safety-critical control (CBF-SC) has emerged as a promising framework, enabling control systems to maintain states within specified forward invariant sets. However, real-world applications often encounter undesirable disturbances and uncertainties, complicating the implementation of CBF and potentially compromising safety guarantees. This article provides a comprehensive survey of recent advances in CBF-based safety-critical control, with a particular focus on handling disturbances and uncertainties. The article begins with an exploration of the foundational principles of the control Lyapunov function and CBF, then reviews and compares strategies to improve robustness in CBF-SC systems, drawing on robust control, adaptive control, and disturbance/uncertainty estimation and attenuation. The comparison between CBF-SC and other related methods is also explored. Advanced mechatronic applications are also reviewed, showcasing how CBFs are implemented in autonomous vehicles, robotics, industrial automation systems, and energy platforms. Finally, future research directions are provided in the area to trigger further discussion and investigation.

The coordinated motion of the trolley and hoisting rope improves crane flexibility but poses challenges in precise trajectory conversion and tracking due to disturbances and inaccessible low-level controllers. This letter proposes a disturbance-aware high-precision trajectory planning method integrating a high-order disturbance observer (HODOB), an actuated trajectory generation (ATG) module, and a high-precision trajectory modifier (HPTM). The HODOB estimates disturbances and their derivatives; the ATG leverages differential flatness with disturbance compensation for accurate conversion; and the HPTM, based on a composite state control barrier function (CSCBF), can modify the reference trajectory in real time to ensure strict safety-constrained tracking. Experiments demonstrate that the proposed method achieves high-precision trajectory conversion and tracking under both nominal and disturbed conditions.

The hydrogen circulation system (HCS) with a circulation pump improves the percentage of hydrogen utilization, efficiency, and peak power of proton exchange membrane fuel cells (PEMFCs) by regulating the hydrogen excess ratio (HER) and the supply manifold pressure (SMP). However, achieving reliable synchronous control of HER and SMP remains challenging due to the inherent nonlinearities and uncertainties in the HCS. In this study, a multiple-input-multiple-output (MIMO) resilient regulation strategy based on the control barrier function (CBF) is presented to address these challenges. First, taking into account the MIMO coupling nonlinearities and parameter uncertainties of the circulation pump, two baseline adaptive controllers are designed, in which the controller gains and adaptive law are derived utilizing a bound estimation method to ensure stability. Subsequently, a prescribed performance control (PPC) approach formulated using the CBF guarantees the desired convergence rates and tracking error bounds for the HER and SMP. This is achieved by modulating the baseline adaptive controllers (BACs)-based control inputs via a quadratic programming (QP) policy. Finally, the robust safety of the CBF-based controllers is proved by quantifying the estimation-error bounds. Compared with the baseline controller, the proposed approach is validated through a hardware-in-the-loop (HIL) experiment using a high-fidelity model, demonstrating reductions of 7% in HER overshoot (OT) and 79 % in its root-mean-square error (RMSE) under varying load currents.

In this article, a composite current-constrained fixed-time control approach is proposed based on the adaptive fixed-time disturbance observer (AFTDO) for dc-dc converters of hybrid energy storage systems (HESS) to regulate the bus voltage so that its desired reference is tracked within a fixed time. As mismatched disturbances can severely impact the system control effect, AFTDO is designed to provide the disturbance estimation and mitigate parameter uncertainties and load disturbances, which accelerates the estimation rate and enhances system robustness. A current-constrained fixed-time controller is then proposed to regulate the duty ratio such that the HESS possesses fixed-time convergence and achieves desired voltage tracking within a fixed time independent of initial states, where a current constraint gain is developed to strictly guarantee overcurrent protection. Moreover, a fixed-time load estimator is presented to enhance system robustness against external unknown loads. The closed-loop HESS fixed-time stability is rigorously proven based on the Lyapunov theory. A 420-V/3-kW scaled-down hardware platform is implemented to validate the proposed controller. Simulation and experimental results demonstrate the feasibility and superiority of the proposed approach under various fluctuating conditions.

We propose PRED-MPPI, the first MPPI variant that seamlessly integrates real-time disturbance preview and adaptive discretization for quadrotor tracking control under significant model inaccuracies and time-varying disturbances. Unlike prior MPPI variants (e.g., mathcal{L}_1-MPPI, DA-MPPI), which assume constant or matched disturbances, PRED-MPPI leverages a high-order Generalized Extended State Observer for disturbance preview and a Variable Discretization Grid (VDG) to reduce computation and control variance. The synergy enables real-time (50 Hz) quadrotor control under time-varying and mismatched disturbances. Extensive comparative simulation and real-world Crazyflie experiments demonstrate substantial performance gains. In AirSim simulation, PRED-MPPI reduces computation time by over 30%, and mean RMSE by 10.3%, 13.5%, and 14.6% compared to baseline MPPI, and by 2.59%, 3.62%, and 5.80% compared to DA-MPPI across three representative scenarios. In real-world Crazyflie experiments, for ground-effect-disturbed hovering, PRED-MPPI reduces mean and standard deviation (Std) of X–Y plane error by 14.2%/17.9% and 6.03%/21.6% compared to MPPI and DA-MPPI; for fan-induced wind experiments, PRED-MPPI yields improvements of 23.4%/36.8% and 13.8%/25.0% in RMSE and tracking error Std. These results establish PRED-MPPI as the first disturbance-preview MPPI achieving real-world UAV robustness and efficiency, paving the way for deployment on resource-limited robotic platforms. GitHub page with videos is at https://pred-mppi.github.io/

Although noncascaded speed control frameworks enable fast dynamic responses, the speed regulation performance of permanent magnet synchronous motors (PMSMs) is often degraded by external loads and uncertain disturbances. This results in a critical coupling between current safety and control performance. This article develops a novel robust adaptive control barrier function (RACBF)-based model predictive speed control for PMSM drives with current safety guarantees. First, an offset-free single-loop model predictive speed controller without current constraint is designed to obtain an enhanced control performance. Then, a novel current-constrained RACBF is devised to strictly enforce current safety under disturbances without relying on overly conservative compensation, while dynamically balancing control performance based on the proximity to current limits. By embedding the RACBF constraint into the model predictive control (MPC) scheme, a synthesized model predictive speed controller is formulated. Experimental results verify that the proposed approach ensures strict current safety under disturbances while delivering superior transient performance, enhanced robustness, and reduced computational complexity.

Control Barrier Function (CBF) based quadratic programs (QPs) have become an effective method for enforcing safety in safety-critical systems and robotics. However, these methods often suffer from infeasibility or overly conservative relaxations when handling multiple constraints, potentially compromising safety. In this paper, we propose a hierarchical framework called “Safety-first" for control design, which simultaneously incorporates performance objectives formulated using Control Lyapunov Functions (CLFs), and safety guarantees via CBFs with input constraints. Unlike existing approaches, the proposed method guarantees solution feasibility while achieving improved performance, and it is scalable to an arbitrary number of CBF constraints. This scalability enables more precise and flexible representation of complex safety requirements using multiple simple CBFs. For application to mobile robot navigation, we employ Constrained Delaunay Triangulation (CDT) to construct multiple CBFs that approximate irregularly-shaped obstacles. Real-world experiments in cluttered and dynamic environments demonstrate that the Safety-first algorithm achieves safe navigation, validating both the theoretical guarantee and practical advantages over existing methods.

This article presents a persistent-excitation (PE)-constrained, dual-control enabled model predictive control (DCMPC) framework for auto-optimization control problems in uncertain environments. A novel uncertainty identification method, multi-innovation stochastic ensemble-based gradient descent (MISEG), has been proposed that leverages historical ensemble feedback to robustly estimate uncertainties from rewards in an uncertain environment, thereby overcoming the limitations of traditional ensemble-based methods in learning speed and accuracy. A proactive PE strategy is implemented to drive the system toward informative states, complementing the dual-control objectives of exploration and exploitation, beyond conventional noise injection or input manipulation-based excitation. The joint mechanism of exploration and exploitation provides a reciprocal learning and control paradigm for systems operating in unknown/uncertain environments by inherently balancing trajectory tracking and uncertainty identification. Theoretical guarantees on convergence and feasibility of the proposed method are provided. The experimental studies based on a 2-rotor platform further demonstrate the effectiveness and superiority of the proposed method in comparison with existing approaches under the considered experimental environment.

DC-DC buck converters play a critical role in power conversion systems, where precise voltage regulation under strict current limitations is essential. To achieve this control objective, in this paper, two discrete-time disturbance observers are first designed to estimate both matched and unmatched disturbances, including input voltage fluctuations, load variations, and parameter uncertainties in the inductor and capacitor. Following that, a discrete-time current-constrained controller is proposed combining with disturbance estimation. This controller employs a composite feedback structure to ensure system stability while incorporating the control barrier function technology to explicitly enforce inductor current constraints under disturbances. Moreover, the discrete-time formulation of the proposed control strategy facilitates straightforward digital implementation. A rigorous robust stability analysis of the closed-loop system is conducted, and experimental results validate the effectiveness of the proposed approach.

Maintaining a stable anode pressure and hydrogen excess ratio (HER) in the hydrogen supply system (HSS) is critical towards the operational efficiency and reliability of a PEM fuel cell. In this paper, we present a novel multiple-input multiple-output (MIMO) double-loop controller based on an active inference control (AIC) approach for coordinated regulation of pressure and HER in the HSS. An HSS model is first established, incorporating a segmented anode model to approximate the stack’s distributed dynamics. Inspired by the neuroscientific free energy principle, the AIC is formulated in a variational Bayesian framework to address the pronounced nonlinearities inherent in the stack pressure and pump flow dynamics. The AIC serves as the outer-loop controller, in which a goal-directed conscious perception mechanism is designed based on a probabilistic generative model to infer system states and proactively predict their higher-order dynamics. By minimizing the free energy function, the AIC achieves robust and offset-free tracking of anode pressure and HER setpoints without requiring an accurate physical plant model, while dynamically generating reference values for the inner loop. The MIMO inner-loop controller employs a nonlinear state-feedback strategy to manage the well-established dynamics of the manifold pressure and pump motor response. Compared with existing controllers, our method mitigates transient overshoot in both anode pressure and HER, while concurrently reducing the respective root mean square errors by 88.1% and 38.2%. Hardware-in-the-loop experiments are conducted to validate the effectiveness of the proposed method.

For servo mechanisms subject to system uncertainties and external disturbances, one fundamental problem is the effective management of the effects induced by these uncertainties. This article proposes a composite estimation and control method that allows for asymptotic convergence of tracking and estimation errors simultaneously. Specifically, the convergence rate of the error dynamics is improved by embedding the error feedback term into the reference model, rather than the conventional approach of incorporating it into the controller directly. Furthermore, the proposed control architecture is structured into nominal control and uncertainty compensation. The nominal controller is designed to direct all uncertainties into the error dynamics, allowing uncertainty estimation based on the error dynamics rather than the full system model. Additionally, the differential error dynamics is transformed into an equivalent algebraic equation, upon which a novel uncertainty estimator is developed. This reformulation eliminates the need for state derivative information and mitigates the noise amplification and signal oscillation typically induced by time delays or additional filtering processes. The proposed method is validated through the implementation of a compliant servo system, where both simulation studies and experimental evaluations consistently demonstrate its effectiveness.

This article considers the problem of output feedback-based decentralized periodic event-triggered control (PETC) for a class of large-scale systems with unknown nonlinear interconnections and measurement delays. When only the delayed sampled-data measurement output is accessible, a novel decentralized high-gain observer is first proposed based on an output predictor for each subsystem. Then, a set of decentralized sampled-data output feedback controllers that are driven by asynchronous periodic event-triggering conditions is developed to globally exponentially stabilize the large-scale systems. With the help of small-gain arguments and feedback domination approach, a rigorous stability analysis shows that there exist some sufficient conditions to ensure the global exponential stability of the overall systems. Different from the sample-and-hold implementation of output information, this article employs the prediction technique to obtain the current output prediction for each subsystem, which in turn effectively compensates for the undesirable effects of measurement delays and information loss. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control method.

In this study, we propose a safety-critical compliant control strategy designed to strictly enforce interaction force constraints during the physical interaction of robots with unknown environments. The interaction force constraint is interpreted as a new force-constrained control barrier function (FC-CBF) by exploiting the generalized contact model and the prior information of the environment, i.e., the prior stiffness and rest position, for robot kinematics. The difference between the real environment and the generalized contact model is approximated by constructing a tracking differentiator, and its estimation error is quantified based on Lyapunov theory. By interpreting strict interaction safety specification as a dynamic constraint, restricting the desired joint angular rates in kinematics, the proposed approach modifies nominal compliant controllers using quadratic programming, ensuring adherence to interaction force constraints in unknown environments. The strict force constraint and the stability of the closed-loop system are rigorously analyzed. Experimental tests using a UR3e industrial robot with different environments verify the effectiveness of the proposed method in achieving the force constraints in unknown environments.

With the increasing penetration of renewable energy resources, weak-grid conditions are becoming increasingly common. As a key power source, large-capacity photovoltaic (LC-PV) systems exhibit inertia-support dynamics with multistage coupling and high-order behavior across multiple time scales, leading to high model complexity and low computational efficiency in stability analysis and control design. To address these issues, this paper considers a grid-connected LC-PV system. First, a full-order small-signal model is developed, and multi-time-scale modes are identified. Then, the full-order model is reduced using singular perturbation theory. Simulation results show that, under typical disturbances, the terminal frequency response of the resulting seventh-order reduced-order model closely matches those of the electromagnetic transient (EMT) simulation model and the full-order model. The proposed approach substantially reduces model complexity while maintaining accuracy and interpretability, thereby improving the efficiency of stability analysis and simulation for grid-connected LC-PV systems under weak-grid conditions.

This paper investigates distributionally robust model predictive control (DR-MPC) with survival-probability-based control barrier function (CBF) for systems subject to stochastic disturbances with unknown probability distributions. In such stochastic settings, safety can no longer be adequately described by a binary safe/unsafe notion and is instead quantified by the survival probability that the system state remains within a prescribed safe set. A survival-probability-based CBF is designed to encode probabilistic safety specifications within the CBF framework, and the resulting constraints are embedded into an MPC scheme. To address the lack of exact distributional information, a distributionally robust optimization formulation with moment-based ambiguity sets is adopted, yielding a tractable deterministic reformulation of the survival-probability-based CBF. A numerical simulation demonstrates the effectiveness of the proposed approach and its improved trade-off between safety and control performance compared with existing methods.

This article introduces a novel framework for reactive control in dual-arm cooperative robotic systems, addressing the significant challenges posed by high-dimensional, nonconvex optimization demands, intricate kinematic, multimodal distribution, the need for precise, and synchronized coordination. The core of our approach is a two-stage sampling-based model predictive control, which integrates k-means, dual quaternion, and null space into a cohesive system. This integration enhances the system's ability to manage complex coordination tasks, such as obstacle avoidance and holding a water cup, while mitigating risks associated with local optima and reducing control jitter. Our framework not only improves performance and reliability, but also overcomes the traditional computational bottlenecks inherent in dual-arm coordination. These advancements are validated through extensive simulations and experiments, demonstrating the robustness and efficiency of our proposed methodology.

The rubber-tired container gantry crane (RTG) is a type of heavy-duty lifting equipment commonly used in container yards, which is driven by two-side rubber tires and steered via differential drive. While moving along the desired path, the RTG must remain centered of the lane with restricted heading angle, as deviations may compromise the safety of subsequent yard operations. Due to its underactuated nature and the presence of external disturbances, achieving accurate lane-keeping poses a significant control challenge. To address this issue, a robust safety-critical steering control strategy integrating disturbance rejection vector field (VF) with a new state-interlocked control barrier function (SICBF) is proposed. The strategy initially employs a VF path-following method as the nominal controller. By strategically shrinking the safe set, the SICBF overcomes the limitations of traditional CBFs, such as state coupling in the inequality verification and infeasibility when the control coefficient tends to zero. Furthermore, by incorporating a disturbance observer (DOB) into the quadratic programming (QP) framework, the robustness and safety of the control system are significantly enhanced. Comprehensive simulation and experiment are conducted on a practical RTG with a 40-ton load capacity. To our best knowledge, the proposed method is one of the very few methods that have demonstrated successful application to the practical RTG systems.

A phase control strategy is proposed for suppressing beat frequency oscillations between parallel-connected DC-DC boost converters, where the beat frequency equals the difference between switching frequencies of converters. First, the fundamental hardware and software causes of beat frequency dynamics are analyzed, which naturally leads to a feasible control strategy to suppress the oscillations. Next, a communication independent phase detector is proposed to estimate the beat frequency oscillation phase which is also the PWM carrier phase difference between converters. Notably, the sampling distortion, typically considered harmful in the boost converter controller implementation, is creatively regarded as a phase-information carrier. On this basis, a phase regulator is designed to manipulate the beat frequency oscillation phase into a preset tolerance range. Finally, a comprehensive control framework is constructed, which can be implemented without additional hardware change. Experimental results validate the effectiveness of the proposed control strategy.

This article introduces a novel framework for achieving leader-steered (L-S) rigid formations within a multirobot vehicle system subject to nonholonomic constraints, while considering field-of-view (FOV) constraints. In contrast to the conventional separation-bearing leader-follower model, this framework incorporates a virtual leader model, established through topological and local agent connections. To achieve L-S rigid formations and address FOV constraints, a transformative approach is employed. In addition to forming L-S rigid formations, the framework ensures visibility maintenance between topologically connected vehicles using onboard cameras. This is achieved through the introduction of a continuous and continuously differentiable switching function, crucial in balancing visibility maintenance with formation adjustments, particularly when the global leader traverses trajectory segments with large curvature. To implement the framework, the distributed control protocol and the distributed observer are developed. Numerical simulations and real-world experiments demonstrate the framework’s capability to achieve L-S rigid formations while accommodating FOV constraints, showcasing its practical utility and effectiveness in real-world applications.

This paper develops a dynamic obstacle avoidance approach for mobile robots by combining monocular depth estimation with an improved Artificial Potential Field (APF). Depth maps inferred from single-view RGB images via the SOTA Depth Anything model are fused with YOLO-based object detection to achieve low-cost, calibration-free, real-time target perception, compared against stereo vision and LiDAR based methods. To address failure modes common in monocular navigation, such as ground-obstacle ambiguity and depth noise near humans, the proposed APF incorporates virtual-depth expansion, human-safety filtering, and a lateral repulsive mechanism that improves maneuverability in cluttered and previously unobserved environments. Integrated in a real-time ROS2 pipeline, the system demonstrates reliable human tracking and collision-free navigation in complex scenes, highlighting its practicality for safe operation in unstructured settings. Demonstration videos for this work are available at: https://drive.google.com/drive/folders/1pCFMlwMjSb3mTV3E7ItHfkAeiEmdYvlt?usp=sharing