Detecting faults in closed-loop control systems is inherently challenging, as feedback mechanisms may obscure or delay the observation of fault effects. Advanced and robust fault detection tools are critical for ensuring safety and reliability in engineering systems. To address this challenge, we propose a physics-informed data-driven framework for designing informative inferential sensors. Using symbolic regression, a genetic programming (GP) approach generates mathematical expressions that effectively represent system parameters, even with limited training datasets. These expressions are optimized using statistical estimation theory, specifically the D-optimality criterion, to achieve enhanced sensitivity to faults. To validate the proposed approach, we integrate an extended Kalman filter (EKF) for fault estimation within a closed-loop dynamic model of a plate-fin cross-flow heat exchanger. The system is evaluated under varying levels of parameter uncertainty and measurement noise variance. The effectiveness and robustness of the developed methodology are demonstrated by comparing the EKF performance, with and without the inclusion of inferential sensor data alongside physical sensor measurements. The results demonstrate that the inferential sensor significantly enhances fault detection accuracy and reliability.

This article investigates how to achieve resilience, safety, and string stability simultaneously for vehicle platoons under false data injection (FDI) attacks. We utilize the resilient control barrier function-quadratic programming (CBF-QP) framework to formulate the control objective. Specifically, a network-redundancy-based resilient controller serves as the nominal controller to ensure internal stability, while safety and string stability are characterized by control barrier function (CBF) constraints for control inputs optimization. We introduce a novel concept of boundary robustness to preserve the validity of CBF constraints by incorporating a robustness margin under bounded attacks. As strict enforcement of multiple CBFs may cause controller infeasibility, we improve quadratic programming (QP) feasibility via parameter predesign and a feasibility-CBF with bounded adaptive relaxation. Our analysis reveals conflicts among resilience, safety, and string stability, and we propose a conditional activation-relaxation approach to achieve tradeoffs among them. Comparative simulations and experiments involving four TurtleBot3s are conducted to demonstrate the effectiveness of the proposed method.

In this article, we address aquatic environmental monitoring using a fleet of uncrewed surface vehicles (USVs). Specifically, we develop an online path generator that provides either circular or elliptic paths based on the real-time feedback so that the USVs efficiently sample the sensor data over given aquatic environment. To this end, we begin by formulating a novel online path generation problem for a group of Dubins vehicles in the form of cost minimization based on the formulation of persistent coverage control. We then transform the cost minimization into a constraint-based specification so that a prescribed performance level is certified. An online coverage path generator is then designed based on the so-called constraint-based control in order to meet the performance certificate together with additional constraints inherent in the parameters that specify the paths. It is also shown there that the present constraint-based approach allows one to drastically reduce the computational complexity stemming from combinations of binary variables corresponding to the turning directions of the USVs. The present coverage path generator is finally demonstrated through simulations and experiments on an original testbed of multiple USVs.

Aggregators of energy resources providing balancing services to the grid face the twofold challenge of assessing the overall amount of flexibility of the pool of energy resources, and mapping the power requests by the grid back to each single energy resource during the service window. In this article, we propose a framework that allows to jointly optimize the power flexibility limits and determines the disaggregation policy for managing the pool of energy resources, thus avoiding an additional computational step in the operational service phase. The baseline power exchange profiles of the energy resources can also be optimized to enhance the pool flexibility, and practical constraints such as time availability of the resources within the service window or network congestion constraints can be included. Notably, the resulting optimization problem is amenable for privacy-preserving and scalable distributed resolution schemes.

In the collaborative application of multiple autonomous underwater vehicles (AUVs), collision avoidance is essential for ensuring the physical safety of AUVs, while a specific connectivity topology is required for information exchange. This dual requirement presents challenges for controller synthesis. Thus, this study focuses on collision-free connectivity topology switching and maintenance during collaborative missions. For the primary control goal of reaching predefined locations, a backstepping-based controller is designed to ensure system stability. Collision avoidance is achieved through the application of high-order zeroing control barrier functions (ZCBFs), which guarantee the forward invariance of the desired safety set. To facilitate high-bandwidth information exchange, establishing a specific connectivity topology within an effective range is necessary. High-order finite-time convergence control barrier functions (FCBFs) are employed to form new topologies within finite time and then maintain the forward invariance of the desired set. The controller is synthesized using quadratic programming optimization, incorporating collision-free topological constraints and minimally modifying the backstepping-based controller. Numerical simulations and field experimental results demonstrate the effectiveness of the proposed controller, showing that topology switches faster than in existing studies while ensuring collision avoidance under disturbances.

Designing safety-critical controllers for acceleration-controlled unicycle robots is challenging, as control inputs may not appear in the constraints of control Lyapunov functions (CLFs) and control barrier functions (CBFs), leading to invalid controllers. Existing methods often rely on state-feedback-based CLFs and high-order CBFs (HOCBFs), which are computationally expensive to construct and fail to maintain effectiveness in dynamic environments with fast-moving, nearby obstacles. To address these challenges, we propose constructing velocity obstacle (VO)-based CBFs (VOCBFs) in the velocity space to enhance dynamic collision avoidance capabilities, instead of relying on distance-based CBFs that require the introduction of HOCBFs. Additionally, by extending VOCBFs using variants of VO, we enable reactive collision avoidance between robots. We formulate a safety-critical controller for acceleration-controlled unicycle robots as a mixed-integer quadratic programming (MIQP), integrating state-feedback-based CLFs for navigation and VOCBFs for collision avoidance. To enhance the efficiency of solving the MIQP, we split the MIQP into multiple suboptimization problems and employ a decision network to reduce computational costs. Numerical simulations demonstrate that our approach effectively guides the robot to its target while avoiding collisions. Compared to HOCBFs, VOCBFs exhibit significantly improved dynamic obstacle avoidance performance, especially when obstacles are fast moving and close to the robot. Furthermore, we extend our method to distributed multirobot systems.

We propose a new Q-learning-based air-fuel ratio (AFR) controller for a Wankel rotary engine. We first present a mean-value engine model (MVEM) that is modified based on the rotary engine dynamics. The AFR regulation problem is reformulated as an optimal proportional-integral (PI) controller for fuel tracking over the augmented error dynamics. Leveraging the generalized-Hamilton-Jacobi–Bellman (GHJB) equation, we propose a new definition of the Q-function with its arguments being the augmented error and the injected fuel flow rate. We then derive its Q-learning Bellman (QLB) equation based on the optimality principle. This allows online learning of a controller via an adaptive critic network for solving the QLB equation, of which the solution satisfies the GHJB equation. The proposed model-free Q-learning-based controller is implemented on an AIE 225CS Wankel engine, where the practical experiments validate the optimality and performance of the proposed controller.

Partial least squares (PLS) is a well-known multivariate statistical process monitoring (MSPM) method. However, there are two key issues that restrict its application in reconstruction-based fault diagnosis, including weak fault representation ability and uncertainty caused by the overlap among different types of faults. To cope with these two issues, an enhanced kernel PLS (eKPLS) fault reconstruction approach fused with pattern classification is proposed in this study. For the first issue, a fine-grained fault subspace extraction method is developed. These fine-grained fault subspaces exhibit richer fault details, conferring upon the model heightened fault representation ability. For the second issue, the fault magnitude is supplemented into the paradigm of fault reconstruction, and a rediagnosis mechanism is designed to overcome the uncertainty in fault diagnosis. Thus, the ideas of fault reconstruction and pattern classification are fused into a consolidated framework, allowing us to complement their strengths together. Finally, several experiments on a hydraulic system and Tennessee Eastman process (TEP) are performed to validate the effectiveness of the proposed method.

The series elastic actuator (SEA) has been widely used in the exoskeletons. However, its mathematical model contains both unknown parameters and states, which need to be jointly estimated online via the measurable input and output. To address this issue, an adaptive observer is developed to reconstruct both unknown model parameters and states simultaneously. In particular, a constructive approach is proposed to extract the parameter estimation error, which is used to design a new adaptive law decoupled from the observer error dynamics. A high-gain modification is also incorporated into the observer to handle the dependency on the unknown system states in the regressor. As a result, the convergence of both the observation error and the estimation error can be proved under the standard excitation condition. Moreover, since the SEA used in the exoskeletons may operate under a weak excitation due to the slow training of rehabilitation, a further tailored adaptive law with the eigenvalue decomposition method is introduced to adapt the weak excitation. Finally, the effectiveness of the proposed methods is validated via experiments on a real-world SEA test rig.

Thruster control allocation (TCA) is a key functionality for many spacecraft, with a significant impact on control performance, propellant consumption, and fault tolerance. Propellant-optimal solutions are desirable and are either based on onboard numerical optimization, or explicit optimization via the use of offline-generated look-up tables (LUTs). This article proposes a TCA and modulation method of the latter type by using multiparametric programming and presents a novel fast LUT evaluation algorithm. Fault tolerance and the handling of non-attainable control commands with full controllability exploitation are also addressed. Furthermore, the solution is extended to include the non-convex minimum impulse bit (MIB) constraint, where the proposed solution can find the global optimum. The use of this constraint is demonstrated in a close-range orbital rendezvous scenario, yielding significant improvements to the performance of boosts, forced motions, and station-keeping maneuvers, at the cost of greater propellant consumption and computation time. Results in consumer hardware for a12-thruster configuration show a worst case onboard computation time of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7~\mu $ </tex-math></inline-formula>s and 0.5 ms for the cases without and with the MIB constraint, which are up to two orders of magnitude lower than those for numerical optimization with a state-of-the-art optimizer. The proposed onboard algorithms are simple, non-iterative, and have worst case computational effort guarantees.