The Explicit Reference Governor (ERG) is a simple and systematic method for handling constraints in safety-critical systems that have been pre-stabilized with a nominal controller, by adjusting the applied reference as needed. The core idea is to convert constraints on the state and control input into an upper bound on the magnitude of the Lyapunov function that establishes the stability of the pre-stabilized system. In general, computing this upper bound at each time requires solving an optimization problem, which can be computationally intensive—especially when the constraints are non-convex and/or the Lyapunov function is mathematically complex. This limitation hinders the applicability of ERG in safety-critical systems with limited computational resources. To overcome this challenge, we introduce a neural network–augmented ERG framework that directly approximates the optimization solution and substantially reduces online computational effort while retaining the structure and guarantees of the original ERG. Since the neural network-generated solution inevitably deviates from the exact solution due to approximation errors, a complementary method is introduced to guarantee constraint satisfaction despite these inaccuracies. The proposed methodology is validated experimentally in two real-world safety-critical applications, demonstrating consistent anytime constraint satisfaction and up to a 1283-fold reduction in computational load. These results highlight the practicality, scalability, and effectiveness of the proposed approach for resource-constrained, safety-critical systems.

This article introduces the notion of carelessness level into robot action planners such that the safety and efficiency are optimized. The core idea is to make the robot’s plan less sensitive to the behavior of careless humans who may inattentively violate safety constraints and degrade efficiency. More precisely, our planner reduces the opportunities given to the careless humans to put themselves in danger and hamper the efficiency of the robot’s plan. The effectiveness of the proposed planner is demonstrated through simulation studies on a packaging line and on a collaborative assembly line. Results show that the proposed scheme can improve efficiency and safety in both examples.

The Butterfly Attack, introduced in an RTSS 2019 paper, was billed as a new kind of timing attack against control loops in cyber-physical systems. We conduct a close inspection of the Butterfly Attack in order to identify the root vulnerability that it exploits, and show that an appropriate application of real-time scheduling theory provides an effective countermeasure. We propose improved defenses against this and similar attacks by drawing upon techniques from real-time scheduling theory, control theory, and systems implementation, that are both provably secure and are able to make efficient use of computing resources.

This paper is concerned with the disturbance attenuation problem of fuzzy large-scale systems which consist of N interconnected subsystems which are represented by Takagi Sugeno fuzzy models. Using Lyapunov function and linear matrix inequalities (LMIs), a criterion is proposed to have a prescribed level of disturbance attenuation. A numerical example is given to illustrate the control design procedure and its effectiveness.