Over the past several decades, power electronic converters have become a key enabler of electrification across nearly all energy, mobility, and industrial sectors. Their widespread integration has driven significant gains in productivity and efficiency while fundamentally reshaping the dynamics of power grids and industrial processes. Modern power systems increasingly rely on converter-based technologies, which, due to their fast dynamics, reduce system inertia and consequently diminish inherent stability margins. At the same time, the transition toward renewable and distributed generation has introduced greater variability and uncertainty in the power supply. In parallel, industrial processes have evolved toward more modular and flexible architectures through their interface with power electronic converters. This transformation has led to enhanced controllability and responsiveness but also to increased system complexity and reduced robustness. This article explores the challenges and opportunities associated with operating power electronic converters in complex electric networks and industrial scenarios, with a particular focus on the role of automatic control in achieving optimal, reliable, and sustainable system performance.
Tactile sensing provides robots with direct access to physical interactions at the contact interface, making it a critical modality for robust and dexterous manipulation. Over the past several decades, tactile-based manipulation research has evolved from mechanics-driven approaches, which explicitly interpret tactile signals through contact mechanics and task-specific reasoning, to data-driven approaches that learn tactile representations and manipulation policies directly from data. This paper reviews the development of tactile-based manipulation from these two complementary perspectives. We first introduce the fundamentals of tactile sensing, and then review, from a mechanics-driven perspective, how those signals form manipulation-relevant state representations and subsequently influence the robot action. We then examine the recent advances in data-driven tactile manipulation, covering imitation learning, reinforcement learning, vision-language-action models, and predictive models. Finally, we discuss open challenges and future opportunities toward scalable, generalizable, and robust tactile-enabled robotic systems.
This survey explores the transformative impact of robotics on intralogistics, driven by supply chain complexities and evolving consumer demands. It reviews various robotic systems alongside foundational algorithms for their operation, such as simultaneous localization and mapping, diverse path planning strategies, and advanced perception/manipulation techniques. An important focus is put on multirobot system coordination, task allocation, and fleet management in logistics. The article then examines human–robot collaboration and relevant safety standards. Finally, it identifies key challenges for future development, including interoperability, advanced AI integration, scalability, robustness in dynamic environments, and economic barriers to adoption.
The increasing use of neural networks (NNs) in control systems for robots, autonomous systems, and other safety-critical applications will demand confidence in their safety, stability, and robustness. The aim of this article is to review recently developed NN model structures (both static and dynamic) with built-in certification of robustness in various forms, including bounded sensitivity to adversarial disturbances (Lipschitzness), dynamical stability, and robust invertibility. In doing so, we aim to help strengthen connections between control theory and machine learning. We present a unifying overview of tools to guarantee such robustness properties while retaining model expressivity and computational efficiency of training and inference. Expressivity is maintained via sophisticated certification methods building on the theory of integral quadratic constraints in robust control, while computational efficiency is maintained via direct parameterizations, which enable learning of robust models via standard unconstrained optimization methods such as gradient descent, without any auxiliary constraints or projections. We then show how such robust NN models can be used in building blocks within architectures for learning control system components such as physics-informed nonlinear observers (state estimators) with guaranteed convergence, control policy parameterizations with guaranteed stability and robustness, and Lyapunov functions and their variants, such as storage and value functions.
This survey reviews recent developments in fault diagnosis for both linear and nonlinear dynamical systems, covering model-based and data-driven approaches as well as passive and active detection and estimation methods. A central focus is placed on the geometric interpretation of diagnosis filters and their connection to the concept of behavioral sets, providing an intuitive view of their performance. We also review optimization-based techniques that enhance the robustness of linear filters when applied to nonlinear or uncertain systems. Furthermore, we point out recent progress in active fault diagnosis, where input design plays a key role in improving detectability and estimation accuracy. To bridge theory and practice, we include a set of real-world industrial applications that demonstrate the implementation and effectiveness of these methods in realistic settings.
