Abstract
This paper presents a novel Nonlinear Model Predictive Controller (NMPC) architecture for trajectory tracking of omnidirectional robots. The key innovation lies in the method of handling constraints on maximum velocity and acceleration outside of the optimization process, significantly reducing computation time. The controller uses a simplified process model to predict the robot’s state evolution, enabling real-time cost function minimization through gradient descent methods. The cost function penalizes position and orientation errors as well as control effort variation. Experimental results compare the performance of the proposed controller with a generic Proportional-Derivative (PD) controller and a NMPC with integrated optimization constraints. The findings reveal that the proposed controller achieves higher precision than the PD controller and similar precision to the NMPC with integrated constraints, but with substantially lower computational effort. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This article presents the development of a tethered fixed-wing tail-sitter VTOL (Vertical Take-Off and Landing) Unmanned Aerial Vehicle system. The design focuses on improving energy efficiency by utilizing the wings to harness wind power, similar to a kite, while maintaining VTOL functionality. A distinguishing feature is the purpose-built autopilot system, with custom hardware and software components specifically engineered for this application. The study presents the system identification process for obtaining five MIMO (Multiple-Input Multiple-Output) transfer functions that characterize the dynamics between roll-yaw commands and responses, including the tether angle feedback. To address the inherent coupling effects and uncertainties in the system, robust mixed sensitivity (H-infinity) MIMO controllers are developed. The controllers were validated through both simulations and experimental flights, demonstrating effective performance in handling cross-coupling effects and maintaining stability under various operating conditions. According to flight test findings, the system can precisely manage the tether angle while adjusting for ground effect disturbances. This allows for accurate tethered navigation, a stable attitude, and the maintenance of an adequate yaw heading.

Abstract

Abstract
In the era of Industry 4.0, fault tolerance is essential for maintaining the robustness and resilience of industrial systems facing unforeseen or undesirable disturbances. Current methodologies for fault tolerance stages namely, detection, diagnosis, and recovery, do not correspond with the accelerated technological evolution pace over the past two decades. Driven by the advent of digital technologies such as Internet of Things, cloud and edge computing, and artificial intelligence, associated with enhanced computational processing and communication capabilities, local or monolithic centralized fault tolerance methodologies are out of sync with contemporary and future systems. Consequently, these methodologies are limited in achieving the maximum benefits enabled by the integration of these technologies, such as accuracy and performance improvements. Accordingly, in this paper, a collaborative fault tolerance methodology for cyber-physical systems, named Collaborative Fault * (CF*), is proposed. The proposed methodology takes advantage of the inherent data analysis and communication capabilities of cyber-physical components. The proposed methodology is based on multi-agent system principles, where key components are self-fault tolerant, and adopts collaborative and distributed intelligence behavior when necessary to improve its fault tolerance capabilities. Experiments were conducted focusing on the fault detection stage for temperature and humidity sensors in warehouse racks. The experimental results confirmed the accuracy and performance improvements under CF* compared with the local methodology and competitiveness when compared with a centralized approach.

Abstract
The reliability and robustness of cyber-physical systems (CPS) are critical aspects of the current industrial landscape. The high level of autonomous and distributed components associated with a large number of devices makes CPS prone to faults. Despite their importance and benefits, traditional fault tolerance methodologies, namely local and/or centralized, often overlook the potential benefits of collaboration between cyber-physical components. This paper introduces a collaborative fault diagnosis methodology for CPS, integrating self-fault diagnosis capabilities in agents and leveraging collaborative behavior to enhance fault diagnosis. The contribution of this paper relay in propose a methodology for fault diagnosis for CPS, based on multi-agent system (MAS) technology as a backbone of infra-structure, highlighting the components, agent behavior, functionalities, and interaction protocols, to explore the benefits of communication and collaboration between agents. The proposed methodology enhance the accuracy of fault diagnosis when compared with local approach. A case study was conducted in a laboratory-scale warehouse, focusing on diagnosing drift, bias, and precision faults in temperature and humidity sensors. Experimental results reveal that the collaborative methodology significantly outperforms the local approach in fault diagnosis, as evidenced by performance improvements in diagnosis classification. The statistical significance of these results was validated using the Wilcoxon signed-ranks test for paired samples. © 2013 IEEE.

Abstract
Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA* through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm.