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This paper presents an approach for design a continuous-time interval observer for linear parameter varying (LPV) systems in the presence of disturbances that are considered unknown but bounded. The proposed observer is used for the design of a sensor fault detection scheme using the input to state stability (ISS) approach through a Lyapunov quadratic function. The conditions of stability and positivity are presented by a set of linear matrix inequalities (LMIs). The performance of the proposed method is shown in simulation using a case study based on a single-link flexible joint robotic system.

This research analyses the dynamic response of a single-cylinder magnetic reciprocating engine (MRE) and the mathematical model formulation with experimental validation. In efforts to reduce the pollution generated by internal combustion engines (ICE), the MREs have emerged as an alternative to this problem. An MRE uses the ICE structure and replaces fossil fuels with magnetic fields to work with the principle of magnetic repulsion between an electromagnet and a permanent magnet. The work focuses on determining the correct angles of when to energize the electromagnet based on the angular position of the crankshaft concerning the top dead center (TDC) of the engine to generate the magnetic repulsion and obtain the highest possible speed in the engine as in the ICE. The experimental results indicate that the energization angle should be between 45° and 55° before the TDC for generating the highest engine velocity. Using the energization angle of 55° before the TDC allows the engine’s velocities to be between 836 RPM and 840 RPM, and the mathematical model shows a goodness of fit (FIT) of 81.21% throughout its dynamics with the experimental comparison. The present work demonstrates the mathematical model’s ability to represent the MRE’s experimental dynamics and how the magnetic force is formulated and integrated with the mechanical analysis of the engine.

Controlling distributed energy resources (DERs) in low voltage microgrids is a challenging task for operators. The simultaneous operation of independent small-scale DER owners could compromise the operator’s hierarchical and centralized control to reach system stability and cost optimization. Recent Decentralized Energy Management (DEM) approaches provide flexibility for DERs control, but several existing solutions depend on powerful and expensive computer clusters and their ability to deal with a high burden of data in the communication channel. This work is motivated towards a DEM framework that involves independent DER owners while microgrid operator still maintains a hierarchical control philosophy. The framework must include a method to reduce the need of powerful computer clusters and depend on low bandwidth communications channel. Here, a multi-layered framework for every DER, consisting of physical, control, and agent layers for DEM is approached, where the agent layer participates in the energy management task. An Asynchronous Decentralized PSO (ADPSO) algorithm is proposed for the agent layer based on its primal characteristic: it can reach a consensus state between networked computing units by exchanging asynchronously only the state variable through the communications channel. The proposed solution allows the integration of DEM capabilities within the physical controller of the DERs, distinguishing it from other decentralized solutions. Easiness of implementation and low computational requirements are shown by performing DEM tests on single board computers. The tests show improved convergence rate, improved swarm diversity behavior and fast consensus reaching of DEM optimization.

In this paper, a robust leader-following consensus protocol for multi-agent systems (MASs) subject to faults in the information exchange and disturbance is presented. The problem under consideration is to guarantee the convergence of the agent trajectories to a leader agent when all the agent followers are under faults in the information exchange as smooth time-varying delays and disturbances. The main contribution in this paper is the design of a robust leader-following control through the Lyapunov approach and an optimal $\mathscr {H}_{\infty }$ criterion such that all agents follow a virtual leader agent despite faults in the information exchange and disturbances. Linear matrix inequalities (LMIs)-based conditions are obtained whose solution allows computing the robust controller gain. In order to show the effectiveness of the proposed approach, numerical examples are carried out comparing a state-of-the-art approach and the proposed strategy in a fleet of unmanned aerial vehicles (UAVs) subject to wind turbulence which are shown to achieve the formation control.

In this paper, we present the design of a fractional-order sliding mode observer (FO-SMO) for actuator fault estimation in a quadrotor unmanned aerial vehicle (QUAV) system. Actuator faults can significantly compromise the stability and performance of QUAV systems; therefore, early detection and compensation are crucial. Sliding mode observers (SMOs) have recently demonstrated their accuracy in estimating faults in QUAV systems under matched uncertainties. However, existing SMOs encounter difficulties associated with chattering and sensitivity to initial conditions and noise. These challenges significantly impact the precision of fault estimation and may even render fault estimation impossible depending on the magnitude of the fault. To address these challenges, we propose a new fractional-order SMO structure based on the Caputo derivative definition. To demonstrate the effectiveness of the proposed FO-SMO in overcoming the limitations associated with classical SMOs, we assess the robustness of the FO-SMO under three distinct scenarios. First, we examined its performance in estimating actuator faults under varying initial conditions. Second, we evaluated its ability to handle significant chattering phenomena during fault estimation. Finally, we analyzed its performance in fault estimation under noisy conditions. For comparison purposes, we assess the performance of both observers using the Normalized Root-Mean-Square Error (NRMSE) criterion. The results demonstrate that our approach enables more accurate actuator fault estimation, particularly in scenarios involving chattering phenomena and noise. In contrast, the performance of classical (non-fractional) SMO suffers significantly under these conditions. We concluded that our FO-SMO is more robust to initial conditions, chattering phenomena, and noise than the classical SMO.