Search

In order to realize the operability and safety of unmanned aerial vehicles (UAVs) in confined areas, this article investigates an adaptive anti-disturbance performance guaranteed fuzzy formation control problem for quadrotor UAVs by using aperiodic signal updating. The unknown dynamics are approximated by using fuzzy logic systems. A disturbance observer is constructed for each UAV, including position subsystem (outer-loop) and attitude subsystem (inner-loop), to reduce the negative effects of UAVs with disturbances in complex flight environments. To avoid the potential internal collision among the multiple UAVs, a prescribed performance function that widens the initial value range of the consistency error is designed to keep the formation error within the specified range. Intermittent output signals generated by event-triggered control strategy of attitude subsystem are used to reduce sensors data transmission on each UAV, thereby saving energy and communication resources. Via the Lyapunov stability theory, the formation error can converge to a prescribed boundary range. Finally, the validity of the proposed control strategy is illustrated by simulation results.

This article presents an excitation operator-based fault separation architecture for a quadrotor unmanned aerial vehicle subject to loss of effectiveness faults, actuator aging, and load uncertainty. The actuator fault dynamics is deeply excavated, containing the deep coupling information among the actuator faults, the system states, and control inputs. By explicitly considering the physical constraints and tracking performance, an excitation operator and a corresponding integrated state observer are designed to estimate separately actuator fault and load uncertainty. Moreover, a fault separation maneuver and a safety controller are proposed to ensure the tracking performance when the excitation operator is injected. Both comparative simulation and flight experiments illustrate the effectiveness of the proposed scheme.

This article investigates the fault-tolerant coordinated tracking control problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults and communication delays. By supplementing the commonly used Gaussian functions in the fuzzy neural networks (FNNs) with sine–cosine functions and constructing two kinds of recurrent loops within the FNN architecture, double recurrent perturbation FNNs are cleverly designed to learn the unknown terms containing faults and uncertainties. Then, adaptive laws are designed for double recurrent perturbation FNNs. Moreover, by assimilating fractional-order calculus into the sliding-mode surfaces and the control signals, refined transient-state and steady-state adjustment performances can be obtained. It is shown by Lyapunov stability analysis that all fixed-wing UAVs can coordinately track their desired trajectories and the tracking errors are uniformly ultimately bounded. Comparative simulation results are provided to show the effectiveness of the proposed control strategy.

This study proposes a distributed fault-tolerant formation control scheme for a team of car-like vehicles with actuator faults. The proposed method includes a fault diagnostic scheme using a high-order sliding mode observer and a reconfigurable controller based on the super-twisting algorithm. The results showed that by the proposed controller, each vehicle can detect, diagnose, and accommodate actuator faults by itself. Two control strategies are proposed: 1) When a vehicle is exposed to a partial fault, the controller accommodates the fault and enables the vehicle to continue the mission with the rest of the team members in the formation. Furthermore, it allows healthy vehicles to re-adjust their motion and move in the way for compensating the reduced capability of the faulty vehicle with degraded performance in the formation mission; 2) In severe fault conditions, the proposed controller excludes the faulty vehicle and re-assigns its task to the rest of the healthy members to complete the mission. The re-assignment task for the team formation is formulated as an optimal assignment problem and is solved by the Simplex algorithm. The experimental tests have been conducted on the latest Quanser self-driving car (QCar) platform to verify the effectiveness of the proposed algorithm and ensure its capability to avoid accidents between team members.

Modern wind farms promise increased capacity, sustainability, and efficiency through the integration of information technology with the existing wind energy technologies. However, this integration creates a new category of cyber-physical vulnerabilities stemming from physical faults and/or cyberattacks potentially leading to devastating physical impacts and catastrophic consequences. The large scale and high complexity of a wind farm, in addition to its growing connectivity, control functionality, and wind intermittency and variability make the task of cyber-physical protection particularly challenging. This paper introduces novel approaches for guaranteeing the safety, security, and reliability of a modern wind farm under simultaneous occurrence of faults and attacks using an advanced cyber-physical health monitoring scheme, defined as “intrusion detection and fault diagnosis system”, as well as fault-tolerant/attack-resilient control strategies at different levels. The proposed fault-tolerant control strategy is based on adaptive model predictive control at turbine level, enhanced with a control reallocation mechanism at farm level. The attack-resilient control strategy is based on an automatic signal correction (ASC) technique that is applied at network operator level. The effectiveness of the suggested approaches is demonstrated using an offshore wind farm model under wind turbulences, measurement noises, and realistic physical fault and cyberattack scenarios.

In this article, a condition monitoring approach is proposed based on vibration signal, aiming at improving the adaptability of feature extraction and the accuracy of classification. First, the original vibration signal acquired under certain working condition is preprocessed by dividing it into multiple segments, followed by the signal decomposition. Then, the features of each decomposed signal are extracted based on the theory of diversity entropy (DE). Two parameters in the DE are optimized considering the fact that these parameters are crucial for the classification result. The optimization objective is to make the different segments of the signal collected in the same working condition have approximate feature characterization. By this means, the feature of the signal is captured adaptively and accurately using the optimized entropy value. Finally, the support vector machine is used to identify the extracted feature vectors to realize condition classification. The experiments on three representative platforms, including a crystal lifting-rotation system in our laboratory, are conducted to verify the effectiveness of the proposed method.