Distributed economic dispatch (ED) has emerged as a critical issue in microgrid operations due mainly to the wide application of various clean energy as well as energy storage units. The openness of communication networks in microgrids can lead to privacy breaches, which pose a serious threat to the entire electricity market. As such, this paper presents a distributed ED algorithm based on the alternating direction method of multipliers (ADMM), where a quantization-based encryption and decryption rule is integrated to avoid privacy leakage while iteratively acquiring the optimal ED scheme. By resorting to the property of monotonically convergent sequences, a sufficient condition about the learning rate is profoundly revealed to guarantee the algorithm convergence. Two extended results are presented, respectively, to enhance the convergence rate and meet the requirement of plug-and-play scenarios. Finally, the validity (both privacy and optimality) of the proposed algorithm is verified by using the dual-source trolleybus system in Beijing. Note to Practitioners—This paper develops an engineering-oriented ED algorithm that optimizes the total generation costs of smart grids online while guaranteeing system constraints. Shared network communication undoubtedly plays a significant role in achieving iteratively the optimal solution of distributed algorithms. However, some crucial and sensitive information exchanged via an open and shared network could be eavesdropped by malicious attackers, which could result in a serious security threat affecting the reliability and stability of the smart grid. To overcome such a shortage, an encryption-decryption rule is constructed via a dynamic quantizer. In light of such a rule, the presented algorithm based on ADMM can iteratively acquire the optimal ED solution in a distributed way, realizing the requirements of optimality and privacy. The desired range of the learning rate is disclosed to guide the parameter selection, and two improved versions are proposed to meet more general engineering practice involving plug-and-play scenarios.

Although numerous distributed control strategies have been developed for formation control of networked multi-UAV systems, they typically rely on the assumption of undirected and connected communication networks. This assumption limits the applicability of control strategies to scenarios with asymmetric information interactions among UAVs. To overcome this limitation, this article addresses the distributed formation control problem of nonlinear multi-UAV systems with unknown uncertainties under general directed communication networks. The approximation capabilities of fuzzy logic systems are leveraged to handle nonlinearities and uncertainties. To conserve limited resources, an event-driven approach is introduced for the design of a distributed formation control strategy. Specifically, the controller of each UAV is updated only when specific events are triggered by a distributed triggering condition, instead of being updated continuously over time. Under intermittent event-driven control updates, all UAVs can achieve a desired time-varying formation configuration, while excluding the Zeno behavior. Adaptive gains, rather than fixed gains, are introduced into the control strategy to enable more flexible control implementation without relying on global information. In addition to formation control, an event-driven attitude control strategy is proposed to ensure that the yaw angles of all UAVs converge to a prescribed value. Finally, simulations are conducted to validate the effectiveness and advantages of the proposed control schemes.

This article examines the recursive estimation issue for a class of complex networks that incorporates decode-and-forward (DaF) relays and energy harvesting (EH) techniques. The random intercoupling topologies are captured by Gaussian noise. Owing to the insufficient transmission capacity of sensors, DaF relays are implemented to connect sensors with remote estimators, augmenting the transmission range and improving communication quality. The energy required for signal transmission can be supplied through EH techniques deployed at sensors and relays. This study focuses on designing a recursive state estimator aimed at guaranteeing accurate estimation performance. An upper bound for the estimation error covariance matrix is formulated via two recursive equations, and subsequently minimized by properly designing the estimation gain. Moreover, the developed estimator is evaluated through detailed theoretical analysis, with emphasis on its uniform boundedness and monotonic behavior. Simulated examples confirm the efficacy of the underlying distributed estimator.

In this paper, we introduce PADUAV, a novel 5-DOF aerial platform designed to overcome the limitations of traditional tiltrotor vehicles. PADUAV features a unique mechanical design that incorporates two off-the-shelf quadrotors passively articulated to a rigid frame. This innovation enables free pitch rotation without mechanical constraints like cable winding, significantly enhancing its capabilities for various tasks. To control PADUAV’s 5 degrees of freedom, we propose a versatile and straightforward 5-DOF geometric tracking control strategy that generates 2D force and 3D torque. A decomposition approach is designed to distribute the output to the torque and thrust commands for each subplane, with no need for complex optimization. We validate our approach through three simulation experiments conducted in the Gazebo environment, leveraging the utilities provided by the RotorS simulator. These experiments not only demonstrate the feasibility of our platform but also provide new perspectives for future aerial platform development, particularly in terms of simulation-based approaches.

This paper investigates the problem of outlier-resistant distributed fusion filtering (DFF) for a class of multi-sensor nonlinear singular systems (MSNSSs) under a dynamic event-triggered scheme (DETS). To relieve the effect of measurement outliers in data transmission, a self-adaptive saturation function is used. Moreover, to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization, a DETS is adopted to regulate the frequency of data transmission. For the addressed MSNSSs, our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS; the local upper bound (UB) on the filtering error covariance (FEC) is derived by solving the difference equations and minimized by designing proper filter gains. Furthermore, according to the local filters and their UBs, a DFF algorithm is presented in terms of the inverse covariance intersection fusion rule. As such, the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers, thereby improving the estimation performance. Moreover, the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is presented. Finally, the validity of the developed algorithm is checked using a simulation example.

This paper deals with the tracking control problem for a type of nonlinear networked systems by utilizing the data-driven control algorithm. A scalar-uniform-quantization-based two description coding mechanism is employed, with the hope to alleviate communication bandwidth limitation and packet dropouts phenomenon during data transmission. Such a mechanism first encodes the source signal into two descriptions, and then send the coded information to the decoder via independent network channels. The random packet dropouts phenomenon is considered that is described via Bernoulli sequences. A data-driven control scheme is designed, ensuring that the system output can track the desired reference. By resorting to the dynamic linearization method in combination with certain convex optimization technique, the desired control protocol is established. Finally, the usefulness of our designed control approach is demonstrated via two examples.Note to Practitioners—Networked systems are composed of sensors, actuators and controllers connected through a shared digital communication network, which have advantages in long-distance control operations. It should be noted that, however, during network communication, data packets would probably suffer dropout due to various reasons such as limited bandwidth, environment abrupt change, device aging and so on, which will degrade system performance or even damage the system. However, so far, in the context of data-driven control, where data play an essential role, such a data-lost phenomenon has not been fully examined when designing the control strategies. Therefore, the main motivation of this paper is to study the tracking control problem of networked systems with data packet dropouts. In this work, we provide an efficient control algorithm that enables the system to track the desired target in spite of packet loss. The core of this method is to transmit data through two independent network channels so as to reduce the probability of the transmission information being completely destroyed at the same time. We show the feasibility and efficiency of our data-driven control scheme on basis of two description coding mechanism by simulation. In future research, we will extend the method to target tracking in multi-sensor networked system subject to data loss.

Secure state estimation is becoming more popular due to the inherent vulnerabilities of communication networks in essence, which could give rise to potential data leakage and manipulation of microgrids. The paper addresses the issue of secure distributed state estimation for a class of microgrids with potential outliers occurring in sensor measurements. First, a secure distributed estimator is constructed by introducing both an artificial saturation rule to achieve outlier resilience and a variable decomposition strategy to safeguard data security, where the generated dynamic key is a time-varying sequence satisfying the predetermined constraint. Deep variance analysis is carried out to profoundly disclose the relationship between private and public estimation error covariance, in accordance with the employed decomposition rule. An upper bound of error covariance is determined by two sets of recursive matrix equations in contrast to that of traditional distributed estimation. Furthermore, the desired estimator gains are obtained recursively with the aid of optimizing the upper bound obtained above. In the end, a simulation example is proposed to confirm the effectiveness and security of the proposed algorithm.

This article focuses on the recursive filtering problem for networked time-varying systems with randomly occurring measurement outliers (ROMOs), where the so-called ROMOs denote a set of large-amplitude perturbations on measurements. A new model is presented to describe the dynamical behaviors of ROMOs by using a set of independent and identically distributed stochastic scalars. A probabilistic encoding-decoding scheme is exploited to convert the measurement signal into the digital format. For the purpose of preserving the filtering process from the performance degradation induced by measurement outliers, a novel recursive filtering algorithm is developed by using the active detection-based method where the "problematic" measurements (i.e., the measurements contaminated by outliers) are removed from the filtering process. A recursive calculation approach is proposed to derive the time-varying filter parameter via minimizing such the upper bound on the filtering error covariance. The uniform boundedness of the resultant time-varying upper bound is analyzed for the filtering error covariance by using the stochastic analysis technique. Two numerical examples are presented to verify the effectiveness and correctness of our developed filter design approach.

The protocol-based memory fault detection (FD) problem under finite-time constraint is investigated for nonlinear networked systems, where the round-robin protocol is adopted to identify which sensor node can release the measured value at each transmission instant. In particular, the past states of the filter are exploited to develop the memory fault detection filter (FDF). Subsequently, the Finsler lemma and the Lyapunov approach are combined to analyze the finite-time stability of the addressed system. Then, the specific expression of the memory FDF gain matrices is obtained by using the solutions to certain matrix inequalities. In addition, for the aim of verifying that the memory FD approach has higher detection accuracy, a memoryless FDF is also designed in comparison with the memory FDF. In the end, the illustrative simulation studies are given to demonstrate the applicability of developed FD filtering algorithms in resistance–inductance–capacitance circuit system and the superiority of the designed memory FD scheme.

This article is concerned with the distributed state estimation problem over wireless sensor networks (WSNs), where each smart sensor is capable of harvesting energy from the external environment with a certain probability. The data transmission between neighboring nodes is dependent on the energy level of each sensor, and the internode communication is deemed as a failure when the current energy level is inadequate to guarantee the normal data transmission. Considering the intermittent information exchange over WSNs, a novel distributed state estimator is first constructed via introducing a set of indicator functions, and then the evolution of the probability distribution of energy level and its steady-state distribution is systematically discussed by resorting to the eigenvalue analysis approach and the mathematical induction. Furthermore, the optimal estimator gain is derived by minimizing the trace of the estimation error covariance under known communication sequences. In addition, the convergence of the minimized upper bound of the expected estimation error covariance is analyzed under any initial condition. Finally, an illustrative example regarding the target tracking problem is provided to verify the validity of the obtained theoretical results.

This article is concerned with the dissipative deconvolution filtering issue for networked two-dimensional (2-D) digital systems. By orchestrating the sensor outputs with a prescribed dynamic transmission order, a new multinode Round-Robin protocol (RRP) under the 2-D setting is developed on the sensor-to-filter channel to ease the communication overheads, and a novel compensation strategy is proposed for enhancing the deconvolution filter performance. A sufficient condition is established for ensuring the dissipativity of the deconvolution filter in terms of coupled matrix inequalities. Furthermore, a particle swarm optimization (PSO) algorithm is formulated to design the filter gain with certain optimized performance. Finally, by transforming the color image into three gray images, the proposed algorithms are applied to the remote color image restoration problem under RRP. It is shown that the use of RRP saves 33.3% communication burden, and the saving of communication resources reduces the signal-to-noise ratio (SNR) by 18.5%. In comparison to the RRP using zero-input compensation strategy in available literature, the introduction of the novel multinode RRP with compensation strategy can indeed improve the reconstruction SNR.

This article addresses the proportional-integral (PI)-based security control issue of large-scale systems subject to randomly occurring joint attacks. Specifically, the considered cyber-attacks could happen in both sensor-to-observer and controller-to-actuator, and only partial data of sensors and controllers are randomly tampered with by malicious attacks due to energy limits. For the addressed problem, an observer-based PI controller is constructed by resorting to the compensation of randomly occurring joint attacks, which are modeled by two diagonal matrices combined with a set of stochastic variables. A sufficient condition only dependent on the local system dynamics as well as the local interconnected matrices is derived in the framework of the input-to-state stability (ISS) theory, and the desired gains of both the controller and the observer are obtained by the cone complementarity linearization (CCL) algorithm. Benefiting from the element matrix inequality, the developed design scheme satisfies the scalability requirement. In the end, the simulation test based on IEEE 39-bus power systems is seriously used to demonstrate the validity of the proposed control scheme.

In this article, the distributed model predictive control (MPC)-based noncooperative game problem is dealt with for the discrete-time multiplayer systems (MPSs) with an undirected graph. To reflect the reality, the state and input constraints are considered along with the matched disturbances and unmatched disturbances. The disturbance-observer-based composite MPC strategy is put forward which optimizes a given cost function over the receding horizon while eliminating the matched disturbances. An iterative algorithm is developed such that the model predictive dynamic game (MPDG) converges to the so-called$\varepsilon $-Nash equilibrium in a distributed manner. Sufficient conditions are established to guarantee the convergence of the proposed algorithm. In addition, easy-to-check conditions are also provided to ensure the uniform boundedness of the studied MPSs. Finally, a numerical example of a group of spacecrafts is provided to verify the effectiveness of the proposed methodology.