In direct-current (DC) microgrids (MGs), distributed secondary control is essential for achieving both voltage restoration and accurate current sharing, wherein precise parameter estimation plays a critical role in ensuring satisfactory control performance. To address system uncertainties, this paper presents a cascaded framework consisting of an adaptive parameter estimator and a distributed secondary controller for DC MGs. The proposed framework enables real-time online estimation of transmission line resistance and inductance while fulfilling the voltage restoration and current sharing. Since it is often challenging to validate the persistent excitation (PE) condition in practical DC MGs, we propose two parameter estimation algorithms. The first one is a standard gradient descent estimator, which requires strict PE to be satisfied. The second algorithm integrates preconditioning dynamics with gradient descent and only needs a weaker interval excitation (IE) condition to achieve effective estimation. Moreover, by integrating a dynamic average model with the virtual current derivative (VCD) approach, the DC MG system is reduced to a second-order model, which simplifies the joint design of parameter estimation and control, as well as facilitates closed-loop stability analysis. Finally, the effectiveness of the proposed method is verified through numerical simulations and experiments.

This paper investigates resilient tracking control for multi-agent systems subject to multi-channel asynchronous denial-of-service (DoS) attacks within a directed graph. We consider both homogeneous dynamics and uncertain heterogeneous dynamics in our analysis. We first propose a distributed resilient tracking control strategy against multi-channel asynchronous DoS attacks for homogeneous systems. The concept of equivalent decay-rates across different attacked channels is introduced to derive sufficient conditions for secure tracking control. Building upon this foundation, we extend the method to address tracking control under uncertain heterogeneous dynamics and constrained communication resources. To this end, we propose an event-triggered resilient control strategy based on equivalent decayrate analysis. The strategy reduces communication overhead by adopting demand-driven scheduling and enhances resilience through a decay-rate-guided feedback mechanism. Our proposed algorithms both mitigate asynchronous DoS attacks by constraining attack surfaces and ensure secure tracking under resource constraints. Finally, numerical examples are provided to verify our theoretical analysis.

Modern energy systems increasingly rely on complex, multi-energy microgrids incorporating diverse energy carriers, including hydrogen-based technologies, yet their efficient dispatch remains challenging due to the need for precise coordination across heterogeneous resources. Existing multi-agent reinforcement learning approaches often fail to capture the nuanced interactions between different energy vectors and agents. We propose an Attention-boosted Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm that enhances inter-agent coordination through self-attention mechanisms in the critic network, enabling more effective feature extraction and global value assessment. Comprehensive simulations demonstrate our approach significantly outperforms baseline methods, reducing operational costs by up to 37.67% and carbon emissions by 34.1%, while maintaining superior power balance across microgrids. These results establish attention-enhanced MATD3 as a promising solution for optimizing complex energy systems with high renewable penetration and diverse storage technologies.

In this article, we study the optimal output synchronization problem (OOSP) for uncertain networked Euler-Lagrange (EL) systems. Specifically, the system outputs are expected to be synchronized at the solution of an uncertain distributed time-varying quadratic optimization problem, where each local time-varying cost function includes uncertain parameters. From a centralized perspective, we first develop a controller with adaptive control gains to guide the output of a double-integrator system toward the time-varying optimal solution. By employing the modified average estimators, we extend the centralized design to a distributed implementation to address the OOSP for uncertain EL systems. Using matrix trace properties and composite Lyapunov analysis, we prove that the system outputs can asymptotically converge to the desired time-varying optimal solution. Two examples are used to verify the proposed designs.

This article studies distributed continuous-time optimization for time-varying quadratic cost functions with uncertain parameters. We first propose a centralized adaptive optimization algorithm using partial information of the cost function. It can be seen that even if there are uncertain parameters in the cost function, exact optimization can still be achieved. To solve this problem in a distributed manner when different local cost functions have identical Hessians, we propose a novel distributed algorithm that cascades the fixed-time average estimator and the distributed optimizer. We remove the requirement for the upper bounds of certain complex functions by integrating state-based gains in the proposed design. We further extend this result to address the distributed optimization where the time-varying cost functions have nonidentical Hessians. We prove the convergence of all the proposed algorithms in the global sense. Numerical examples verify the proposed algorithms.

This article is concerned with developing a privacy-preserving distributed-learning-based fault diagnosis approach for power transformers. Due to the constraints of data privacy, it is not possible to have enough labeled samples for training. Recently, the emergence of federated learning (FL) has provided a secure and distributed learning framework. However, the unbalanced data from multiple power stations may reduce the overall performance of FL while an untrusted central server can threaten the data privacy and security of clients. To address such challenges, a privacy-preserving FL scheme is developed for transformer fault diagnosis, where a multistep data-sharing strategy and an adaptive differential privacy technology are proposed. Specifically, amounts of shared data and noise perturbation will be designed according to the quantity of local data by the central server. The experimental results on the dataset generated according to IEC publication 60599 show that the proposed method has high diagnostic accuracy across various categories of transformer faults and even on training datasets with extremely unbalanced data quantity where the average accuracy is as high as 95.28%.

We investigate the capacity of an adversary to learn the underlying interaction network through repeated best response actions in linear-quadratic games. The adversary strategically perturbs the decisions of a set of action-compromised players and observes the sequential decisions of a set of action-leaked players. The central question pertains to whether such an adversary can fully reconstruct or effectively estimate the underlying interaction structure among the players. To begin with, we establish a series of results that characterize the learnability of the interaction graph from the adversary’s perspective by drawing connections between this network learning problem in games and classical system identification theory. Subsequently, taking into account the inherent stability and sparsity constraints inherent in the network interaction structure, we propose a stable and sparse system identification framework for learning the interaction graph based on complete player action observations. Moreover, we present a stable and sparse subspace identification framework for learning the interaction graph when only partially observed player actions are available. Finally, we demonstrate the efficacy of the proposed learning frameworks through numerical examples.

This paper studies robust regulation problem of oxygen excess ratio (OER) and cathode pressure for proton exchange membrane fuel cell (PEMFC) air supply systems with centrifugal compressor. With the speed of the centrifugal compressor and the valve opening degree as control inputs, a robust output regulation problem is formulated for the PEMFC air supply system modelled as a two-input two-output nonlinear control system. To overcome the difficulties of nonlinearities, uncertainties and couplings between input-output channels, we introduce a change of coordinate to transform the original problem into a robust output stabilization problem of a two-input two-output nonlinear system. For such a problem, an “ideal” full-information controller is designed via feedback linearization, based on which an extended-state observer-based control method is proposed, robustly regulating the OER and cathode pressure arbitrarily close to desired values. Simulations are made to demonstrate the effectiveness of the proposed method.

This paper proposes a thermal management strategy for proton exchange membrane fuel cell based on nonlinear model predictive control (NMPC). To handle the highly coupled and nonlinear nature of the thermal management system, we adopt NARMAX model and introduce BP neural network to fit the nonlinear prediction model. Then in rolling optimization stage, an optimization problem consisting of two inputs and single output is established, for which an improved particle swarm optimization method is employed in combination with a mutation mechanism and an improved parameter update mechanism. Simulation results validate the effectiveness of the proposed NMPC-based thermal management strategy.

We consider a distributed online convex optimization problem when streaming data are distributed among computing agents over a connected communication network. Since the data are high-dimensional or the network is large-scale, communication load can be a bottleneck for the efficiency of distributed algorithms. To tackle this bottleneck, we apply the state-of-art data compression scheme to the fundamental GD-based distributed online algorithms. Three algorithms with difference-compressed communication are proposed for full information feedback (DC-DOGD), one-point bandit feedback (DC-DOBD), and two-point bandit feedback (DC-DO2BD), respectively. We obtain regret bounds explicitly in terms of time horizon, compression ratio, decision dimension, agent number, and network parameters. Our algorithms are proved to be no-regret and match the same regret bounds, w.r.t. time horizon, with their uncompressed versions for both convex and strongly convex losses. Numerical experiments are given to validate the theoretical findings and illustrate that the proposed algorithms can effectively reduce the total transmitted bits for distributed online training compared with the uncompressed baseline.

In this article, we consider a convex optimization problem which minimizes the sum of local agents' cost functions subject to certain local constraints. Besides, both the local cost function and local constraints are only known by the local agent itself. To solve this problem, a new accelerated distributed gradient-based algorithm is proposed, which is inspired by the “momentum” phenomena in nature and aims to accelerate the convergence speed of conventional distributed gradient algorithms. Sufficient conditions for the stepsizes and the acceleration gains are derived to ensure the convergence of the proposed algorithm. Furthermore, based on this proposed fast distributed algorithm, a new decentralized approach is proposed to solve economic dispatch problem, especially for a large-scale power system. Based on the idea of virtual agent, it is proved that this decentralized algorithm is equivalent to the original fast distributed gradient method. Several case studies implemented on IEEE 30-bus, IEEE 118-bus power systems, and a large-scale power system consisting of 1000 generators are conducted to validate the proposed method.

This paper revisits the decentralized stabilization problem for a class of interconnected nonlinear systems subject to strongly coupled uncertain interactions. As a main contribution, we show that, without any priori required restrictive nonlinear growth conditions, a C1condition of the nonlinear interactions is sufficient to derive a finite-time convergent decentralized stabilizing control law under a semi-global control objective. Another new feature is the adoption of a novel one step nonsmooth synthesis method which leads to a simple form of stabilizing control law with easily implementable gain tuning mechanisms. A numerical example and its simulation verification are provided to illustrate the simplicity and effectiveness of the proposed method.

This paper investigates a novel nonrecursive tracking control law design for a class of nonlinear systems via dynamic output feedback. As the main contribution of this paper, a global nonrecursive tracking design procedure is first proposed to render a simple construction of a realizable output feedback control law, whose gain selections follow the conventional pole placement approach while the stability margin can be guaranteed via a sufficiently large scaling gain. By introducing a Lyapunov function which neglects the virtual controllers in essence, rigorous analysis is presented to ensure the global stability. In addition, finite-time and asymptotical tracking results can now be achieved within the same design framework whereas the tunable homogeneous degree plays as a key role. As another contribution, by proposing a saturated dynamic compensator, a less ambitious but practical control objective, namely semiglobal stability is achieved of the closed-loop system to relax the requirement of the restrictive growth conditions for global control design. Taking consideration of the case when system is subject to mismatched disturbances, a unified design and stability analysis framework shows that the practical tracking result can also be realized. A numerical example is provided to illustrate the effectiveness of the nonrecursive design and the simplicity of the proposed tracking control algorithm.

In this paper, reciprocal collision avoidance is studied for nonholonomic mobile robots to achieve an efficient navigation. Two strategies are proposed to respectively adjust the linear and angular velocities so that a collision-free navigation can be achieved. By characterizing the collision-free navigation as a set of changing ratios for linear velocities, it is shown that collision can be avoided if the changing ratio of linear velocities is inside this set. Moreover, the strategy of the adjusting angular velocities is established following TTC-based method. With the combination of these two strategies, a simulation is done for four robots crossing the intersection, which shows the effectiveness of the proposed method.