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This paper studies the mirror descent algorithm for distributed optimization, where the underlying digraph is assumed to be weight-unbalanced. Within this framework, a novel distributed mirror descent algorithm based on gradient weighting technique is developed. Theoretically, different from the existing works, which prove that the function value corresponding to the estimates converge to the optimal value of the optimization problem, this paper proves that (1) the proposed algorithm can achieve exact convergence of the estimates to the solution of the optimization problem; and (2) the algorithm has a convergence rate O (1 T) with a given time horizon T. Further, taking into account the fact that the cost functions in many significant optimization problems are dynamic, the distributed online optimization based on the proposed algorithm is studied. Especially, it is shown that the individual regret of the proposed algorithm is bounded by O (T). Finally, the theoretical results are verified through some simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, we consider a distributed economic dispatch problem (EDP) in smart grids, where each generator only communicates with its neighbors and minimizes its own cost in the presence of network-wide equality constraints and local capacity limits. Distributed algorithms to solve this problem have become an attractive focus of engineering research due to their multiple advantages. Many existing methods are time-triggered, whereas few works have been dedicated to solving the problem using an event-triggered approach. This problem gets more challenging when further accelerated convergence is desired. To solve such a problem, we carefully design an event-triggered parameter distributed accelerated algorithm, named as ET-PDA. On the one hand, the choice of different parameters in ET-PDA will lead to different momentum (Nesterov or heavy-ball) methods, which facilitates the accelerated convergence of the algorithm. On the other hand, the event-triggered mechanism in ET-PDA makes the time gap between two continuous interaction moments of each generator larger than the iteration interval, contributing to improved communication efficiency. In particular, ET-PDA achieves linear convergence when the local cost function of each generator is smooth and strongly convex, moreover, it precludes Zeno-like behavior. The effectiveness of ET-PDA is also verified through a series of simulation experiments. [ABSTRACT FROM AUTHOR]

This paper proposes a novel importance-based persistent coverage for environments with hotspots. Multiple quadrotors are utilized to maintain coverage while acquiring high-quality information over prioritized regions by lowering the altitude. To achieve this, the approach utilizes distributed communication protocols and cost functions to address the tradeoff between wider coverage and better quality information. The cost functions determine three-dimensional waypoints based on coverage levels, sensor resolution, and Euclidean distance. Voronoi tessellations are used locally for efficient computation and collision avoidance. Trajectories of the quadrotors are generated based on quadrotor dynamics using error functions that take into account state information and desired waypoints. The proposed approach is validated through simulations, comparisons, and experiments for multi-agent systems under quadrotor dynamics. Further, metrics of performance analysis are formulated that show consistent coverage levels and differences to assess the novel importance-based approach. The simulations and experiments demonstrate the feasibility and effectiveness of the approach, with prioritized regions receiving higher sensor resolution while maintaining persistent coverage of the environment. • Novel importance-based persistent coverage for information acquisition over hotspots. • Quadrotor waypoints based on information resolution and decaying environmental costs. • Distributed communication protocols for scalability and low computational complexity. • Real-time importance-based persistent coverage for multi-quadrotor dynamical systems. • Proposed coverage with varying velocities is verified by simulation and experiments. [ABSTRACT FROM AUTHOR]

The control input derived from model predictive control (MPC) scheme depends on the receding horizon whose length needs to be determined. Design length is also a trade-off between computational complexity and optimization metrics. To this end, a framework for adaptive selection of the horizon length has been developed for discrete systems in previous works. In this paper, this framework is extended to continuous nonlinear systems. A new algorithm is presented to solve the problem of how to adaptively select the horizon length. Then, according to the traditional method and the robust MPC scheme based on tubes, the recursive feasibility and robustness of the algorithm (the adaptive horizon nonlinear model predictive control, called AH-NMPC) for quasi-infinite time domain adaptive horizontal continuous-time nonlinear systems are proved. Finally, the NMPC controller is used to perform path-tracking experiments on an autonomous vehicle to verify the good effects of the algorithm (AH-NMPC). [ABSTRACT FROM AUTHOR]

This paper is concerned with model predictive control (MPC) for multisensor linear systems. In order to fully utilize information and achieve higher control accuracy, two fusion MPC algorithms are proposed. The first algorithm is named estimation-fusion model predictive controller (E-F MPC), which local estimations are fused and then fusion control input is obtained by the fusion estimation. The second algorithm is named control-fusion model predictive controller (C-F MPC), which fuses the control inputs obtained from local systems. The relationships, differences and complexity of the two algorithms are analyzed. The advantages of E-F MPC are that it is simple and easy to implement with high control accuracy, while the disadvantage is the bad robustness because of the heavy estimation, prediction and control tasks for the fusion node. For C-F MPC, with a small loss of accuracy, network throughput and computational cost of its fusion node will be greatly reduced, which makes it good robustness and flexibility. Simulation analysis verifies the effectiveness and correctness of the conclusion. [ABSTRACT FROM AUTHOR]

This paper studies non-cooperative games, in which each player's local objective function depends on its own decisions as well as those of other players. In these games, each player's local objective function is differentiable, its decision is constrained by a local feasibility constraint, and the decisions of all players are coupled with an equality constraint. By analyzing the variational problem of the games, a distributed generalized Nash equilibrium seeking algorithm with fixed step sizes is developed based on the backward-reflected-forward–backward splitting. Each player performs a backward step followed by a forward–backward step, and the pseudo-gradient is evaluated at a reflection term. Moreover, the convergence of the proposed distributed algorithm is analyzed under standard assumptions using the operator theory and the convex analysis theory. Finally, the simulation results verify the effectiveness of the algorithm and the correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed online convex optimization problems with multiple coupled constraints over unbalanced digraphs, which are widely applied in diverse fields such as machine learning, smart grids, and resource allocation problems, etc. In such practical scenarios, multiple coupled constraints, where each coupled constraint might only contain a portion of all nodes, are more general and complex than global coupled constraints. Additionally, due to certain constraint conditions such as energy and communication limitations, each node can only exchange information with other nodes in an identical constraint. Besides, the network connectivity of practical constrained problems might not be only confined to one type of weight matrices. By adopting an augmented Lagrangian method, the multiple coupled constrained optimization problem is converted into a saddle-point problem. To tackle this problem, a novel double accelerated push–pull (DAPP) algorithm is proposed which concurrently employs row-stochastic (RS) and column-stochastic (CS) weight matrices. Specifically, each node pushes gradient information weighted by CS matrices to out-neighbors, while pulls dual variables fused by RS matrices from in-neighbors. Combined with CS matrices, the gradient tracking scheme is employed to promote the efficiency of tracking the averaged gradients. Furthermore, aiming at improving the convergence rate, the heavy-ball method is used to update both primal and dual variables. Then, rigorous theoretical results indicate that DAPP obtains the sublinear bounds of regret and constraint violation. Finally, the plug-in electric vehicles (PEVs) charging problem is utilized to demonstrate the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper designs continuous-time algorithms with linear convergence for solving distributed convex optimization problems without a strongly convex condition. The proposed primal–dual algorithms operate under weight-balanced digraphs and the dual variables are not exchanged with neighbors, which makes the algorithm more efficient. To save communication resources, we propose a class of event-triggered communication (ETC) schemes, which includes static and dynamic counterparts with the latter being more effective in communication resource saving. Furthermore, using Lyapunov theory, we prove that the distributed event-triggered algorithms converge to the optimum set with exact linear convergence rates. Finally, we present a comparison example that validates the effectiveness of the proposed algorithms in reducing communication burdens. [ABSTRACT FROM AUTHOR]

This paper studies the optimal three-dimensional (3D) formation circumnavigation problem for multiple unmanned aerial vehicles (UAVs) with relative measurement and an unknown moving target. First, the 3D formation circumnavigation errors under the relative measurement and directed topology, that is, the spherical enclosing along the normal direction, the latitude surrounding along the north direction, and the longitude formation along the east direction, are constructed. Then, radial basis function neural networks (RBFNNs) based adaptive estimation is designed for each UAV to approximate the unknown target velocity using relative information. Further, to achieve the optimal energy of 3D formation circumnavigation along the normal, north, and east directions with lowered data transmission cost, an event-triggered actor–critic algorithm is designed under limited neighbor and relative target information. The stability of the whole system is analyzed under continuous sampled and event sampled cases, respectively, and the Zeno phenomenon is excluded. The effectiveness is validated through simulation under cases of with and without random noise. Meanwhile, comparative simulations are performed to validate the system energy and convergence time of the proposed method in contrast with two relevant works. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time consensus of second-order multi-agent systems subject to matched and mismatched disturbances. The derivatives of disturbances are assumed to be bounded. A novel integral sliding mode-based observer is presented by utilizing the super-twisting algorithm to estimate the agent states. The estimations can achieve the convergence to the leader in a fixed time. Furthermore, the corresponding fixed-time controller is developed to make the agents converge to the estimated states. As the estimated states converge to the leader, the agents will track the leader states with the help of estimated states. In addition, the overall convergence of multi-agent systems is non-monotonic due to the disturbances. It is theoretically shown that the proposed control approach can maintain a bounded convergence and get the consensus of multi-agent systems in a fixed time. Some simulations are presented to verify the effectiveness of the proposed control protocol. [ABSTRACT FROM AUTHOR]

In recent years, the multitask diffusion least mean square (MD-LMS) algorithm has been extensively applied in distributed parameter estimation and target tracking of multitask network. However, its performance degrades under colored input signals or impulsive noises. To overcome these two drawbacks, this paper introduces the subband adaptive filter (SAF) into a multitask network for the first time, and a robust multitask diffusion normalized M-estimate subband adaptive filtering (MD-NMSAF) algorithm is proposed, which solves the global network optimization problem based on the modified Huber (MH) function in a distributed manner, achieves robustness to impulsive noise, and significantly improves the convergence performance of the MD-LMS algorithm. Compared with existing robust multitask diffusion affine projection algorithms (MD-APAs), the computational complexity of the proposed MD-NMSAF algorithm is greatly reduced. In addition, we analyze the mean and mean-square stability conditions of MD-NMSAF, provide theoretical models characterizing the network mean square deviation (MSD) behaviors of transient and steady-state, and further verify their correctness by computer simulations. Simulation results under different network topologies, input signals, filter lengths and impulsive noise types fully demonstrate the performance advantages of the proposed MD-NMSAF algorithm over its competitors in terms of steady-state estimation accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

In this paper, a generalized conjugate direction algorithm (GCD) is proposed for solving general coupled Sylvester matrix equations. The GCD algorithm is an improved gradient algorithm, which can realize gradient descent by introducing matrices P j (k) and T j (k) to construct parameters α (k) and β (k). The matrix P j (k) and T j (k) are iterated in a cross way to accelerate the convergence rate. In addition, it is further proved that the algorithm converges to the exact solution in finite iteration steps in the absence of round-off errors if the system is consistent. Also, the sufficient conditions for least squares solutions and the minimum F-norm solutions are obtained. Finally, numerical examples are given to demonstrate the effectiveness of the GCD algorithm. [ABSTRACT FROM AUTHOR]

The event-triggered time-varying formation tracking for a class of second-order multi-agent systems (MASs) subject to a non-cooperative leader is investigated in this paper. First, in the presence of the unknown input of the leader and external disturbances, a distributed observer with adaptive parameters is put forward for followers to estimate the velocity tracking error. Then, based on the estimated tracking error and an auxiliary variable, a finite time formation controller is further constructed, which is updated depending on a pre-designed event-triggered mechanism. As a result, the desired time-varying formation configuration can be realized in finite time with less communication resource consumption. It's noted that the constructed formation strategy doesn't rely on any global information and thus is fully distributed. The stability of the controlled multi-agent system is proved rigorously and it's verified that event-triggered intervals are with a positive lower bound. At last, simulations are carried out to illustrate the effectiveness of the presented algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of decentralized optimization to minimize a finite sum of local convex cost functions over an undirected network. Compared with the existing works, we focus on improving the communication efficiency of the stochastic gradient tracking method and propose an effective event-triggering decentralized stochastic gradient tracking algorithm, namely, ET-DSGT. ET-DSGT utilizes the event-triggering mechanism in which each agent only broadcasts its estimators at the event time to effectively avoid real-time communication, thus improving communication efficiency. In addition, we present a theoretical analysis to show that ET-DSGT with a decaying step-size can converge to the exact global minimum. Moreover, we show that for each agent, the time interval between two successive triggering times is greater than the iteration interval under certain conditions. Finally, we provide several simulations to demonstrate the effectiveness of ET-DSGT. [ABSTRACT FROM AUTHOR]

Federated learning (FL) has attracted significant attention in the machine learning community owing to its instinct local privacy awareness. Depending on how the data are distributed over the clients, FL problems can be divided into three categories, namely the horizontal FL (HFL), the vertical FL (VFL) and the hybrid FL (HBFL). Among them, the HBFL problem is the most challenging because each client neither owns the full set of data samples nor knows the complete feature information. While many FL algorithms have been developed for the HFL and VFL problems, unfortunately they cannot handle the HBFL problem. In this paper, we propose a new FL algorithm, termed as FedHD, that can solve the challenging learning task under the HBFL setting. Since the clients cannot perform local optimization on their own under the hybrid data, a tracking variable is introduced to enable the clients to track the global gradient information and update the model based on their local data. FedHD allows the clients to perform multiple steps of local stochastic gradient descent (SGD), and hence has improved communication efficiency. Theoretical analysis is conducted to show that FedHD has a O (1 / Q T) convergence rate, where Q denotes the local update number and T is the total number of communication rounds. Then we further reveal the insights on how various algorithm parameters impact on the convergence performance. Experiment results show that the proposed FedHD exhibits robust performance on the hybrid data, and is largely superior to the naive local training model. [ABSTRACT FROM AUTHOR]

This paper solves the finite-time consensus problem for discrete time multi-agent systems (MASs) where agents update their values via linear iteration and the interactions between them are described by signed digraphs. A sufficient condition is presented that the agents can reach consensus on any given linear function of multiple initial signals in finite time, i.e., there exists an eventually positive Laplacian-based matrix associated with the underlying graph. We prove that the linear iterative framework "ratio consensus" developed for unsigned graphs in the literature can be extended to the computation for signed graphs with appropriate modifications. Our method weakens the limitation of the iterative framework on the "marginal Schur stability" of the weight matrix without increasing the computational complexity. Reaching average consensus on unsigned graphs as in the literature is regarded as a special case of our algorithm. Two illustrative examples are presented to demonstrate the correctness of the proposed results. [ABSTRACT FROM AUTHOR]

In this paper, a resilient distributed consensus algorithm is proposed based on multi-agent system to defend against the Byzantine attack. Two attack scenarios including node attack and edge attack are considered and the differences between them are described, in which the edge attack scenario is seldom considered before. The assumption for a network under g -local attacks is relaxed from (2 g + 1) -robust directed graph to (g + 1) -robust one based on a novel filtering method. The convergence and optimality are guaranteed by theoretical analysis, which ensures all the agents without attack can make an agreement and converge to a certain range. The simulation results show that for the two attack scenarios, the proposed algorithm can converge to the optimal solution of the compensation problem and the original problem, respectively. The proposed algorithm is more effective and has faster convergence speed compared with the contrast one. [ABSTRACT FROM AUTHOR]

This paper aims to create a secure environment for state estimation and control design of a networked system composed of multiple dynamic entities and remote computational units, in the presence of disclosure attacks. In particular, both dynamic entities and computational units may be vulnerable to attacks and become malicious. The objective is to ensure that the input and output data of the benign entities are protected from the malicious parties as well as protected when they are transmitted over the network in a distributed environment. We propose a methodology integrating a novel double-layer cryptographic technique with an observer-based control algorithm to achieve the objective where the cryptographic technique addresses the security requirements and the control algorithm satisfies the performance requirements. [ABSTRACT FROM AUTHOR]

This paper focuses on the numerical solution of a class of generalized coupled Sylvester-conjugate matrix equations, which are general and contain many significance matrix equations as special cases, such as coupled discrete-time/continuous-time Markovian jump Lyapunov matrix equations, stochastic Lyapunov matrix equation, etc. By introducing the modular operator, a cyclic gradient based iterative (CGI) algorithm is provided. Different from some previous iterative algorithms, the most significant improvement of the proposed algorithm is that less information is used during each iteration update, which is conducive to saving memory and improving efficiency. The convergence of the proposed algorithm is discussed, and it is verified that the algorithm converges for any initial matrices under certain assumptions. Finally, the effectiveness and superiority of the proposed algorithm are verified with some numerical examples. [ABSTRACT FROM AUTHOR]

This paper investigates the application of deep reinforcement learning (RL) in the motion control for an autonomous underwater vehicle (AUV), and proposes a novel general motion control framework which separates training and deployment. Firstly, the state space, action space, and reward function are customized under the condition of ensuring generality for various motion control tasks. Next, in order to efficiently learn the optimal motion control policy in the case that the AUV model is imprecise and there are unknown external disturbances, a virtual AUV model composed of the known and determined items of an actual AUV is put forward and a simulation training method is developed on this basis. Then, in the given deployment method, three independent extended state observers (ESOs) are designed to deal with the unknown items in different directions, and the final controller is obtained by compensating the estimated value of ESOs into the output of the optimal motion control policy obtained through simulation training. Finally, soft actor-critic is chosen as deep RL algorithm of the framework, and the generality and effectiveness of the proposed method are verified in four different AUV motion control tasks. [ABSTRACT FROM AUTHOR]

This paper proposes two-stage continuous-time triggered algorithms for solving distributed optimization problems with inequality constraints over directed graphs. The inequality constraints are penalized by adopting log-barrier penalty method. The first stage of the proposed algorithms is capable of finding the optimal point of each local optimization problem in finite time. In the second stage of the proposed algorithms, zero-gradient-sum algorithms with time-triggered and event-triggered communication strategies are considered in order to reduce communication costs. Then, with the help of LaSalle's invariance principle, it is proved that the state solution of each agent reaches consensus at the optimal point of the considered penalty distributed optimization problem, and Zeno behavior is also excluded. Finally, numerical examples are given to illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on the solution to a formation-aggregation problem concerned with a cluster of quadrotors. In particular, all the quadrotors are steered towards a target region while reaching an expected formation configuration. However, only a subset of quadrotors, treated as leaders, have access to the projections onto the target region; and in the meantime, each quadrotor just exchanges information with its neighbors. It is assumed that the quadrotors without the knowledge of the target region, treated as followers, are within the convex hull of the leaders in the expected configuration. A distributed formation-aggregation control algorithm is proposed under the hierarchical framework. To be specific, resorting to an auxiliary dynamic system with a distributed idea, a command force is first synthesized in the position loop for the concerned formation-aggregation objective. Next, an applied thrust and a command attitude are extracted from the synthesized command force. Finally, an applied torque is synthesized in the attitude loop such that the attitude tracking to the command one is realized. In terms of the Lyapunov theory, it is shown that the leaders are capable of guiding the overall quadrotor cluster into the target region in the expected configuration. Simulations are carried out to validate the theoretical results. [ABSTRACT FROM AUTHOR]

Distributed coordination of multi-agent systems (MASs) has been investigated for many years, and fractional-order calculus has been proved that it can model the dynamics more accurately in certain circumstances. Hence, in this paper, combining the above two aspects, the distributed coordination of fractional-order MASs (FOMASs) is researched, which is a promising topic. Besides, in this paper, the uncertainty, inherent nonlinearity and external disturbances are taken into consideration, aiming at achieving the robust consensus tracking. In particular, the uncertain parameters will be identified from an optimization perspective using artificial bee colony algorithm (ABC). Firstly, to ameliorate the performance of the standard ABC, a hybrid ABC (hABC) incorporating two groups of searching mechanisms is designed, it facilitates the identification of unknown parameters. After obtaining the identified parameters, an efficient distributed nonlinear controller is raised to fulfill the robust consensus tracking. Finally, experiments prove that the designed parameters identification approach can successfully estimate the uncertain parameters with high accuracy, besides the designed control algorithm can robustly control the FOMASs. [ABSTRACT FROM AUTHOR]

In this paper, we propose the full-rank and reduced-rank relaxed gradient-based iterative algorithms for solving the generalized coupled Sylvester-transpose matrix equations. We provide analytically the necessary and sufficient condition for the convergence of the proposed iterative algorithm and give explicitly the optimal step size such that the convergence rate of the algorithm is maximized. Some numerical examples are examined to confirm the feasibility and efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

To alleviate the restriction of system model on control design, data-driven model-free adaptive control (MFAC) is an excellent alternative to model-based control methods. This paper studies event-triggered data-driven control for switched systems over a vulnerable and resource-constrained network. The system is transformed into an equivalent switched data model through dynamic linearization. Resource constraints and denial of service (DoS) attacks in the network are concerned, and a novel joint anti-attack method including resilient event-triggering mechanism and prediction scheme is presented. Furthermore, new event-triggered MFAC algorithms are proposed. In this scenario, by constructing a Lyapunov functional on tracking error, sufficient conditions to ensure its boundedness are derived. This is the first time in the literature to give a complete solution to data-driven control of switched systems. At last, the validity of new algorithms and theoretical results is confirmed by simulations. [ABSTRACT FROM AUTHOR]

This paper focuses on constructing a conjugate gradient-based (CGB) method to solve the generalized periodic coupled Sylvester matrix equations in complex space. The presented method is developed from a point of conjugate gradient methods. It is proved that the presented method can find the solution of the considered matrix equations within finite iteration steps in the absence of round-off errors by theoretical derivation. Some numerical examples are provided to verify the convergence performance of the presented method, which is superior to some existing numerical algorithms both in iteration steps and computation time. [ABSTRACT FROM AUTHOR]

Recovering a low-rank matrix and a sparse matrix from an observed matrix, known as sparse and low-rank decomposition (SLRD), is becoming a hot topic in recent years. The most popular model for SLRD is to use the ℓ 1 norm and nuclear norm for the sparse and low-rank approximation. Since this convex model has certain limitations, various nonconvex models have been explored and found to be very promising. In this paper, we introduce a generalized nonconvex nonsmooth model for SLRD which covers a wide range of nonconvex surrogate functions that are continuous, concave and monotonically increasing on [ 0 , ∞) to approximate both the ℓ 0 norm and the rank function, such as ℓ p norm (0 < p < 1), Logarithm, Geman, SCAD and MCP functions. The choice of the nonconvex surrogates for the sparse and low-rank components can be different. Due to the nonconvexity and extensive options of the surrogates, the optimization problem is untractable. Based on the majorization-minimization (MM) algorithm, we propose a unified framework named MM-ADMM algorithm to solve this problem, which can be applied to all eligible surrogates as long as their supergradients are available. The constrained majorizing problems established under the MM framework can be easily solved by the alternating direction method of multipliers (ADMM). The theoretical convergence properties are investigated and proved, including the convergence of the sequence of objective function values generated by the designed algorithm and a weak convergence result related to the inner ADMM-iterations. Experiments on the synthetic data and real-world applications demonstrate the effectiveness of our designed MM-ADMM algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the distributed adaptive fault estimation issue using practical fixed-time design is investigated for attitude synchronization control systems. A distributed fault estimation observer is proposed based on the fixed-time technique. Meanwhile, a novel fixed-time adaptive fault estimation algorithm is also constructed to guarantee convergence rate and improve estimation rapidity. The fault estimation error is uniformly ultimately bounded and is practically fixed-time stable, which converges to the neighborhood of the origin in a fixed time. Finally, simulation results of an attitude synchronization control system are presented to verify the effectiveness of proposed techniques. [ABSTRACT FROM AUTHOR]

• A novel approach is discussed to incorporate fuzzy algorithm into a single board microcomputer based processor for intelligent relaying operation. • A fastest intelligent relaying algorithm reported by validating the same under IEEE 13 Bus test system with respect to performed simulation result analysis. • A proper validation of an idea authenticated based on distinguished result obtained from both the Fuzzy based as well as Non Fuzzy based systems. Protection and reliability enactment of electrical systems are important and emerging in power system research. Nowadays, it is very evident that the implementation of an intelligent algorithm is found in the field of substation equipment protection and relaying purposes. Majority of the researches are based on single load connected to a single feeder line and validated using simulation. A hardware based implementation and validation system will be an additional aspects. In this paper, we have discussed an expert system based intelligent relaying scheme by incorporating fuzzy algorithm in microcontroller. Purpose is to control the moving contacts of the breaker part for controlling multiple loads connected to a single feeder line. This paper reports the entire performance of intelligent relaying mechanism only considering stage - I with respect to non-fuzzy based relaying scheme and successfully achieved fastest coordination time after validating it under IEEE 13 Bus system. We have also validated the cascaded fuzzy based system and a non-fuzzy based system using ATMEL microcontroller. [ABSTRACT FROM AUTHOR]

In this paper, we address the distributed convex optimization problems for multi-agent systems. In our research, it is assumed that each agent in the multi-agent system could merely interact with its neighbors via a directed graph and is available to its own cost function. We then utilize the push-sum distributed dual averaging (PS-DDA) algorithm to tackle with the distributed optimization problem. However, we consider that there exist subgradient delays in PS-DDA algorithm. The proof of the main result which shows that the PS-DDA algorithm with subgradient delays converges and the error possesses sublinear growth of a rate O (τ 2 T − 0.5) , where T denotes the total amount of iterations, is detailed presented in this paper. Finally, a numerical example is simulated to show the performance of the algorithm we study in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a novel switching predefined-time parameter identification algorithm with a relaxed excitation condition based on the dynamic regressor extension and mixing (DREM) method. DREM often requires the persistent excitation (PE) of the extended square regressor's determinant to ensure exponential parameter convergence. Unlike the classical DREM method, a new parameter identification algorithm configured with a two-layer filter technique is proposed under a relaxed initial excitation (IE) condition, rather than strict PE. A key point in choosing IE instead of PE is the introduction of a smooth switching function that dominates the pure integral action and filter behavior of the extended square regressor. The proposed algorithm relies on the predefined-time stability theorem and the settling-time of the identification algorithm is set a priori as a system parameter. The contributions of this paper are a novel switching predefined-time parameter estimation algorithm that 1) relaxes the stringent PE condition, 2) achieves predefined-time convergence, and 3) guarantees the monotonicity of each element of the parameter error inherited from the classical DREM method. Comparative simulation results are presented to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• The optimized interaction control is presented for multiple robot agents collaborating with human arm to move the object. • The Q-learning method is utilized to eliminate the dependence on the dynamics of the human arm and the manipulated object. • The existence of the Nash equilibrium for human-multi-robot collaboration problems with reference position is proven. • Multi-player Nash Q-learning algorithm considering reference position is presented to solve the optimized control problem. [Display omitted] In this paper, optimized interaction control is investigated for human-multi-robot collaboration control problems, which cannot be described by the traditional impedance controller. To realize global optimized interaction performance, the multi-player non-zero sum game theory is employed to obtain the optimized interaction control of each robot agent. Regarding the game strategies, Nash equilibrium strategy is utilized in this paper. In human-multi-robot collaboration problems, the dynamics parameters of the human arm and the manipulated object are usually unknown. To obviate the dependence on these parameters, the multi-player Q-learning method is employed. Moreover, for the human-multi-robot collaboration problem, the optimized solution is difficult to resolve due to the existence of the desired reference position. A multi-player Nash Q-learning algorithm considering the desired reference position is proposed to deal with the problem. The validity of the proposed method is verified through simulation studies. [ABSTRACT FROM AUTHOR]

In this paper, the IO iteration algorithm proposed in Tian et al. [3] is further investigated, and some new results for solving the Sylvester matrix equation are obtained. At first, a new convergence property of the IO iteration algorithm is given for a wider convergence region associated with the parameter γ. Under the new convergence condition, a corresponding comparison result is established for the IO iteration algorithm compared with the Smith method. Moreover, if the matrices A , B in the Sylvester matrix equation are nonnegative, the IO iteration algorithm is proved to be convergent for any inner iteration number l k ≥ 1 with 0 < γ < 1. Finally, three numerical examples are implemented to confirm the validity of the new results. [ABSTRACT FROM AUTHOR]

• Consider a class of decentralized convex optimization problems with local feasible sets, equality and inequality constraints. • Integrate the inequality constraints into local cost function by means of suitable barrier function terms, and avoid the unapproximable property of proximal functions with respect to inequality sets. • Propose a synchronous full-decentralized primal-dual proximal splitting algorithm and its randomized version that removes the global clock coordinator, both of which enjoy private uncoordinated step-sizes. This paper concentrates on a class of decentralized convex optimization problems subject to local feasible sets, equality and inequality constraints, where the global objective function consists of a sum of locally smooth convex functions and non-smooth regularization terms. To address this problem, a synchronous full-decentralized primal-dual proximal splitting algorithm (Syn-FdPdPs) is presented, which avoids the unapproximable property of the proximal operator with respect to inequality constraints via logarithmic barrier functions. Following the proposed decentralized protocol, each agent carries out local information exchange without any global coordination and weight balancing strategies introduced in most consensus algorithms. In addition, a randomized version of the proposed algorithm (Rand-FdPdPs) is conducted through subsets of activated agents, which further removes the global clock coordinator. Theoretically, with the help of asymmetric forward-backward-adjoint (AFBA) splitting technique, the convergence results of the proposed algorithms are provided under the same local step-size conditions. Finally, the effectiveness and practicability of the proposed algorithms are demonstrated by numerical simulations on the least-square and least absolute deviation problems. [ABSTRACT FROM AUTHOR]

This paper investigates online composite optimization in dynamic environments, where each objective or loss function contains a time-varying nondifferentiable regularizer. To resolve it, an online proximal gradient algorithm is studied for two distinct scenarios, including convex and strongly convex objectives without the smooth condition. In both scenarios, unlike most of works, an extended version of the conventional path variation is employed to bound the considered performance metric, i.e., dynamic regret. In the convex scenario, a bound O ( T 1 − β D β (T) + T) is obtained which is comparable to the best-known result, where D β (T) is the extended path variation with β ∈ [ 0 , 1) and T being the total number of rounds. In strongly convex case, a bound O (log T (1 + T − β D β (T))) on the dynamic regret is established. In the end, numerical examples are presented to support the theoretical findings. [ABSTRACT FROM AUTHOR]

In the existing efficient robust model predictive control (ERMPC) algorithms (see e.g. [14,31,32]), through offline optimization and online lookup table calculation, a fixed state feedback control law or a linear interpolated control law is applied to a system when the system state lies between two adjacent polyhedrons, which undoubtedly will result in conservativeness of the controller. Faced with this issue, an improved ERMPC algorithm is proposed in this paper, which considers the nonlinearity between the state feedback control laws with respect to polyhedrons and the norm distance from system state to origin, and can provide continuously variable state feedback control law varying with the state. First, a set of polyhedral parameters and their corresponding state feedback control law sequences are obtained offline by solving a set of LMIs optimization problems. Next, for each state feedback control law sequence, a nonlinear fitting function is established offline between the state feedback control law and its serial number. Then a simplified lookup table is constructed offline to save memory space and shorten online computation time of the controller. According to the simplified lookup table and information of the norm distance from system state to origin, we online establish the coordinate of current state in the nonlinear fitting curve for getting current feedback control law, which changes continuously with the state. The proposed ERMPC algorithm is successfully applied to an actual fast-responding linear one stage inverted pendulum (LOSIP) system to verify its effectiveness. [ABSTRACT FROM AUTHOR]

This paper considers a nonsmooth constrained distributed convex optimization over multi-agent systems. Each agent in the multi-agent system only has access to the information of its objective function and constraint, and cooperatively minimizes the global objective function, which is composed of the sum of local objective functions. A novel continuous-time algorithm is proposed to solve the distributed optimization problem and effectively characterize the appropriate gain of the penalty function. It should be noted that the proposed algorithm is based on an adaptive strategy to avoid introducing the primal-dual variables and estimating the related exact penalty parameters. Additional, it is demonstrated that the state solution of the proposed algorithm achieves consensus and converges to an optimal solution of the optimization problem. Finally, numerical simulations are given and the proposed algorithm is applied to solve the optimal placement problem and energy consumption problem. [ABSTRACT FROM AUTHOR]

In this paper, we investigate some convergence results of the gradient based iterative algorithm for solving the Sylvester matrix equation A X + X B = C. We first review the development of the gradient based iterative algorithm and correct mistakes of some convergence results. Then, the idea of "two iterative factors" is applied to propose some new relaxed gradient iterative algorithms to improve the convergence performance of their previous algorithms. Finally, some numerical examples are taken to illustrate the correctness of the concluded results. [ABSTRACT FROM AUTHOR]

In conventional PID-type iterative learning control (ILC) designs, to determine the learning control gains involved, relevant model knowledge on the controlled systems is often dependent. In this paper, two completely data-driven ILC laws, the extended PD-type ILC law and the extended P-type ILC law, are designed in frequency domain for linear discrete-time (LDT) single-input single-output (SISO) systems. The designs of the proposed ILC laws are based on the approximation/identification to unknown transfer function with a novel adaptive Fourier decomposition (AFD) technique. As a result, the strictly monotonic convergence of ILC tracking error is guaranteed in a deterministic way. A numerical example on a four-axis robot arm is performed to illustrate the effectiveness of the proposed data-driven ILC algorithms [ABSTRACT FROM AUTHOR]

This paper presents a privacy-preserving average consensus algorithm for a discrete-time network with heterogeneous dynamic nodes in the presence of Gaussian privacy noises. Rényi divergence is used to measure the privacy, and a distributed algorithm is proposed for each node in the network to protect the initial output state and ensure consensus almost surely. The convergence rate of the proposed algorithm relates to the communication topology, dynamics of systems, and decaying rates of privacy noises. Moreover, by increasing neighbors of nodes in the network, the proposed algorithm can strengthen preservation. To demonstrate the theoretical results, a numerical example is carried out on a network of one hundred nodes. [ABSTRACT FROM AUTHOR]

In this paper, a novel complete model-free integral reinforcement learning (CMFIRL) algorithm based fault tolerant control scheme is proposed to solve the tracking problem of steer-by-wire (SBW) system. We begin with the recognition that the reference errors can eventually converge to zero based on the command generator model. Then an augmented tracking system is constructed with a corresponding performance index which is considered as a type of actuator failure. By using the reinforcement learning (RL) technique, three novel online update strategies are respectively developed to cope with the following three cases, i.e., model-based, partially model-free, and completely model-free. Especially, the RL algorithm for the complete model-free case eliminates the constraints of requiring the known system dynamics in fault-tolerant tracking controlling. The system stability and the convergence of the CMFIRL iteration algorithm are also rigorously proved. Finally, a simulation example is given to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, two improved projection algorithms are proposed for a servo turntable system based on the autoregressive exogenous (ARX) model. The improvement pertains to the complexity, convergence speed and estimation accuracy of the algorithm. First, the ARX model structure to be identified is obtained from the open-loop velocity model of the servo turntable. To solve the problems of imprecise step size calculation and slow convergence, the adaptive step size momentum projection (AMP) algorithm is proposed, in which segmented adaptive step sizes are adopted and momentum factors are introduced. On this basis, the latest estimation-based hierarchical adaptive step size momentum projection (LE-AMP) algorithm, which has faster convergence, is proposed as a combination of the hierarchical strategy and the latest estimation approach. Second, the complexity and convergence performance of the proposed algorithms are compared and analyzed. Finally, the effectiveness of the algorithms is verified using two examples. Theoretical analysis and simulation experiments show that the AMP algorithm has excellent parameter estimation accuracy, and its convergence speed is further improved through the addition of the momentum factor and the latest estimation, and these improved strategies do not cause any significant increase in complexity. [ABSTRACT FROM AUTHOR]

In this paper, a novel watermarking algorithm based on quaternion wavelet transform(QWT) and quaternion singular value decomposition(QSVD) is proposed for copyright protection. Firstly, color images are converted from RGB space to YCbCr space. Then one-level QWT transform is applied to the luminance component Y. The LL component is split to 4 × 4 non-overlapping blocks. Every block is decomposed by QSVD, and the bits of watermark are embedded in singular vectors or singular values. Moreover, 2D Chebyshev-Logistic map is proposed to encrypt the watermarks and scramble the embedding positions. In the watermarking extraction process, both blind and semi-blind watermarking extraction methods are introduced. Experiments show that proposed watermarking algorithm has good robust against common attacks and has good invisibility. [ABSTRACT FROM AUTHOR]

Feedback shift registers (FSRs), which have two configurations: Fibonacci and Galois, are a primitive building block in stream ciphers. In this paper, a transformation between Fibonacci FSRs and Galois FSRs is improved based on semi-tensor product (STP) of matrices. It is verified that a weakly equivalent Galois FSR with fewer stages cannot be found for a Fibonacci FSR with n stages, but not vice versa. Furthermore, for a given Fibonacci FSR with n stages, there are totally (2 n − 1) ! 2 − 1 weakly equivalent Galois FSRs. Additionally, an effective algorithm is developed to reduce the number of variables of the Galois FSRs while keeping it weakly equivalent to the given Fibonacci FSR. Finally, the feasibility of the proposed strategies is demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

• The identification of a fractional system with unknown but bounded (UBB) measurements noise is investigated. • A new identification method combining operational matrix of block pulse functions and set-membership method is proposed. • The coefficients and differentiation orders are simultaneously identified via a nest loop optimization. • The optimal bounding ellipsoid algorithm is used to estimate the parameters of the system and interior-point method for orders estimation. • The identification accuracy is improved. In this paper, a new method is proposed to identify the coefficients and differentiation orders of fractional order systems with measurement noise. The proposed method combines the operational matrix method and the set-membership method. First, the block pulse functions operational matrix of the fractional differentiation is used to convert the fractional order system to an algebraic system. Then, the coefficients and differentiation orders are simultaneously estimated through a nest loop optimization process, where the optimal bounding ellipsoid set-membership algorithm is utilized to estimate the system's coefficients and the orders are estimated with the interior-point method. The proposed method can accurately estimate the coefficients and differentiation orders of fractional order systems under any bounded measurement noise with less computational effort. Experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The main computational cost of building a support vector machine (SVM) training model lies in tuning the hyperparameters, including the kernel parameters and penalty constant C. This paper introduces a new kernel, the random radial basis function (RRBF) kernel, which all kernel parameters can be assigned to randomly. The key idea of the RRBF is to extend a one-dimensional parameter to d -dimensional parameters by simple modification of the traditional RBF kernel. We prove the universal approximation capability of a SVM with the RRBF kernel, proposing a simple model selection algorithm. The experiments on benchmark datasets show that SVM with the RRBF kernel clearly outperforms the traditional RBF kernel and other popular kernels, and the results are quite insensitive to C. Compared with other kernels, a SVM with the RRBF kernel is not sensitive to the penalty constant C , and it can be tuned in a wide range. Additionally, this property also enables a SVM to find the hyperparameters quickly. A simulation of parameter sensitivity study also show that a SVM is more stable w.r.t. to the different choices of kernel parameters. [ABSTRACT FROM AUTHOR]

The piecewise-linear characteristics often appear in the nonlinear systems that operate in different ways in different input regions. This paper studies the identification issue of a class of block-oriented systems with piecewise-linear characteristics. The asymmetric piecewise-linear nonlinearity is expressed as a linear parametric representation through introducing an appropriate switching function, then the identification model of the system is derived by using the key term separation technique. On this model basis, a multi-innovation forgetting gradient algorithm is presented to estimate the unknown parameters. To further enhance the identification accuracy, the filtering identification model of the system is derived by changing the structure of the system without changing the relationship between the input and output. Further, a data filtering-based multi-innovation forgetting gradient algorithm is proposed through the use of the data filtering technique. A simulation example is employed to illustrate that the proposed approaches are effective for parameter estimation and the data filtering-based multi-innovation forgetting gradient algorithm has better estimation performance. [ABSTRACT FROM AUTHOR]

This paper studies the voltage regulation problem of Buck converter under current constraint. Firstly, to speed up the voltage regulation, a nonlinear algorithm is proposed to make the output voltage track the reference voltage quickly in a finite time. Meanwhile, to protect the devices in the circuit, the current-constraint problem is also considered in the finite-time controller design. In addition, the situation of load change is also considered. The proof of finite-time stability of the total closed-loop system is given and its validity is fully tested. Finally, simulation and experiment verify that the system has excellent dynamic performance and steady-state performance. [ABSTRACT FROM AUTHOR]

In this paper, a distributed, non-cooperative and dynamic load-balancing algorithm is proposed in the context of multi-commodity adversarial network equilibria with constrained providers' capacities. The algorithm is proven to converge to a generalised Wardrop user-equilibrium, referred to as Beckmann equilibrium in the literature, in which, for each commodity, the latencies of the unsaturated providers are equalized. The algorithm is then used as a Multi-connectivity algorithm in the context of 5G heterogeneous networks, in which the user equipments are able to use different access networks simultaneously to increase the transmission capacity and/or to improve the transmission reliability. The proposed controller provides a solution for dynamic traffic steering by distributing the traffic load over the available heterogeneous access points, considered as capacity providers. Simulation results validate the approach. The developed network simulator is available as an open-source environment De Santis et al. (2020). [ABSTRACT FROM AUTHOR]

This paper focuses on designing a leader-following event-triggered control scheme for a category of multi-agent systems with nonlinear dynamics and signed graph topology. First, an event-triggered controller is proposed for each agent to achieve fixed-time bipartite consensus. Then, it is shown that the Zeno-behavior is rejected in the proposed algorithm. To avoid intensive chattering due to the discontinuous controller, the control protocol is improved by estimating the sign function. Moreover, a triggering function is proposed which avoids continuous communication in the event-based strategy. Finally, numerical simulations are given to show the accuracy of the theoretical results. [ABSTRACT FROM AUTHOR]