Designing Proportional-Integral Consensus Protocols for Second-Order Multi-Agent Systems Using Delayed and Memorized State Information

This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state information.Under the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay system.Note that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the system.In this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first derived.Then allowable regions of consensus protocol parameters are estimated.Some necessary and sufficient conditions for determining effective protocol parameters are provided.The designed protocol can achieve consensus and improve the dynamic performance of the second-order multi-agent system.Moreover,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are investigated.Furthermore,some results on proportional-integral consensus are derived for a class of high-order linear time-invariant multi-agent systems.

Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

The Distribution of Zeros of Quasi-Polynomials

PROFESSOR Yitang Zhang, a number theorist at the University of California, Santa Barbara, USA, has posted a paper on arXiv [1] that hints at the possibility that he may have solved the Landau-Siegel zeros conjecture. He has claimed that he has disproved a weaker version of the Landau-Siegel zeroes conjecture, an important problem related to the hypothesis.The conjecture is that there are solutions to the zeta function that do not assume the form prescribed by the Riemann hypothesis. Inspired by his work, in this Perspective, we would like to discuss about the distribution of zeros of quasi-polynomials for linear time-invariant(LTI) systems with time delays.

Bondage and Reinforcement Number of γ_f for Complete Multipartite Graph

The bondage number of γ f, b f(G) , is defined to be the minimum cardinality of a set of edges whose removal from G results in a graph G′ satisfying γ f(G′)> γ f(G) . The reinforcement number of γ f, r f(G) , is defined to be the minimum cardinality of a set of edges which when added to G results in a graph G′ satisfying γ f(G′)< γ f(G) . G.S.Domke and R.C.Laskar initiated the study of them and gave exact values of b f(G) and r f(G) for some classes of graphs. Exact values of b f(G) and r f(G) for complete multipartite graphs are given and some results are extended.

Group-Consensus of Hierarchical Containment Control for Linear Multi-Agent Systems

The existing containment control has been widely developed for several years, but ignores the case for large-scale cooperation. The strong coupling of large-scale networks will increase the costs of system detection and maintenance. Therefore, this paper is concerned with an extensional containment control issue, hierarchical containment control. It aims to enable a multitude of followers achieving a novel cooperation in the convex hull shaped by multiple leaders. Firstly, by constructing the three-layer topology, large-scale networks are decoupled. Then,under the condition of directed spanning group-tree, a class of dynamic hierarchical containment control protocol is designed such that the novel group-consensus behavior in the convex hull can be realized. Moreover, the definitions of coupling strength coefficients and the group-consensus parameter in the proposed dynamic hierarchical control protocol enhance the adjustability of systems. Compared with the existing containment control strategy, the proposed hierarchical containment control strategy improves dynamic control performance. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed hierarchical control protocol.

A Distributed Self-Consistent Control Method for Electric Vehicles to Coordinate Low-Carbon Transportation and Energy

Dear Editor,In this letter, a distributed self-consistent control method to coordinate low-carbon transportation and energy is proposed to address the efficient utilization of regional transportation energy and renewable energy. Specifically, taking into account the coordinated development of transportation, power grids, and renewable energy, transportation energy self-consistent, including instant self-consistent rate and power self-consistent rate。

Shared Control of Highly Automated Vehicles Using Steer-By-Wire Systems

A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect the abnormal driving behaviour and transfer the control of the car to the automatic system designed based on a fault tolerant model predictive control(MPC) controller driving the vehicle along an optimal safe path.The proposed concept and control algorithm are tested in a number of scenarios representing intersection, lane change and different types of driver's abnormal behaviour. The simulation results show the feasibility and effectiveness of the proposed method.

Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due time.It is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production systems.Two objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the problem.To obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy algorithm.The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers.Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

Optimal Control for a Class of Complex Singular System Based on Adaptive Dynamic Programming

This paper presents a new design approach to achieve decentralized optimal control of high-dimension complex singular systems with dynamic uncertainties. Based on robust adaptive dynamic programming(robust ADP) method, controllers for solving the singular systems optimal control problem are designed. The proposed algorithm can work well when the system model is not exactly known but the input and output data can be measured. The policy iteration of each controller only uses their own states and input information for learning,and do not need to know the whole system dynamics. Simulation results on the New England 10-machine 39-bus test system show the effectiveness of the designed controller.

Design of Double Cladding Nearly Zero Dispersion Flattened Nonlinear Photonic Crystal Fiber

We present a design of double cladding nearly zero dispersion flattened nonlinear photonic crystal fiber （PCF） with the core consisting of seven missing holes. The dispersion of the designed PCF fluctuates from -0.28 to 0.29 ps·km^-1·nm^-1 in the range of 1.35-1.795μm and the dispersion slope is -0.0038 ps·km^-1·nm^-2at 1.55μm. Due to its small air-hole to air-hole pitch in the inner cladding, the effective mode area is 6.48μm2 and the effective nonlinearity V is as high as 13.78 W^-1 km^-1 at 1.55μm. Two layers of air-hole rings in the outer cladding ensures the loss of the fundamental mode to be 2.9 dB/km at 1.55μm and two more air-hole rings can further reduce the fundamental mode＇s loss to the level of 4.2 ×10^-3 dB/km.

Secure Synchronization Control for a Class of Cyber-Physical Systems With Unknown Dynamics

This paper investigates the secure synchronization control problem for a class of cyber-physical systems(CPSs)with unknown system matrices and intermittent denial-of-service(DoS)attacks.For the attack free case,an optimal control law consisting of a feedback control and a compensated feedforward control is proposed to achieve the synchronization,and the feedback control gain matrix is learned by iteratively solving an algebraic Riccati equation(ARE).For considering the attack cases,it is difficult to perform the stability analysis of the synchronization errors by using the existing Lyapunov function method due to the presence of unknown system matrices.In order to overcome this difficulty,a matrix polynomial replacement method is given and it is shown that,the proposed optimal control law can still guarantee the asymptotical convergence of synchronization errors if two inequality conditions related with the DoS attacks hold.Finally,two examples are given to illustrate the effectiveness of the proposed approaches.

Tuning-up Learning Parameters for Deep Convolutional Neural Network:A Case Study for Hand-Drawn Sketch Images

Several recent successes in deep learning(DL),such as state-of-the-art performance on several image classification benchmarks,have been achieved through the improved configuration.Hyperparameters(HPs)tuning is a key factor affecting the performance of machine learning(ML)algorithms.Various state-of-the-art DL models use different HPs in different ways for classification tasks on different datasets.This manuscript provides a brief overview of learning parameters and configuration techniques to show the benefits of using a large-scale handdrawn sketch dataset for classification problems.We analyzed the impact of different learning parameters and toplayer configurations with batch normalization(BN)and dropouts on the performance of the pre-trained visual geometry group 19(VGG-19).The analyzed learning parameters include different learning rates and momentum values of two different optimizers,such as stochastic gradient descent(SGD)and Adam.Our analysis demonstrates that using the SGD optimizer and learning parameters,such as small learning rates with high values of momentum,along with both BN and dropouts in top layers,has a good impact on the sketch image classification accuracy.

Distributed Fault-Tolerant Containment Control for Nonlinear Multi-Agent Systems Under Directed Network Topology via Hierarchical Approach

This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.

Adaptive Containment Control for Fractional-Order Nonlinear Multi-Agent Systems With Time-Varying Parameters

This paper investigates adaptive containment control for a class of fractional-order multi-agent systems(FOMASs)with time-varying parameters and disturbances.By using the bounded estimation method,the difficulty generated by the timevarying parameters and disturbances is overcome.The command filter is introduced to solve the complexity problem inherent in adaptive backstepping control.Meanwhile,in order to eliminate the effect of filter errors,a novel distributed error compensating scheme is constructed,in which only the local information from the neighbor agents is utilized.Then,a distributed adaptive containment control scheme for FOMASs is developed based on backstepping to guarantee that the outputs of all the followers are steered to the convex hull spanned by the leaders.Based on the extension of Barbalat's lemma to fractional-order integrals,it can be proven that the containment errors and the compensating signals have asymptotic convergence.Finally,three simulation examples are given to show the feasibility and effectiveness of the proposed control method.

Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems

Radio spectrum awareness,including understanding radio signal activities,is crucial for improving spectrum utilization,detecting security vulnerabilities,and supporting adaptive transmissions.Related tasks include spectrum sensing,identifying systems and terminals,and understanding various protocol layers.In this paper,we investigate various identification and classification tasks related to fading channel parameters,signal distortions,Medium Access Control(MAC)protocols,radio signal types,and cellular systems.Specifically,we utilize deep learning methods in those identification and classification tasks.Performance evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.

From Independence to Interconnection--A Review of AI Technology Applied in Energy Systems

The development of diversified energy structures,distributed energy scheduling models and active participation ability of users,leads to a rapid movement toward energy system in which different energy carriers and systems interact together in a synergistic way.This energy development will face many challenges with the requirements of big data processing capability,professional skill,distributed collaboration and realtime monitoring for the energy system that demands an intelligent and flexible tool to realize the smart energy.Artificial intelligence(AI)technology has become a focus because of its better performance.This paper proposed a classification method that incorporates the intelligence of an independent energy unit(IEU)and the intelligence among interconnected energy units(IEUS)to review the development of AI technology in energy systems.The dominant structures of IEU can be considered from three aspects including perception,decision and implementation to study the optimal strategy for AI methods utilized in IEU.And considering the interaction relationship of IEUS,the AI applied for it can be described by the coordinated relationship and adversarial relationship problems to achieve consensus.By discussing the AI technologies and the potentials of AI in the energy system,some suggestions are presented to improve intelligent technologies for sustainable energy systems in the future.

Multidirection Update-Based Multiobjective Particle Swarm Optimization for Mixed No-Idle Flow-Shop Scheduling Problem

The Mixed No-Idle Flow-shop Scheduling Problem(MNIFSP)is an extension of flow-shop scheduling,which has practical significance and application prospects in production scheduling.To improve the efficacy of solving the complicated multiobjective MNIFSP,a MultiDirection Update(MDU)based Multiobjective Particle Swarm Optimization(MDU-MoPSO)is proposed in this study.For the biobjective optimization problem of the MNIFSP with minimization of makespan and total processing time,the MDU strategy divides particles into three subgroups according to a hybrid selection mechanism.Each subgroup prefers one convergence direction.Two subgroups are individually close to the two edge areas of the Pareto Front(PF)and serve two objectives,whereas the other one approaches the central area of the PF,preferring the two objectives at the same time.The MDU-MoPSO adopts a job sequence representation method and an exchange sequence-based particle update operation,which can better reflect the characteristics of sequence differences among particles.The MDU-MoPSO updates the particle in multiple directions and interacts in each direction,which speeds up the convergence while maintaining a good distribution performance.The experimental results and comparison of six classical evolutionary algorithms for various benchmark problems demonstrate the effectiveness of the proposed algorithm.

Fast Community Detection Based on Distance Dynamics

The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale networks. To identify the community structure of a large-scale network with high speed and high quality, in this paper, we propose a fast community detection algorithm, the F-Attractor, which is based on the distance dynamics model. The main contributions of the F-Attractor are as follows. First, we propose the use of two prejudgment rules from two different perspectives： node and edge. Based on these two rules, we develop a strategy of internal edge prejudgment for predicting the internal edges of the network. Internal edge prejudgment can reduce the number of edges and their neighbors that participate in the distance dynamics model. Second, we introduce a triangle distance to further enhance the speed of the interaction process in the distance dynamics model. This triangle distance uses two known distances to measure a third distance without any extra computation. We combine the above techniques to improve the distance dynamics model and then describe the community detection process of the F-Attractor. The results of an extensive series of experiments demonstrate that the F-Attractor offers high-speed community detection and high partition quality.