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This paper proposes a non‐singular fixed‐time fault‐tolerant adaptive tracking control method for a kind of multi‐input and multi‐output nonlinear system, which is in form of non‐strict feedback and is subject to actuator faults. The major contribution, compared to other practical fixed‐time fault‐tolerant control methods, is that the fixed time can be calculated precisely. By applying fuzzy logic systems, the identification of unknown nonlinearities is achieved, and by the boundness of fuzzy basis functions, the algebraic loop problem is avoided. Based on the fixed‐time stability theory, the practical fixed‐time fault‐tolerant adaptive tracking control method is proposed with adaptive back‐stepping control technology, in which the control singular problem is overcome by applying the adding power integration method. By the Lyapunov function theory, the practical fixed‐time stability of the closed‐loop system is theoretically proved. And even in the case of unknown actuator faults, the tracking error of the controlled system for the given command signal can still converge to a small neighbourhood of zero within a fixed time, regardless of the system initial state. Finally, the proposed control method is successfully implemented on a multi‐motor control platform, proving its effectiveness, and multiple comparative simulations are carried out, demonstrating its superiority. [ABSTRACT FROM AUTHOR]

This paper presents a study of the cooperative optimal swarm control problem for two‐order multi‐agent systems with partially unknown nonlinear functions. Unlike traditional approaches that consider a single error, this paper proposes to use multi‐order errors in the performance index function to achieve optimal control performance. Additionally, different proportional coefficients are assigned to illustrate the varying influences of each sequence error, and a two‐order cooperative (TOC)performance index function is designed. To address the influence of unknown nonlinear functions, a swarm control system based on sliding mode control with an actor‐critic network is constructed, which increases the applicability of the proposed method to a variety of dynamic models. Furthermore, to alleviate the computational pressure caused by the multi‐order errors in the TOC performance index function, a new reinforcement learning (RL)‐based sliding mode swarm controller is designed. The stability of the proposed controller is demonstrated using the Lyapunov function. Finally, the control model and control rate are applied to a quadrotor unmanned aerial vehicle system, and simulation results demonstrate that the multi‐agent systems can effectively achieve swarm control. Impact Statement: This paper proposes a reinforcement learning‐based sliding mode control strategy for the cooperative optimal swarm control problem, where the nonlinear functions of two‐order multi‐agent systems are only partially known. In addition, we also propose a cooperative performance index function, which takes into account multi‐order errors for optimizing the performance. This contribution is significant for research in sliding mode control strategies and error co‐optimization. [ABSTRACT FROM AUTHOR]

This work aims to address two issues that often exist in least square regression (LSR) models for classification tasks, which are (1) learning a compact projection matrix for feature selection and (2) adopting relaxed regression targets. To this end, we first propose a sparse regularized LSR framework for feature selection by introducing the ℓ 2 , 1 regularizer. Second, we utilize two different strategies to relax the strict regression targets based on the sparse framework. One way is to exploit the -dragging technique. Another strategy is to directly learn the labels from the inputs and constrain the distance between true and false classes simultaneously. Hence, more feasible regression schemes are constructed, and the models will be more flexible. Further, efficient iterative methods are derived to optimize the proposed models. Various experiments on image databases intend to manifest our proposed models have outstanding recognition capability compared with many state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we propose a heterogeneous swarm control method based on a two‐layer communication topology, which solves the problems of great difficulty in controller design and high computational complexity of communication topology in large‐scale heterogeneous swarm control. Firstly, we divide large‐scale heterogeneous swarms into several independent isomorphic sub‐swarm and design corresponding sub‐swarm controllers, which dramatically reduces the difficulty of swarm controller design. Secondly, we introduce a two‐layer communication topology to treat the followers in the first‐layer communication topology as leaders in the second‐layer communication topology, which establishes the connection between each sub‐swarm and the global leader and reduces the computational complexity of the communication topology. Finally, based on the two‐layer communication topology, the swarm controllers in the first‐layer and second‐layer communication topologies are designed using the sliding mode and dynamic surface methods. The Lyapunov function is designed to prove its stability. The simulation results verify the effectiveness of the controllers. [ABSTRACT FROM AUTHOR]

This article deals with a practical distributed hybrid differentiation flowshop scheduling problem (DHDFSP) for the first time, where manufacturing products to minimize makespan criterion goes through three consecutive stages: 1) job fabrication in first-stage distributed flowshop factories; 2) job-to-product assembly based on specified assembly plan on a second-stage single machine; and 3) product differentiation according to customization on one of the third-stage dedicated machines. Considering the characteristics of multistage and diversified processing technologies of the problem, building new powerful evolutionary algorithm (EA) for DHDFSP is expected. To achieve this, we propose a general EA framework called distributed co-evolutionary memetic algorithm (DCMA). It includes four basic modules: 1) dual population (POP)-based global exploration; 2) elite archive (EAR)-oriented local exploitation; 3) elite knowledge transfer (EKT) among POPs and EAR; and 4) adaptive POP restart. EKT is a general model for information fusion among search agents due to its problem independence. In execution, four modules cooperate with each other and search agents co-evolve in a distributed way. This DCMA evolutionary framework provides some guidance in algorithm construction of different optimization problems. Furthermore, we design each module based on problem knowledge and follow the DCMA framework to propose a specific DCMA metaheuristic for coping with DHDFSP. Computational experiments validate the effectiveness of the DCMA evolutionary framework and its special designs, and show that the proposed DCMA metaheuristic outperforms the compared algorithms. [ABSTRACT FROM AUTHOR]

In this article, an adaptive swarm control within saturated input based on the nonlinear coupling degree is proposed. Swarm control is a forward study in kinematics and dynamics of the swarm system. However, the coupling degree in the previous work can only manifest whether the agents in the swarm being connected or not, ignoring the connection strength. As a result, the nonlinear coupling degree is proposed, which is more suitable for practical engineering than the previous coupling degree. Based on the nonlinear coupling degree, we put forward novel swarm kinematics and dynamics. Besides, the effects of input saturation and nonlinear dynamics should be considered for this novel swarm control based on the nonlinear coupling degree. Therefore, we introduce the backstepping method to design an adaptive swarm controller. With this controller, the input saturation auxiliary system is designed to reduce the effects of input saturation, and a radial basis function neural network (RBF-NN) is introduced to approximate the nonlinear dynamics. To overcome the differential explosion in the backstepping method, a command filter is put forward to reduce the amount of calculation and reduces the difficulty of the controller design. It is proved that the proposed controller ensures stability based on the Lyapunov stability theory. Finally, the simulation results of a multiagent system composed of six omnidirectional mobile robots illustrate the validity of the proposed controller. [ABSTRACT FROM AUTHOR]

In this article, we propose the dynamic coverage control method based on K-means. In the traditional coverage control, Voronoi partition method is used to assign the coverage positions for intelligent units. However, the Voronoi partition method requires that the space to be covered is compact and convex, and it is difficult to realize the coverage control of high-dimensional space. Therefore, in this article, we propose a dynamic coverage planning algorithm based on K-means, which relaxes the requirements on the coverage objects and can calculate the optimal coverage positions of the intelligent units. In addition, we also design a dynamic coverage control law based on discrete sliding mode control to drive the intelligent units to the optimal coverage positions. Through the combination of planning algorithm and control law, the optimal coverage control performance can be achieved for the specified targets and the specified area, which can be discrete, nonconvex, and high dimensional. The stability of the planning algorithm and the control law are proved by theoretical deduction. The effectiveness and superiority of the dynamic coverage control method are verified by two examples. [ABSTRACT FROM AUTHOR]

This article proposes the fuzzy swarm control based on sliding-mode strategy with self-organized omnidirectional mobile robots (OMRs) system. In previous work, swarm control is realized by using sliding-mode strategy. However, chattering in the OMRs system is very serious. To overcome this drawback, a new fuzzy swarm control for OMRs system is designed, which introduces the fuzzy logic to replace switching control in traditional sliding-mode control (SMC). The fuzzy SMC (FSMC) can weaken chattering for sliding-mode strategy in the OMRs system. However, it is difficult to design swarm control law for OMRs because of model uncertainties, external disturbance and the introduction of fuzzy logic. The FSMC is proposed in this article and its stability is analyzed. Simulation and experiment results show that the fuzzy swarm control based on sliding-mode strategy with self-organized OMRs system can realize stability asymptotically and chattering is weakened. [ABSTRACT FROM AUTHOR]

Variable-length license plate segmentation and recognition has always been a challenging barrier in the application of intelligent transportation systems. Previous approaches mainly concern fixed-length license plates, lacking adaptability for variable-length license plates. Although objection detection methods can be used to address the issue, they face a series of difficulties: cross class problem, missing detections, and recognition errors between letters and digits. To solve these problems, we propose a machine learning method that regards each character as a region of interest. It covers three parts. Firstly, we explore a transfer learning algorithm based on Faster-RCNN with InceptionV2 structure to generate candidate character regions. Secondly, a strategy of cross-class removal of character is proposed to reject the overlapped results. A mechanism of template matching and position predicting is designed to eliminate missing detections. Moreover, a twofold broad learning system is designed to identify letters and digits separately. Experiments performed on Macau license plates demonstrate that our method achieves an average 99.68% of segmentation accuracy and an average 99.19% of recognition rate, outperforming some conventional and deep learning approaches. The adaptability is expected to transfer the developed algorithm to other countries or regions. [ABSTRACT FROM AUTHOR]

This paper studies the communication smooth switching problem of time-varying formation control. When the formation system performs topology switching, sudden changes in communication will cause communication failure and affect formation control. To avoid this adverse impact, we introduce a smooth transition function of communication topology. At the same time, for the formation system with unknown nonlinear dynamics, an adaptive backstepping controller combining the backstepping control method and the fuzzy logic system is designed. Then, based on Lyapunov stability theory, we prove the stability of the designed controller. The results of the simulation experiments can verify the effectiveness of the designed controller. [ABSTRACT FROM AUTHOR]

In this article, we propose the intelligent decision making and bionic movement control of self-organized swarm. Swarm intelligent decision making can provide swarm path planning. Bionic movement control of self-organized swarm can simultaneously generate swarm formation, realize formation switching, and drive swarm while avoiding collision and preserving the existing communication topology. When the swarm topology switches, the swarm intelligent decision making is introduced to find out the optimal route between the initial position and destination for each intelligent unit. We propose an enhanced genetic algorithm (GA) to realize swarm intelligent decision making. Avoiding collision and preserving the existing communication topology should be achieved during the process of swarm moving. As a result, we design a new swarm motion potential function, which can realize bionic movement control. The stability of bionic movement control is proved to be effective in the collision avoidance, connectivity preservation, and formation generation. Simulation results and experiment results show that the enhanced GA and swarm motion potential function are effective. [ABSTRACT FROM AUTHOR]

In this article, we propose a smooth transition in communication for swarm control with formation change. The communication topology in a swarm is automatically constructed by the relation-invariable persistent formation, which is a method of a self-organized system. Based on the communication topology, intelligent units in swarm systems can solve difficult tasks which would be hardly completed by a single intelligent unit. Intelligent units in the swarm are controlled to realize swarm behaviors, such as aggregation, dispersion, and switching formation when the swarm moves. The switching function in the traditional swarm control with formation change is step function and the transient switching may result in communication failure, transient velocity change, energy waste, and actuator damage. To overcome transient switching in traditional methods, we design smooth transition in communication. The proposed method guarantees the intelligent units could be connected in the communication, which can ensure the communication security of swarm system during the process of communication topology changing. Introduction of smooth switching function in swam control makes it difficult to design the controller. The swarm control with smooth transition in communication, which is based on sliding mode control, is designed and analyzed. Finally, a simulation is carried out to verify the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

In this article, we propose the swarm control for a self-organized system with fixed and switching topology, which can realize aggregation, dispersion, or switching formation when swarm moves. The self-organized system can automatically construct the communication topology for intelligent units in swarm. Swarm control can realize aggregation and dispersion of intelligent units based on its communication topology when swarm moves. The proposed swarm control, in which distances between the related intelligent units are time varying, is different from traditional swarm consensus or swarm formation maintenance. To design swarm control, we define the normalization adjacency matrix and normalization degree matrix based on communication topology. The communication topology is automatically generated based on relation-invariable persistent formation. Depending on whether the communication topology changes or not, the swarm control can be classified as fixed topology and switching topology. Then, the swarm control with fixed and switching topology is designed and analyzed, respectively. The swarm control can realize stability asymptotically when topology is fixed and realize stability in finite time when topology is switched. The simulation results show that the proposed approaches are effective. [ABSTRACT FROM AUTHOR]

This paper presents the generation strategy, motion planning, and switching topologies of a distance-based leader–follower relation-invariable persistent formation (RIPF) of multiagent systems (MASs). An efficient algorithm is designed to find out if a persistent formation can be generated from a rigid graph. Derived from the properties of a rigid graph, the algorithm to generate RIPF from any initial location is presented. In order to generate different RIPFs in the switching topology, state and transition matrices are introduced. To achieve the minimum agent-movement among RIPFs, a downward-tree combinatorial optimization algorithm is presented. In the end, with the selected minimum agent-movement RIPF, a control law is designed to drive initial RIPF to desired RIPF with given distances among agents. Simulation results show the proposed generation method, control law, and downward-tree are effective to realize the desired formation. [ABSTRACT FROM AUTHOR]

This article presents an optimal evolution strategy for continuous strategy games on complex networks via reinforcement learning (RL). In the past, evolutionary game theory usually assumed that agents use the same selection intensity when interacting, ignoring the differences in their learning abilities and learning willingness. Individuals are reluctant to change their strategies too much. Therefore, we design an adaptive strategy updating framework with various selection intensities for continuous strategy games on complex networks based on imitation dynamics, allowing agents to achieve the optimal state and a higher cooperation level with the minimal strategy changes. The optimal updating strategy is acquired using a coupled Hamilton-Jacobi-Bellman (HJB) equation by minimizing the performance function. This function aims to maximize individual payoffs while minimizing strategy changes. Furthermore, a value iteration (VI) RL algorithm is proposed to approximate the HJB solutions and learn the optimal strategy updating rules. The RL algorithm employs actor and critic neural networks to approximate strategy changes and performance functions, along with the gradient descent weight update approach. Meanwhile, the stability and convergence of the proposed methods have been proved by the designed Lyapunov function. Simulations validate the convergence and effectiveness of the proposed methods in different games and complex networks.

A safe time-varying formation (TVF) control framework is proposed in this article for heterogeneous multiagent systems under the constraints of denial of service (DoS) attacks, noncooperative dynamic obstacles, and input saturation. The framework integrates both the cyber-layer and physical-layer components to address the challenges posed by these adverse conditions. In the cyber-layer, a distributed resilient observer is provided based on a control Lyapunov function (CLF)-quadratic program (QP). This observer estimates a reference exosystem, effectively decoupling heterogeneous dynamics from unsafe networks and optimizing the system resilience against DoS attacks. At the physical-layer, for the first time, a collision-free TVF controller is presented based on the CLF-exponential control barrier function-QP. The controller guarantees high-order heterogeneous agents' operation safety under noncooperative obstacles and input saturation. The effectiveness and advantages of the proposed algorithms are verified through the comparative simulations and experiments conducted on a physical system comprising unmanned aerial vehicles and unmanned ground vehicles.

In this article, we propose bionic swarm control based on second-order communication topology (SOCT) inspired by the migration of birds, which solves the difficulty in constructing communication topologies and high-computational complexity in controlling large-scale swarm systems. To realize bionic swarm control, there are three problems supposed to be solved. First, the adjacency matrix and the Laplacian matrix in traditional methods cannot be applied to SOCT directly, which should be redesigned. Second, sub-swarm systems formed based on 2-order communication topology (2-OCT) and independently distributed with each other also need to be put forward to reduce computational complexity. At last, the followers in 1-order communication topology (1-OCT) are set as the leaders of sub-swarm systems in 2-OCT. As a result, coupling in large-scale swarm systems would be reduced. The bionic swarm controller is designed through the backstepping method. In this case, the stability of bionic swarm controller is proven by the designed Lyapunov function. The simulations show the efficiency of the designed bionic swarm controller. And the tracking-containment control based on SOCT with 42 swarm members is realized.

This article addresses a distributed time-varying optimal formation protocol for a class of second-order uncertain nonlinear dynamic multiagent systems (MASs) based on an adaptive neural network (NN) state observer through the backstepping method and simplified reinforcement learning (RL). Each follower agent is subjected to only local information and measurable partial states due to actual sensor limitations. In view of the distributed optimized formation strategic needs, the uncertain nonlinear dynamics and undetectable states may jointly affect the stability of the time-varying cooperative formation control. Furthermore, focusing on Hamilton-Jacobi-Bellman optimization, it is almost incapable of directly dealing with unknown equations. Above uncertainty and immeasurability processed by adaptive state observer and NN simplified RL are further designed to achieve desired second-order formation configuration at the least cost. The optimization protocol can not only solve the undetectable states and realize the prescribed time-varying formation performance on the premise that all the errors are SGUUB, but also prove the stability and update the critics and actors easily. Through the above-mentioned approaches offer an optimal control scheme to address time-varying formation control. Finally, the validity of the theoretical method is proven by the Lyapunov stability theory and digital simulation.