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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]

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]

Long short-term memory (LSTM) is a recurrent neural network (RNN) framework designed to solve the gradient disappearance and explosion problems of traditional RNNs. In recent years, LSTM has become a state-of-the-art model for solving various machine-learning problems. This paper propose a novel regularized LSTM based on the batch gradient method. Specifically, the L 2 regularization is appended to the objective function as a systematic external force, effectively controlling the excessive growth of weights in the network and preventing the overfitting phenomenon. In addition, a rigorous convergence analysis of the proposed method is carried out, i.e., monotonicity, weak convergence, and strong convergence results are obtained. Finally, comparative simulations are conducted on the benchmark data set for regression and classification problems, and the simulation results verify the effectiveness of the method. [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 paper, a finite-time convergent continuous action iterated dilemma (CAID) with topological optimization is proposed to overcome the limitations of traditional methods. Asymptotic stability in traditional CAID does not provide information about the rate of convergence or the dynamics of the system in the finite time. There are no effective methods to analyze its convergence time in previous works. We made some efforts to solve these problems. Firstly, CAID is proposed by enriching the players' strategies as continuous, which means the player can choose an intermediate state between cooperation and defection. And discount rate is considered to imitate that players cannot learn accurately based on strategic differences. Then, to analyze the convergence time of CAID, a finite-time convergent analysis based on the Lyapunov function is introduced. Furthermore, the optimal communication topology generation method based on the Deep Q-learning (DQN) is proposed to explore a better game structure. At last, the simulation shows the effectiveness of the proposed method. • The dynamic model of Continuous Action Iterated Dilemma (CAID) with continuous strategy is more realistic. • The convergence time of CAID is analyzed by proposed finite-time convergent analysis method based on the Lyapunov function. • The optimal communication topology generation method based on DQN is proposed to enhance the game structure. [ABSTRACT FROM AUTHOR]

• ε -dragging technique was introduced into BLS to improve the discriminative ability. • Regularization is adopted to optimize network structure. • Theoretical analysis verifies the convergence of the algorithm. • Numerical simulations are conducted to verify the performance of the algorithm. This paper presents the framework of the smoothing group L 1 / 2 regularized discriminative broad learning system for pattern classification and regression. The core idea is to improve the sparseness of the standard broad learning system and improve performance on recognition and generalization. First, the ε -dragging technique is introduced into the standard broad learning system to relax regression targets and enlarge distances between categories. Then, we integrate the group L 1 / 2 regularization to optimize the network architecture to achieve sparsity. For the original group L 1 / 2 regularization, the objective function is non-convex and non-smooth, which is hard for theoretical analysis. Therefore, we propose a simple and effective smoothing technique, i.e.,smoothing group L 1 / 2 regularization, which can effectively eliminate the deficiency of the original group L 1 / 2 regularization. As a result, the final weights projection matrix has a compact form and shows discriminative power capability. In addition, the alternating direction method of multipliers was adopted to optimize the algorithm. The simulation results show that the proposed algorithm has redundancy control capability and improved performance on recognition and generalization. The simulation results proves the efficiency of the theoretical analysis. [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]

In this paper, we propose an adaptive swarm controller for a kind of high-order self-organized system. There are always unknown heterogeneous nonlinear dynamics and unmeasured states in practical systems, which may lead to poor control effects and even system instability. To eliminate these possible problems, a radial basis function neural network approximator is designed to approximate unknown heterogeneous nonlinear dynamics, and a neural network high-gain state observer method is introduced to estimate the unmeasured states of intelligent units. Besides, a novel sliding mode switching approach law is designed to improve sliding mode control. Based on these works, an adaptive swarm controller is proposed to ensure trajectory tracking. With the designed adaptive swarm controller, the high-order self-organized system can achieve aggregation, dispersion, and formation switching in the process of swarm movement. Based on Lyapunov stability theory, we prove the stability of the proposed controller. Finally, according to numerical simulation, the effectiveness of the designed controller is proved. [ABSTRACT FROM AUTHOR]

In this work, a two-level mobile robot swarm system with self-organized formation network is proposed. Initially, based on the position information of the robots, a relation-invariable persistent formation (RIPF) algorithm can automatically organize the swarm network and construct an optimal persistent formation. At the upper formation planning level, the collision-free reference paths of the swarm can be planned for guiding the robots to reach and maintain a desired distance with their neighbors. Then, at the lower formation tracking control level, a neural-dynamic combined model predictive control (MPC) method is applied to drive the swarm moving on the reference paths. The MPC can reformulate the system into a convex minimization problem, which can further be transformed into a constrained quadratic programming (QP) problem such that an efficient QP solver, called primal-dual neural network (PDNN), is implemented to obtain the optimal control inputs online for the robots. In the end, simulation results show the effectiveness of the proposed formation system. [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]

• A light-weight UAV formation control algorithm based on inner and outer loop control is proposed. • An improving ADRC based on prescribed-time extended state observer and broad learning is proposed. • The broad learning is used to dynamically optimize the weights according to the different wind disturbances. Considering that the formation composed of light-weight UAVs is highly susceptible to the interference, which may from the changes in the external environment and the uncertainty of system, a formation control method based on the inner and outer loops is proposed. In the inner loop, the single UAV stable flight can be achieved via controlling the state variables of UAV angles. In the outer loop, we can achieve multi-UAVs cooperative flight through the leader-follower strategy. In this paper, we mainly focus on the stability control of each member UAV, which is the basic composition of formation control. For this purpose, an optimized active disturbance rejection control (ADRC) is proposed which combined an improved prescribed-time extended state observer (PTESO) and weight optimization module based on broad learning. In this way, the single UAV can dynamically adjust the control weights according to different wind degrees, so that the influence of environmental changes on the control system is reduced. Then, the effectiveness and superiority of the proposed formation control methods are verified by simulations. The stability enhancement control method proposed in this paper provides a new and effective theoretical support for the actual control of the light-weight UAV formation, which has a well engineering application prospect. [ABSTRACT FROM AUTHOR]

For constructing a distributed consensus formation scheme for the two-wheel mobile robots with directed communication topology and nonholonomic constraints, in this work, an integrated leader–follower consensus formation framework using neural-network-optimized distributed model predictive control (NNODMPC) strategy is presented. The proposed leader–follower formation framework can be regarded as integrating a consensus trajectories planning subsystem (CTPS) and a consensus tracking subsystem. The CTPS can plan the consensus trajectories for the mobile robots through a leader–follower structure, and in the consensus tracking subsystem, the robots are guided to the consensus. To simultaneously control these two subsystems, the NNODMPC based protocol is applied. The errors and constraints of the integrated system can be incorporated and formulated into a constrained quadratic programming (QP) problem whose optimal solution can be obtained by a primal–dual neural networks (PDNN) QP optimizer. Stability analysis illustrates the convergence of the proposed DMPC-based consensus formation system. The novelties of this work are rooted in the construction of a generalized consensus formation scheme for the practical wheeled robots with nonholonomic constraints, the formulation of an MPC-based distributed consensus controller and the consideration of system constraints. The experimental results on the mobile robots are conducted to verify the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Because of its simple network structure and efficient learning mode, the Broad Learning System (BLS) has achieved impressive performance in image classification tasks. Nevertheless, two deficiencies still exist which have severely limited its learning ability. First, the strict binary labeling strategy used in BLS-based models restricts the model's flexibility. Second, the final broad features are inevitably redundant, which can cause useless features to be learned and reduce the recognition accuracy. In this paper, we propose three discriminative BLS-based models to address these mentioned problems. Specifically, we first integrate the ɛ -dragging technique into the framework of standard BLS to relax the regression targets and propose the ℓ 2 -norm based discriminative BLS (L2DBLS) model. Secondly, to avoid the negative effects of redundant features in L2DBLS, we utilize the ℓ 2 , 1 regularizer to replace the Frobenius norm for feature selection. Furthermore, we propose to constrain the projection matrix of BLS by ℓ 2 and ℓ 2 , 1 regularization simultaneously. As a result, the obtained output weights can be more compact and smooth for recognition. Efficient iterative methods based on the alternating direction method of multipliers are derived to optimize the proposed models. Finally, various experiments on image databases are intended to demonstrate the outstanding recognition capability of our proposed models in comparison with other state-of-the-art classifiers. • Three regularized discriminative BLS models are proposed according to the ɛ -dragging technique and regularization theory. These models can simultaneously learn more flexible labels and compact features for better image classification. • Efficient iterative algorithms are designed to optimize the three proposed models. Moreover, a solid theoretical analysis and experimental verification are carried out to fully demonstrate its effectiveness. • Diverse experiments are conducted to validate that our three proposed models have apparent superiority for image recognition in comparison with other state-of-the-art models. [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]

In this paper, for the multi-agent system, the Nash equilibrium (NE) search problem for non-cooperative game with state constraints under false data injection (FDI) attack is studied. The FDI attack can directly destroy the connectivity between agents, resulting in the performance degradation or even failure for most NE search algorithms. We propose a new distributed NE search method to improve the performance of NE algorithms under FDI attacks. The NE algorithm is designed to find the solution of NE under network attack and make the system recover after the attack. However, most of the existing results only studied the NE algorithm of the non-attacking system, and the existing results rarely take into account whether the system state exceeds the safety limit, which can cause the system to crash. This paper solves the state constraints problem of the multi-agent game under the FDI attack. Finally, the reliability of the algorithm is verified by a numerical result. [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 paper, an improved artificial potential field (APF) method with predictive state is proposed to reduce the influence of communication delay and physical delay on swarm control. The input threshold is introduced to optimize the problem of excessive APF method control input. The control of large-scale swarm systems is challenging due to the complexity of the communication topology. The second-order communication topology is introduced to simplify the coupling relationship of the system. The collision avoidance and formation maintenance control of the swarm system is realized by setting the collision avoidance and formation maintenance potential functions. Based on the proposed improved artificial potential field method, a new distributed controller for swarm system is designed and its stability is analyzed. Numerical simulations are conducted to prove the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]