This investigation introduces a sophisticated robust fuzzy distributed protocol, which synergistically merges the strengths of robust control and fuzzy control to confront the global consensus conundrum in unknown multi-agent systems. The technological ingenuity of this protocol lies in its integration of a seamless switching function, which ensures the robust and effective functionality of the fuzzy protocol across a broad global spectrum. Furthermore, the study delves into the global consensus dilemma in both first-order and second-order stochastic unknown multi-agent systems, outlining the specific design framework for robust fuzzy controllers. To uphold the stability of the closed-loop systems, the investigation innovatively formulates a novel type of Lyapunov function, inspired by the tenets of Lyapunov quadratic form design. Conclusively, through a series of simulation experiments, the investigation substantiates the practical effectiveness of the proposed algorithms. Note to Practitioners—Practitioners in automation, robotics, and distributed decision-making face a significant challenge in achieving global consensus in multi-agent systems amidst uncertainties and disturbances. This research introduces a sophisticated robust fuzzy distributed protocol that integrates robust control and fuzzy control, leveraging a switching function to ensure effectiveness across various scenarios. The study provides a detailed design framework for robust fuzzy controllers in first- and second-order stochastic unknown multi-agent systems, crucial for developing resilient strategies to maintain system stability and performance. Innovatively, a novel Lyapunov function, inspired by Lyapunov quadratic form design, upholds closed-loop system stability, offering a theoretical foundation for control strategies. Simulation experiments confirm the protocol’s practical effectiveness, achieving high-efficiency and reliable global consensus in unknown MASs. Preliminary results suggest promising practical implementation, benefiting practitioners across various fields.

This article presents a novel distributed robust fuzzy control scheme to address the global consensus problem of unknown nonlinear multiagent systems (MASs). By replacing the nonlinear dynamic model constrained by the global Lipschitz condition with a more general system model, the proposed approach enhances applicability. A robust fuzzy control scheme based on a smooth switching function is introduced, effectively resolving the global consensus problem for unknown nonlinear systems. Furthermore, time-varying σ-modification terms are incorporated into the adaptive parameter design, replacing constant terms to avoid asymptotically uniform ultimate boundedness and ensuring global asymptotic consensus of the closed-loop systems. The efficacy of the proposed scheme is demonstrated through simulation results.

For the input-delayed stochastic systems with the states and input quantization, the adaptive stabilization problem is investigated in this article. The whole control scheme design process can be divided into three steps. First, the traditional adaptive neural control scheme is developed for the controlled system. Next, the effective control scheme is proposed for the system with the quantized states. Finally, the adaptive neural control method is developed for the considered system with the states and input quantization. The radial basis function neural network (RBFNN) is applied to approximate the unknown terms online, and the Pade approximation method is introduced to deal with the input-delayed problems. The adaptive neural fault control strategy is presented to address sensor faults and the discontinuity due to the quantized states. Under the constructed controllers, all the closed-loop signals remain semi-globally uniformly ultimately bounded (SGUUB) in mean square. The effectiveness and superiority of the presented control schemes are verified by some simulation results.

This article studies the human-in-the-loop fuzzy iterative learning control of leader-following consensus for unknown mixed-order nonlinear multi-agent systems. The human operator participates in the cooperative control of multi-agent systems, which indirectly affects the followers by directly controlling the leader. Moreover, the leader's input is unknown to all followers. The mixed-order multi-agent systems contain both first- and second-order agents, which include the special case of the second-order multi-agent systems. By using fuzzy logic systems to approximate unknown nonlinear dynamics, a fully distributed fuzzy iterative learning controller with time-varying coupling gain is designed. In the estimation parameters, a$\sigma$-modification related to the number of iterations is designed to ensure the convergence of the closed-loop systems. Based on the new composite energy function, the exact consensus of the closed-loop systems is proved. Finally, the simulation results verify the effectiveness of the designed control algorithm.

This article concentrates on adaptive tracking control of strict-feedback uncertain nonlinear systems with an event-based learning scheme. A novel neural network (NN) learning law is proposed to design the adaptive control scheme. The NN weights information driven by the prediction-error-based control process is intermittently transmitted in the event-triggered context to the NN learning law mainly for signal tracking. The online stored sampled data of NN driven by the tracking error are utilized in the event context to update the learning law. With the adaptive control and NN learning law updated via the event-triggered communication, the improvements of NN learning capability, tracking performance, and system computing resource saving are guaranteed. In addition, it is proved that the minimum time interval for triggering errors of the two types of events is bounded and the Zeno behavior is strictly excluded. Finally, simulation results illustrate the effectiveness and good performance of the proposed control method.

In the complex marine environment, traditional detectors based on the classifiers cannot guarantee the global false alarm control. In this paper, we propose a detector based on dual channel convolutional neural network (DC-CNN) and global false alarm controllable adaptive boosting tree (GFAC-A), which is shortened as DC-CNN-GFAC-A. DC-CNN focuses on the correlation features of radar echoes in time domain and frequency domain, better use multi-dimensional features and show a better feature extraction ability. Through the application of GFAC-A, the false alarm rate is introduced into the algorithm combining decision tree and AdaBoost to achieve the high-performance detection of global false alarm controllable for high-dimensional features. It is heuristics. The combination of DC-CNN and GFAC-A solves the disadvantage that current classifiers cannot meet the conditions of good performance and low false alarm. First, the connectivity information is obtained by using the frequency domain amplitude characteristics of radar echo data, and the recursive information is obtained by using the nonlinear recursive time series characteristics. And the dual-channel datasets of targets and clutter are built. Then, DC-CNN is built to extract and fuse high-dimensional features to obtain feature vectors of targets and clutter. Besides, the performance comparison of different neural network model combinations is carried out. Finally, compared with the traditional threshold-controllable classifiers, the proposed GFAC-A classifier achieves the high detection performance under the global controlled false alarm. The results show that DC-CNN-GFAC-A can achieve 96.491% detection accuracy when the false alarm rate is 10-3, which is superior to other detections.

In this article, complex-valued neural networks (CVNNs) with proportional delays and inhibitory factors are proposed. First, the global exponential stability of the model addressed is investigated by employing the Halanay inequality technique and the matrix measure method. Some criteria are derived to guarantee the global exponential stability of CVNNs with proportional delays and inhibitory factors. The obtained criteria are applicable not only to systems with proportional delays but also to systems with arbitrary delays. Here, the Lyapunov functions are not constructed. Compared with the Lyapunov method, the matrix measure method makes the obtained criteria more concise, and the Halanay inequality makes the analytical procedure more compact. Furthermore, the global exponential synchronization of two neural-network models with proportional delays and inhibitory factors is also studied. By designing a feedback controller and giving some limitation conditions, the drive system and the response system realize global exponential synchronization. Finally, numerical simulation examples are provided to validate the effectiveness of the theoretical results obtained.

In this article, a robust adaptive learning control strategy for uncertain single-input-single-output systems in strict-feedback form and controllability canonical form (CCF) is studied. For the strict-feedback system, the dynamic surface control is introduced while for the controllability canonical system, sliding-mode control is further constructed. The finite-time design is introduced for fast convergence. Under the switching mechanism, the intelligent design and the robust technique work together to obtain robust tracking performance. Once the states run out of the domain of intelligent control, the robust item will pull the states back while inside the neural working domain, the composite learning is developed to achieve higher approximation precision by building the prediction error for the weight update. The closed-loop system stability is analyzed via the Lyapunov approach. Especially for the CCF, the finite-time convergence is achieved while the system signals are globally uniformly ultimately bounded. Simulation studies on the general nonlinear systems and the flight dynamics show that the new design scheme obtains better tracking performance with higher precision and stronger robustness.

A distributed algorithm is presented in this article under directed communication networks, which is used to solve the economic dispatch problem in fixed time in smart grid systems. A new globally fixed-time stability theory (Lemma 3) is first given in this article, which contains a new upper bound for the estimation of the settling time. Moreover, the fixed-time convergence for the proposed algorithm is rigorously proved with the aid of convex optimization theory, the new lemma, and Lyapunov stability theory under the strongly connected and weight-balanced network topology. Finally, numerical simulations show the effectiveness and advantages of the distributed fixed-time optimization algorithm.

Efficient task assignments can significantly improve agent management and reduce communication load and energy consumption. This article investigates the cooperative control problem for multiagent systems with an active task assignment strategy, in which whether an agent exchanges the information with neighbors depends on a perceived mission. By defining a set of missions, a task assignment mechanism for cooperative control problem is first proposed, in which the tasks will be scheduled by intermittent communication signals associated with the actual optimization requirements. By allowing appropriate task assignment conditions, a class of tracking cooperative control protocol is designed and accordingly, the stability of the closed-loop systems under the intermittent communication will be guaranteed. We also consider a case that the communication links between the followers and the leader can be optimized. To maximize the quality of information interaction, a leadership competition mechanism is introduced to design the tracking cooperative control protocol. As an application, cooperative surveillance using a group of rotary-wing air vehicles is considered. Numerical simulation demonstrates the effectiveness of the proposed approaches.

In this paper, a neural network (NN)-based distributed cooperative identification strategy with event-triggered communication is studied for a group of coupled identical nonlinear systems. We develop a distributed cooperative learning law in the context of event-triggered communication, where an agent will transmit its NN weights to its neighbors only when its weight trigger error norm exceeds an exponentially decreasing threshold. It is proven that the estimated weights of all radial basis function NNs converge to a small neighborhood of their optimal values. Therefore, the unknown nonlinear function is approximated along the union of all the system trajectories. It is further proven that there exists a positive minimum interevent interval and Zeno behavior can be avoided. Finally, we give a simulation example to demonstrate these features.

In this paper, an event-based distributed cooperative learning (DCL) law is proposed for a group of adaptive neural control systems. The plants to be controlled have identical structures, but reference signals for each plant are different. During control process, each agent intermittently broadcasts its neural network (NN) weight estimation to its neighboring agents under an event-triggered condition that is only based on its own estimated NN weights. If communication topology is connected and undirected, the NN weights of all neural control systems can converge to a small neighborhood of their optimal values. The generalization ability of NNs is guaranteed in the event-triggered context, that is, the approximation domain of each NN is the union of all system trajectories. Furthermore, a strictly positive lower bound on the interevent intervals is also guaranteed to avoid the Zeno behavior. Finally, a numerical example is given to illustrate the effectiveness of the proposed learning law.

This paper proposes a heterogeneous coupling network framework to address the cooperative tracking control problem for multiagent systems with dynamic interaction topology and bounded intermittent communication. By considering the underlying dynamic interaction topology and introducing the adjustable heterogeneous coupling weighting parameters, a bounded consensus condition of cooperative tracking control is proposed. With considering a bounded intermittent communication condition, a class of intermittent cooperative tracking control protocol is designed based on the combination of the individual agent dynamic and the exchange of information among the agents under an appropriate consensus speed constraint. It is proved in the sense of Lyapunov that the cooperative tracking control for the closed-loop multiagent systems can be achieved under the dynamic interaction topology, an appropriate feedback gain matrix, and the intermittent communication information of all agents. The results are further extended to the information consensus protocol with intermittent coordinated constraint information. Finally, two examples are presented to verify the effectiveness.

This paper investigates the fuzzy adaptive practical tracking problem for a class of nonlinear pure-feedback systems with quantized input signal. In the control scheme design process, the considered system is transformed into a plant with a strict-feedback form by borrowing the mean value theorem of differential, then fuzzy logic systems are used to compensate for some uncertain nonlinearities in the considered plant and the classical adaptive technique is employed to handle some unknown parameters. In the backstepping design, some nonnegative switching functions are introduced to develop the desired fuzzy controller, and Barbalat's lemma is used to analyze the stability and the control performance of the closed-loop system. It can be shown that under the novel adaptive fuzzy controller, all the closed-loop signals are semiglobally uniformly ultimately bounded, and especially the tracking error satisfies the accuracy assigned a priori. A simulation example is presented to verify the effectiveness of the proposed control method.

This paper addresses the leader-follower consensus problem of multivehicle wirelessly networked uncertain systems with nonlinear dynamics and actuator fault and proposes a class of distributed discontinuous communication protocols based only on the relative states among neighboring vehicles. By introducing a novel fault model for multivehicle wirelessly networked uncertain systems, fault tolerant consensus can be achieved with different fault modes of the actuators. It is proved in the sense of Lyapunov that, if the conditions of dwell time and the intermittent communication rate are satisfied, the leader-follower consensus can be achieved for closed-loop multivehicle wirelessly networked uncertain systems with nonlinear dynamics and actuator fault under the topology that frequently but not always contains a spanning tree rooted at the leader. Furthermore, the results are extended to the collision avoidance and formulation control problems. Four examples are presented to demonstrate the effectiveness of the proposed approaches.

This paper studies a distributed machine learning problem by applying a distributed optimization algorithm over an undirected and connected communication network. Each node has its own fuzzy logic system (FLS) based machine whose weights are trained by the proposed FLS-based distributed cooperative learning (DCL) algorithm to reach the optimum of the global cost function. The training process utilizes the data that are distributed among different nodes and cannot be gathered at any node in the network. The main advantages of the FLS-based DCL algorithm are as follows: It has an exponential convergence; it requires a small amount of computation and communication at each iteration step; and the private and confidential information is protected without exchanging raw data between neighboring nodes. These advantages are verified by performing simulation experiments to compare the FLS-based DCL algorithm with the distributed average consensus based learning algorithm, the alternating direction method of multipliers based learning algorithm and the diffusion least-mean square algorithms.

This paper proposes an accurate cooperative control strategy to address the distributed adaptive consensus problem for multiple subsystems of the process industrial plants by constructing a two-layer node-to-node communication framework. In the present framework, each subsystem is modeled by an agent, and all the subsystems and the information flow are regarded as a multiagent uncertain system. By introducing proper assumptions, a class of distributed adaptive consensus protocol for accurate cooperative control is designed by adaptive weighting factors, appropriate feedback gains, and limited state information. It shows that distributed adaptive consensus of accurate cooperative control can be achieved for closed-loop multiagent uncertain systems with the two-layer node-to-node communication framework, if each follower is affected by at least one leader for some uniformly bounded communication time intervals. The results are further extended to nonlinear situations. Two application examples are presented to verify the effectiveness of the proposed approaches.

In this paper, the problem of the global finite-time adaptive stabilization for nonlinearly parametrized systems with multiple unknown control directions is addressed. Different from the previous results, the control directions of the considered systems are completely unknown. Adopting the adding a power integrator design technique, we develop an adaptive switching controller with a tuning parameter. Due to control directions unknown, a novel logic switching regulation is established based on Lyapunov function method to overcome this main obstacle. According to this switching rule, the design parameter is tuned online in a switching way. With the help of the obtained adaptive switching controller, the global finite-time stability of the closed-loop systems is shown. To verify the effectiveness of the control algorithm, a simulation example is presented.

Source localization is a fundamental problem in underwater wireless sensor networks. From the observability analysis, we know that the sensor placement can significantly affect the localization performance. This letter is concerned with the optimal sensor placement for underwater source localization, and a parameter is introduced into the measurement model to represent the distance-dependent noise. The evaluation criterion used to solve the optimal placement is built by the Cramer-Rao lower bound theory. Subsequently, we mainly discuss the case when the distances between the sensors and the source are fixed, and the optimal sensor placement is affected by the relative magnitude of the distances.

This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method.

In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.

In this paper, we put forward a novel approach for change detection in synthetic aperture radar (SAR) images. The approach classifies changed and unchanged regions by fuzzy c-means (FCM) clustering with a novel Markov random field (MRF) energy function. In order to reduce the effect of speckle noise, a novel form of the MRF energy function with an additional term is established to modify the membership of each pixel. In addition, the degree of modification is determined by the relationship of the neighborhood pixels. The specific form of the additional term is contingent upon different situations, and it is established ultimately by utilizing the least-square method. There are two aspects to our contributions. First, in order to reduce the effect of speckle noise, the proposed approach focuses on modifying the membership instead of modifying the objective function. It is computationally simple in all the steps involved. Its objective function can just return to the original form of FCM, which leads to its consuming less time than that of some obviously recently improved FCM algorithms. Second, the proposed approach modifies the membership of each pixel according to a novel form of the MRF energy function through which the neighbors of each pixel, as well as their relationship, are concerned. Theoretical analysis and experimental results on real SAR datasets show that the proposed approach can detect the real changes as well as mitigate the effect of speckle noises. Theoretical analysis and experiments also demonstrate its low time complexity.

This paper considers the problem of cooperative adaptive identification for a class of nonlinear systems via neural networks. The proposed adaptive laws of neural network weights are distributed, and the interconnection topologies are established among identification models in order to share their data on-line. It is proved that if the interconnection topologies are undirected and connected, then all adaptive laws of neural network weights for the same system function can converge to a small neighborhood around their optimal values over a union of sets consisting of system trajectories. Thus, the learned system model has the better generalization capability. A simulation example are provided to verify the effectiveness and advantages of the algorithms proposed in this paper.

A novel-function approximator is constructed by combining a fuzzy-logic system with a Fourier series expansion in order to model unknown periodically disturbed system functions. Then, an adaptive backstepping tracking-control scheme is developed, where the dynamic-surface-control approach is used to solve the problem of “explosion of complexity” in the backstepping design procedure, and the time-varying parameter-dependent integral Lyapunov function is used to analyze the stability of the closed-loop system. The semiglobal uniform ultimate boundedness of all closed-loop signals is guaranteed, and the tracking error is proved to converge to a small residual set around the origin. Two simulation examples are provided to illustrate the effectiveness of the control scheme designed in this paper.

This brief addresses the problem of designing adaptive neural network tracking control for a class of strict-feedback systems with unknown time-varying disturbances of known periods which nonlinearly appear in unknown functions. Multilayer neural network (MNN) and Fourier series expansion (FSE) are combined into a novel approximator to model each uncertainty in systems. Dynamic surface control (DSC) approach and integral-type Lyapunov function (ILF) technique are combined to design the control algorithm. The ultimate uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. Two simulation examples are provided to illustrate the feasibility of control scheme proposed in this brief.

For the first time, this paper addresses the problem of adaptive output-feedback control for a class of uncertain stochastic nonlinear strict-feedback systems with time-varying delays using neural networks (NNs). The circle criterion is applied to designing a nonlinear observer, and no linear growth condition is imposed on nonlinear functions depending on system states. Under the assumption that time-varying delays exist in the system output, only an NN is employed to compensate for all unknown nonlinear terms depending on the delayed output, and thus, the proposed control algorithm is more simple even than the existing NN backstepping control schemes for uncertain systems described by ordinary differential equations. Three examples are given to demonstrate the effectiveness of the control scheme proposed in this paper.

The purpose of this comment is to point out some mistakes in the above paper. It is shown that the main results of the paper cannot stand in general. Also, it is pointed out that after some corrections, the proposed control algorithm is still applicable to a more simple system. For simplicity, all the symbols in this comment are the same as those in the above paper.

An adaptive backstepping neural-network control approach is extended to a class of large-scale nonlinear output-feedback systems with completely unknown and mismatched interconnections. The novel contribution is to remove the common assumptions on interconnections such as matching condition, bounded by upper bounding functions. Differentiation of the interconnected signals in backstepping design is avoided by replacing the interconnected signals in neural inputs with the reference signals. Furthermore, two kinds of unknown modeling errors are handled by the adaptive technique. All the closed-loop signals are guaranteed to be semiglobally uniformly ultimately bounded, and the tracking errors are proved to converge to a small residual set around the origin. The simulation results illustrate the effectiveness of the control approach proposed in this correspondence.