This paper introduces a fully distributed model-free adaptive control method, addressing the tracking problem for multiple-high-speed trains (MHSTs) featuring sensor resolution and utilizing round-robin protocol. Sensor resolution is one of the basic characteristics of almost all sensors and an important indicator of sensor performance in practical applications, but it has not been given enough attention in the study of MHSTs. Since MHSTs have unknown dynamical properties, firstly, the dynamic linearisation technique is used to obtain the data relation description of MHSTs. Secondly, to address the issue of data transmission pressure caused by limited network bandwidth in Train-to-Train (T2T) direct communication, the round-robin protocol can be adopted for effective alleviation. On this basis, a distributed model-free adaptive control strategy is proposed to achieve the bounded formation tracking of MHSTs with sensor resolution under the round-robin protocol. Finally, the effectiveness of the proposed control scheme is verified by simulation examples.

This paper investigates a data-driven finite-time cooperative control problem for nonlinear multi-agent systems (MASs) and develops a distributed model-free adaptive prescribed-performance control strategy based on partial-form dynamic linearization (PFDL). First, a finite-time prescribed performance function is introduced to transform the distributed output errors, ensuring that the prescribed performance constraints are satisfied. Next, an improved dynamic linearization method is derived to convert the transformed error dynamics into an equivalent linear data-driven model, which facilitates controller synthesis. On this basis, a finite-time performance index is constructed and a distributed model-free adaptive cooperative control algorithm is proposed. Moreover, a dual-channel event-triggering mechanism with separate triggering rules for control inputs and measured outputs is designed to reduce the communication burden. Rigorous analysis establishes finite-time convergence of the distributed output errors and boundedness of the control inputs. Numerical simulations demonstrate the effectiveness and robustness of the proposed method.

In numerous applications of multi-agent systems (MASs), e.g., social networks and biological networks, the relationship between agents may shift from competition to cooperation or vice versa. With that in mind, this paper investigates flocking behavior of MASs with evolving cooperation-competition relationships. The relationship between neighboring agents is characterized by a state-dependent nonlinear function: competition is triggered when the state discrepancy between agents exceeds a predefined threshold, whereas cooperation is maintained otherwise. A complete analysis is conducted on flocking behavior using the infinite products of substochastic matrices. As algebraic conditions regarding agent states and cooperative ranges are established to ensure the emergence of flocking behavior, and a lower bound for the convergence rate of flocking behavior is established. Finally, the theoretical results are validated through numerical simulations and an indoor multi-UAV system platform.

As renewable energy sources and electric vehicles (EVs) gradually integrate into the power system, modern grids have evolved into complex large-scale networked control systems. The increasing complexity of the system internal structure presents greater challenges for load frequency control (LFC). To address these challenges, this paper proposes an enhanced model-free adaptive control (MFAC) approach based on system operating data. Initially, this study constructs a dynamic linearized relationship between system output, input and wind power. Then, leveraging pseudo-partial derivatives (PPD) of the I/O data and the partial derivatives of system output with respect to wind power data, a data-driven MFAC algorithm is designed using observer techniques. Given that additional communication burdens may result from data-driven methods, this paper introduces an event-triggered mechanism based on the system output saturation to reduce communication bandwidth. The theoretical analysis thoroughly outlines the design process of the proposed control algorithm and rigorously proves the system stability. Finally, multiple sets of experiments are conducted to validate the effectiveness of the proposed algorithm. The results demonstrate that the event-triggered MFAC (ETMFAC) algorithm effectively mitigates the impact of load disturbances on the power system, ensuring frequency stability. Sensor fault experiments are also carried out to further evaluate the algorithm’s robustness.

Escalating cybersecurity risks in communication networks pose severe challenges to the stability and reliability of microgrid control systems. This paper investigates the load frequency control problem of nonlinear networked wind-power microgrids under denial-of-service (DoS) attacks and event-triggered communication. A data-driven prediction framework is first established to construct a control-oriented dynamic linearization data model for the underlying nonlinear system. Sub-sequently, a novel event-triggered model-free adaptive predictive control (ET-MFAPC) algorithm with time-varying parameters is proposed, which integrates multi-step adaptive prediction and receding-horizon optimization. The proposed algorithm features several key innovations: 1) An output-related auxiliary variable is designed to circumvent unavailable pseudo-partial derivatives (PPDs) and their sign constraints. 2) An adaptive predictive compensation module is developed to mitigate the impacts of DoS attacks by reconstructing hijacked data packets. 3) An attack-aware online optimization mechanism is formulated to adaptively tune controller gains for improved system performance. Furthermore, a comprehensive security analysis is conducted based on a model-independent data energy function, and explicit boundary conditions are derived for tolerable attack frequency and duration. Finally, the effectiveness and robustness of the proposed method are validated through simulation studies.

The coordination of multiple high-speed trains (MHSTs) is considered an effective means to enhance train tracking accuracy and operational efficiency. In this study, the model free adaptive control (MFAC) of MHSTs under false data injection attacks (FDIAs) and input limits is investigated within the framework of data-driven control. The advantage of this control scheme lies in its ability to mitigate performance degradation caused by model inaccuracies. Firstly, a dynamic model for MHSTs is established and then transformed into an equivalent linearized form that solely relies on input and output (I/O) data. Secondly, considering power limitations in the traction network and external FDIAs during direct train-to-train (T2T) communication, a data-driven coordinated MFAC scheme for MHSTs is developed. Finally, the effectiveness of the controller, coordination performance among MHSTs, as well as the impact of external constraints on MHSTs are verified through numerical simulations.

This paper investigates the distributed localization problem in wireless sensor networks by adopting the barycentric coordinate method based on angle measurement. First, all sensor nodes are divided into two categories: anchor nodes with known locations and non-anchor nodes with unknown locations. On this basis, each non-anchor node calculates its barycentric coordinates relative to its neighbor nodes through angle measurement technology, and constructs an iterative equation for location estimation based on the local information exchange mechanism. Subsequently, corresponding localization algorithms are designed for two typical network deployment scenarios: non-anchor nodes are distributed inside the convex hull of anchor nodes and randomly distributed outside the convex hull. By introducing the convergence analysis method of sub-stochastic matrix multiplication, it is theoretically proved that the proposed distributed iterative localization algorithm can achieve progressive and precise localization of non-anchor nodes in both above two network deployment scenarios. Finally, the effectiveness of the proposed method is verified by numerical simulation experiments. The results show that the method can achieve high localization accuracy in both above two types of sensor network structures.

The reliance on open communication networks poses significant challenges to power systems. This paper studies a prescribed performance-based scalable distributed event-triggered model-free adaptive control (DETMFAC) scheme for multi-area load frequency control (LFC) systems that are integrated with wind-energy, analyzing both scenarios without and with jamming attacks. First, a transformed-error-based dynamic linearization model (DLM) for distributed outputs is developed to handle the unknown system dynamics under performance constraints. By directly incorporating the DLM and the prescribed performance function into the criterion function, we derive a novel prescribed performance-based DETMFAC scheme. This algorithm improves the control performance while overcoming the problems caused by global topology information and model dependence as in the traditional distributed LFC design approaches by introducing a parameter adaptation mechanism. Additionally, a novel jamming attack model based on the signal-to-interference-plus-noise ratio (SINR) is introduced to illustrate the interaction between the power system and the malicious attacker in a Stackelberg game framework. Then a secure scheme with attack compensation is proposed to mitigate the negative attack impact by reconstructing the LFC commands via linear estimation. Experimental results for a four-area power system demonstrate the designed scheme. Note to Practitioners—LFC is a fundamental strategy for frequency regulation in networked power systems. However, traditional LFC systems are vulnerable to cyber-attacks, tracking performance constraints, and bandwidth limitations due to the interconnection between different subareas. This paper proposes a prescribed performance-based DETMFAC scheme and a secure control scheme with a resilience mechanism to effectively address these challenges in a coordinated manner, integrating an event-trigger, parameter identifier, attack compensator, and input update law. The control input is only updated when the triggering condition is met, based on the distributed output and identified adaptive parameters. In the case of an attack, the event-trigger ceases operations and the attack compensator generates an estimated input signal. Importantly, the proposed LFC scheme relies solely on local input and output data (i.e., model-free) and can be implemented in a fully distributed architecture (i.e., scalable). Thus, practitioners can seamlessly integrate the designed strategies with existing LFC systems to enhance performance, security, and efficiency. The results provide a valuable reference for scalable, cost-effective and secure distributed data-driven control design of multi-area LFC systems and promote related application research.

Overhead cranes are important transportation equipments in practice, however, their existing control methods have encountered many difficulties in applications due to the underactuation, input limitation and computation complexity. This paper proposes an adaptive fixed-time control scheme for the underactuated overhead crane with multiple uncertainties to deal with the above challenges simultaneously. A coordinate change is employed to address the underactuated structure by reformulating the crane dynamics as a strict-feedback system. A series of time-varying tuning functions are designed to guarantee the input signal varies within a small range to meet the practical input requirement of the overhead crane system. Moreover, a second-order nonlinear tracking differentiator (NLTD) is set up to avoid the repetitive derivative calculation of the virtual controllers. Then, an adaptive law is designed to tackle multiple uncertainties with no need of introducing any other control algorithms but only the single one of itself. Further, a light computational adaptive fixed-time control scheme is proposed by consisting of the tuning functions, NLTD, and the adaption law to achieve a fast location of the overhead crane system. The simulation experiments illustrate the effectiveness of the presented method.

For cooperative control of multiple high-speed trains (MHSTs), a new model free adaptive predictive iterative learning cooperative control (MFAPILCC) method is proposed in this paper. Firstly, the unknown nonlinearity of the MHSTs is handled with the full form dynamic linearization (FFDL) technology along the iteration domain. Then with the predictive mechanism, the unknown bounded time-iteration-varying external disturbances are estimated and predicted, and also be incorporated into the controller design, which achieves better disturbance rejection and system stability. Specially, under the proposed MFAPILCC method, apart from the convergence of tracking error of system could be guranteed, the modeling error of system which practically makes a difference in the cooperative tracking performance of MHSTs also could be guranteed to converge to zero. The convergence of the modeling error and speed tracking error of the system under the constructed method could be guranteed through rigorous theoretical derivation, while the distance between adjacent trains is always maintained within a safety range. Finally, a simulation on the Chinese Railway High-Speed Train (CRH-3) operation control system is supplied to validate the efficacy and superiority of the proposed MFAPILCC method for cooperative control of MHSTs. It is obvious that the theoretical justification and simulation effects either reflect efficacy of the proposed MFAPILCC method.

This article considers the point-to-point tracking control problem for a class of unknown nonlinear discrete-time systems with output saturation. A novel data-driven finite-iteration learning control algorithm is proposed to achieve bounded tracking errors within limited iteration. First, considering the case that the model of the nonlinear discrete-time system is unknown, the relationship between the output of the system and the control inputs at these given points is derived using recursive evolution in the time domain. Then, the dynamic data-driven model of the system is established using iterative domain dynamic linearization techniques. Second, a finite finite-iteration learning algorithm based on the fractional power of error information is designed, and the finite-iteration convergence of the proposed algorithm is rigorously proven in theory. Finally, the effectiveness of the proposed method is validated by simulation results.

This study proposes an active defense strategy against denial-of-service (DoS) attacks to address the secure event-triggered control of multiarea load frequency control (LFC) systems. A novel interval secure event-triggered mechanism (ISETM) is introduced, integrating event-triggered control with cybersecurity mechanisms under the software defined network (SDN) framework. ISETM generates not only a triggering instant but also a secure triggering interval (STI) simultaneously. The STI sent to the SDN control plane is an estimation time interval generated by the Taylor expansion and model-based prediction method. During this interval, the SDN control plane programs OpenFlow switches to filter attack traffics, ensuring delayed but secure triggering transmission. Under ISETM conditions represented by two systems of inequalities, a multiarea LFC system is modeled as a delay system incorporating a triggering error based on the Taylor expansion. To achieve performance of the established LFC system, a criterion is derived using the Lyapunov-Krasovskii functional method. A codesign approach is provided to solve the proposed ISETM control (ISETC) gains through linear matrix inequality (LMI) techniques. Finally, simulations validate the effectiveness and advantages of our proposed method.

Abstract Lower versions of EfficientDet (such as D0, D1) have smaller network structures and parameter sizes, but lower detection accuracy. Higher versions exhibit higher accuracy, but the increase in network complexity poses challenges for real-time processing and hardware requirements. To meet the higher accuracy requirements under limited computational resources, this paper introduces SpanEffiDet based on the channel adaptive frequency filter (CAFF) and the Span-Path Bidirectional Feature Pyramid structure. Firstly, the CAFF module proposed in this paper realizes the frequency domain transformation of channel information through Fourier transform and effectively extracts the key features through semantic adaptive frequency filtering, thus, eliminating channel redundant information of EfficientNet. Simultaneously, the module has the ability to compute the weights across the channels and at fine granularity, and capture the detailed information of element features. Secondly, a two-way characteristic pyramid network multi-level cross-BIFPN, which can achieve multi-layer and multi-nodes, is proposed to build cross-level information transmission to incorporate both semantic and positional information of the target. This design enables the network to more effectively detect objects with significant size differences in complex environments. Finally, by introducing generalized focal Loss V2, reliable localization quality estimation scores are predicted from the distribution statistics of bounding boxes, thereby improving localization accuracy. The experimental results indicate that on the MS COCO dataset, SpanEffiDet-D0 achieved an AP improvement of 3.3% compared to the original EfficientDet series algorithms. Similarly, on the PASCAL VOC2007 and 2012 datasets, the mAP of SpanEffiDet-D0 is respectively 1.66 and 2.65% higher than that of EfficientDet-D0.

Abstract To solve the problem of insufficient feature fusion between the deep and shallow feature layers of the original YOLOX algorithm, which resulting in a loss of object semantic information, this paper proposes a YOLOX object detection algorithm based on attention and bidirectional cross-scale path aggregation. First, an efficient channel attention module is embedded in the YOLOX backbone network to reinforce the key features in the object region by distinguishing between the importance of the different channels in the feature layer, thus enhancing the detection accuracy of the network. Second, a bidirectional cross-scale path aggregation network is designed to change the information fusion circulation path while increasing the cross-scale connections. Weighted feature fusion is used to learn the importance of the different path input features for differentiated fusion, thereby improving the feature information fusion capability between the deep and shallow layers. Finally, the SIOU loss function is introduced to improve the detection performance of the network. The experimental results show that on the PASCAL VOC2007 and MS COCO2017 datasets, the algorithm in this paper improves mAP by 2.32% and 1.53% compared with the original YOLOX algorithm, and has comprehensive performance advantages compared with other algorithms. The mAP reaches 99.44% on the self-built iron ore metal foreign matter dataset, with a recognition speed of 56.90 frames/s.

In this paper, we consider the need for deployment in the long-distance safe longitudinal formation control task when the connected autonomous vehicle (CAV) platoon is subjected to malicious cyber attacks. To ensure the safe, orderly, stable and efficient driving performance of the vehicle platoon, a novel distributed data-driven control (DDDC) approach for a homogeneous connected autonomous vehicle platoon under false data injection (FDI) attacks is investigated. First, an FDI attacks detection and compensation mechanism is designed to detect whether the received position signals are under attack or not and compensate the attacked position signals. Then, a novel DDDC approach for the vehicle platoon longitudinal formation control is developed by using the compensation data from the designed attack compensation mechanism and a dynamic linearization data model. Theoretical analysis verifies that the proposed DDDC method can ensure the internal stability (IS) and string stability (SS) of the homogeneous platoon subjected to FDI attacks. Finally, the effectiveness and practicality of the proposed DDDC approach are validated through a group of comparative simulations subjected to random FDI attacks of equal frequency and magnitude.Note to Practitioners—This work aims to solve the vehicle platoon long-distance safe longitudinal formation control task subjected to malicious FDI attacks. FDI attacks can achieve their destructive purposes by processing intercepted information and injecting false data into the original information. Existing literature overly relies on a priori knowledge of network attacks, yet in practice it is difficult to capture the true intentions of attackers in advance. For multi-channel V2V communication networks, it is even more important to design a resilient and accurate distributed controller strategy for such unpredictable and specific network attacks. Therefore, this paper proposes a data-driven distributed longitudinal formation control strategy with attack detection and compensation mechanism. The proposed strategy is shown to be able to ensure the safe longitudinal formation control task for the homogeneous CAV platoon suffering from FDI attacks. In addition, the stability of the CAV platoon is then investigated while the attacked signals are detected and cleaned, and it is shown to guarantee the internal stability of a single vehicle and the string stability of the platoon.

This article investigates the issues of state estimation and state estimation-based stabilization for Boolean control networks (BCNs). Unlike previous state observers, this article proposes an optimal state estimator by designing a particular input sequence for the first time, where the maximum-minimum method is employed such that the state of BCNs can be uniquely estimated in short time steps. A minimum reconstructible state set (MRSS) is constructed to determine this input sequence. Next, based on the estimated state, a finite-time stabilization scheme is proposed by constructing a switching controller consisting of three stages. A controller is first developed to estimate the state of BCNs in finite-time steps, and a state reachable controller is also provided to make the state of BCNs reachable to a given equilibrium point. Subsequently, a constant controller is further developed to stabilize the state of BCNs to the equilibrium point. Finally, an oxidative stress response model is used to illustrate the effectiveness of the proposed results.

An indirect–direct secure control (IDSC) method is proposed for load frequency control (LFC) of multi-area power system under false data injection attacks. In the indirect secure control, based on the inherent local security of primary frequency control, an estimation system in power plant is established to reconstruct LFC commands. In the direct secure control, PI-based LFC scheme is adopted with a new robust performance index. Thus, a complementary secure strategy realized by IDSC is developed from combining resilience to cyber-physical disturbances and elimination of attack intrusion. We adopt mixed$H_{\infty }/H_{2}$control method to formulate and design interconnected IDSC and decentralized IDSC. Simulations of the three-area power systems are carried out to verify the validness of our proposed IDSC method.

This paper investigates a distributed data-driven event-triggered fault-tolerant control for a connected heterogeneous vehicle platoon with sensor faults under the vehicle-to-vehicle (V2V) communication network. First, a sensor fault diagnosis scheme based on the high-gain observer is designed to detect, estimate and compensate for the fault signal. Then, the measurement signals with sensor faults are recovered, and the reconstructed system can be modeled by the full-form dynamic linearization (FFDL) technique. To obtain reliable vehicle data communication with the efficient use of network resources, an event-triggered mechanism based on the formation error is established, and then a distributed data-driven controller can be designed to accomplish the platoon formation control task. Theoretical analysis demonstrates that the proposed distributed event-triggered fault-tolerant control method can realize the task of ensuring the safe formation control of the platoon system under some sensor faults. Finally, a simulation of a platoon with three faulty vehicles is made to verify the effectiveness and real-time performance of the proposed method.

This article discusses the problem of nonuniform running length in incomplete tracking control, which often occurs in industrial processes due to artificial or environmental changes, such as chemical engineering. It affects the design and application of iterative learning control (ILC) that relies on the strictly repetitive property. Therefore, a dynamic neural network (NN) predictive compensation strategy is proposed under the point-to-point ILC framework. To handle the difficulty of establishing an accurate mechanism model for real process control, the data-driven approach is also introduced. First, applying the iterative dynamic linearization (IDL) technique and radial basis function NN (RBFNN) to construct the iterative dynamic predictive data model (IDPDM) relies on input-output (I/O) signal, and the extended variable is defined by a predictive model to compensate for the incomplete operation length. Then, a learning algorithm based on multiple iteration errors is proposed using an objective function. This learning gain is constantly updated through the NN to adapt to changes in the system. In addition, the composite energy function (CEF) and compression mapping prove that the system is convergent. Finally, two numerical simulation examples are given.

In this article, an improved model-free adaptive control (iMFAC) is proposed for discrete-time multi-input multioutput (MIMO) nonlinear systems with an event-triggered transmission scheme and quantization (ETQ). First, an event-triggered scheme is designed, and the structure of the uniform quantizer with an encoding-decoding mechanism is given. With the concept of partial form dynamic linearization based on event-triggered and quantization (PFDL-ETQ), a linearized data model of the MIMO nonlinear system is constructed. Then, an improved model-free adaptive controller with the ETQ process is designed. By this design, the update of the pseudo partitioned Jacobean matrix (PPJM) estimates and control inputs occurs only when the trigger conditions are met, which reduces the network transmission burden and saves the computing resources. Theoretical analysis shows that the proposed iMFAC with the ETQ process can achieve a bounded convergence of tracking error. Finally, a numerical simulation and a biaxial gantry motor contour tracking control system simulation are given to illustrate the feasibility of the proposed iMFAC method with the ETQ process.

This paper addresses the event-triggered model free adaptive control (MFAC) problem for unknown nonlinear systems under denial-of-service (DoS) attacks, where the design and analysis are discussed under the data-driven framework. Firstly, by using the novel pseudo partial derivative, the nonlinear systems are converted into an equivalent data-relationship model. Then, the DoS attacks are described as limited by their frequency and duration, and without any more specific assumptions about the attack structure or strategy. Next, a novel event-triggered MFAC scheme is proposed. By employing the Lyapunov stability theory, the stability performance is analyzed. Furthermore, a compensation algorithm is designed to against the adverse impact brought by the DoS attacks. Finally, simulations including a numerical example and a load frequency control (LFC) example for multi-area power systems are given to demonstrate the validity and applicability of the proposed schemes.

The bipartite formation control for the nonlinear discrete-time multiagent systems with signed digraph is considered in this article, in which the dynamics of the agents are completely unknown and multi-input multi-output (MIMO). First, the unknown nonlinear dynamic is converted into the compact-form dynamic linearization (CFDL) data model with a pseudo-Jacobian matrix (PJM). Based on the structurally balanced signed graph, a distance-based formation term is constructed and a bipartite formation model-free adaptive control (MFAC) protocol is designed. By employing the measured input and output data of the agents, the theoretical analysis is developed to prove the bounded-input bounded-output stability and the asymptotic convergence of the formation tracking error. Finally, the effectiveness of the proposed protocol is verified by two numerical examples.

This article investigates the problem of event-triggered model-free adaptive iterative learning control (MFAILC) for a class of nonlinear systems over fading channels. The fading phenomenon existing in output channels is modeled as an independent Gaussian distribution with mathematical expectation and variance. An event-triggered condition along both iteration domain and time domain is constructed in order to save the communication resources in the iteration. The considered nonlinear system is converted into an equivalent linearization model and then the event-triggered MFAILC independent of the system model is constructed with the faded outputs. Rigorous analysis and convergence proof are developed to verify the ultimately boundedness of the tracking error by using the Lyapunov function. Finally, the effectiveness of the presented algorithm is demonstrated with a numerical example and a velocity tracking control example of wheeled mobile robots (WMRs).

This article addresses an important problem of how to improve the learnability of an intelligent agent in a strongly connected multiagent network. A novel spatial-dimensional linear dynamic relationship (SLDR) is developed to formulate the spatial dynamic relationship of an agent with respect to all the related agents. The obtained SLDR virtually exists in the computer to describe the input-output (I/O) relationship in the spatial domain and an iterative adaptation mechanism is developed to update the SLDR using I/O information to show real-time dynamical behavior of multiagent systems with nonrepetitive initial states. Subsequently, an SLDR-based adaptive iterative learning control (SLDR-AILC) is presented with rigorous analysis for iteration-variant formation control targets. Not only the 3-D dynamic behavior of the multiagent network but also the control protocols of the communicated agents are incorporated in the learning mechanism and thus strong learnability of the proposed SLDR-AILC is achieved to improve control performance. The proposed SLDR-AILC is a data-driven scheme where no explicit model structure is needed. Simulations with strongly connected topologies verify the theoretical results.

This article presents an event-triggered data-driven load frequency control (LFC) method for multiarea interconnected power systems via model-free adaptive control, where the dynamic model of the power system is assumed to be unknown completely. By introducing the dynamic linearization technique for the unknown power system, an equivalent data relationship model between the area-control-error (ACE) data and the input signal is established. Then, a data-driven LFC scheme is developed only relying on the input and output data of the power system. Meanwhile, an event-triggered strategy is also proposed in the design of data-driven LFC such that the communication and computation burden of the system can be reduced. Whether the current instant is the transmission instant is determined by judging the proposed triggering condition at each sampling instant. It is showed that the presented event-triggered data-driven LFC method is independent to any model information of the power system and does not need to measure any state signals. Simulation tests are carried out to verify the effectiveness of the presented control method.

The problem of consensus learning from network topologies is studied for strongly connected nonlinear nonaffine multiagent systems (MASs). A linear spatial dynamic relationship (LSDR) is built at first to formulate the dynamic I/O relationship between an agent and all the other agents that are communicated through the networked topology. The LSDR consists of a linear parametric uncertain term and a residual nonlinear uncertain term. Utilizing the LSDR, a data-driven adaptive learning consensus protocol (DDALCP) is proposed to learn from both time dynamics of agent itself and spatial dynamics of the whole MAS. The parametric uncertainty and nonlinear uncertainty are estimated through an estimator and an observer respectively to improve robustness. The proposed DDALCP has a strong learning ability to improve the consensus performance because time dynamics and network topology information are both considered. The proposed consensus learning method is data-driven and has no dependence on the system model. The theoretical results are demonstrated by simulations.

This article studies the resilient control problem for a class of unknown nonlinear systems with fading measurements under malicious denial-of-service (DoS) attacks. The system output is assumed to be transmitted through a fading channel, where the fading phenomenon is described by a Rice fading model. The strategy of the attacker is to periodically interfere with the networked channels to reduce the success rate of data transmissions. First, a dynamic linearization method along the iteration domain is introduced to convert the nonlinear system into an equivalent data-related model. Then, a model-free adaptive iterative learning control (MFAILC) scheme is presented, which is independent of model information. The convergence of the MFAILC scheme is deduced theoretically and the influence of DoS attacks and stochastic fading phenomenon on system stability are also analyzed. Finally, the effectiveness of the design is verified by a numerical simulation and a trajectory tracking example of wheeled mobile robots (WMRs).

This article considers the problem of finite-time consensus for nonlinear multiagent systems (MASs), where the nonlinear dynamics are completely unknown and the output saturation exists. First, the mapping relationship between the output of each agent at the terminal time and the control input is established along the iteration domain. By using the terminal iterative learning control method, two novel distributed data-driven consensus protocols are proposed depending on the input and output saturated data of agents and its neighbors. Then, the convergence conditions independent of agents' dynamics are developed for the MASs with fixed communication topology. It is shown that the proposed data-driven protocol can guarantee the system to achieve two different finite-time consensus objectives. Meanwhile, the design is also extended to the case of switching topologies. Finally, the effectiveness of the data-driven protocol is validated by a simulation example.

In this paper, a novel data-driven predictive iterative learning control (DDPILC) scheme based on a radial basis function neural network (RBFNN) is proposed for a class of repeatable nonaffine nonlinear discrete-time systems subjected to nonrepetitive external disturbances. First, by utilizing the dynamic linearization technique (DLT) with a newly introduced and unknown system parameter pseudopartial derivative (PPD) and designing a new RBFNN estimation algorithm along the iterative learning axis for addressing the unknown PPD and the unknown nonrepetitive external disturbances, a data-driven prediction model is established. It is theoretically shown that by constructing a composite energy function (CEF) with respect to the modeling error for the first time, the convergence of the modeling error via the proposed DLT-based RBFNN modeling method can be guaranteed, and the convergence speed is tunable. Then, a DDPILC with a disturbance compensation term is designed, and the convergence of the tracking control error is analyzed. Finally, simulations of a train operation system reveal that even if the train suffers from randomly varying load disturbances and nonlinear running resistance, the proposed scheme can make both the modeling error and the tracking control error decrease successively with increasing operation number.

This paper considers the problem of data driven iterative learning control (DDILC) for a class of nonaffine nonlinear systems subject to data quantization and sensor saturation. Two novel quantized DDILC (QDDILC) algorithms are proposed based on saturated and quantized information of system outputs. The convergence of the proposed QDDILC algorithms is strictly proved and the effects of output saturation and data quantification are also analyzed. It is shown that sensor saturation does not change the convergence property, thus it causes the convergence rate to slow down. For the QDDILC algorithm, data quantization will cause the tracking error to converge to a bound depending on the quantization level. However, the modified QDDILC algorithm, which using the different quantization scheme from QDDILC algorithm, can ensure that the tracking error converges to zero. Illustrative simulations are exploited to verify the theoretical results.

This paper proposes a distributed model free adaptive iterative learning control (MFAILC) method for a class of unknown nonlinear multiagent systems to perform consensus tracking. Here, both fixed and iteration-varying topologies are considered and only a subset of followers can access the desired trajectory in each topology. To design the control protocol, the agent’s dynamic is first transformed into a dynamic linearization model along the iteration axis, and then a distributed MFAILC scheme is constructed to guarantee that all agents can track the desired trajectory. Through rigorous analysis, it is shown that under this novel distributed MFAILC scheme, the tracking errors of all agents are convergent along the iteration axis. The main merit of this design is that consensus tracking task can be achieved only utilizing the input/output data of the multiagent system. Three examples are given to validate the effectiveness of the proposed design.

This brief presents a novel adaptive iterative learning control (ILC) algorithm for a class of single parameter systems with binary-valued observations. Using the certainty equivalence principle, the adaptive ILC algorithm is designed by employing a projection identification algorithm along the iteration axis. It is shown that, even though the available system information is very limited and the desired trajectory is iteration-varying, the proposed adaptive ILC algorithm can guarantee the convergence of parameter estimation over a finite-time interval along the iterative axis; meanwhile, the tracking error is pointwise convergence asymptotically. Two examples are given to validate the effectiveness of the algorithm.

This paper investigates the data-driven consensus tracking problem for multiagent systems with both fixed communication topology and switching topology by utilizing a distributed model free adaptive control (MFAC) method. Here, agent's dynamics are described by unknown nonlinear systems and only a subset of followers can access the desired trajectory. The dynamical linearization technique is applied to each agent based on the pseudo partial derivative, and then, a distributed MFAC algorithm is proposed to ensure that all agents can track the desired trajectory. It is shown that the consensus error can be reduced for both time invariable and time varying desired trajectories. The main feature of this design is that consensus tracking can be achieved using only input-output data of each agent. The effectiveness of the proposed design is verified by simulation examples.