This paper proposes a multi-vehicle cooperative control framework to address cyber-attack interference and environmental uncertainty in complex traffic scenarios. First, to tackle the cooperative adaptive cruise control (CACC) problem under network attacks, an adaptive stochastic hybrid model predictive control (SHMPC) approach is developed by integrating deep deterministic policy gradient (DDPG) with mixed logical dynamics (MLD), enabling effective mode switching and online tuning of control parameters, thereby enhancing system robustness and adaptability. Second, for safe lane changing in dynamic traffic, an stochastic model predictive control (SMPC) controller incorporating a probabilistic control barrier function (PCBF) and iterative linear quadratic regulator (iLQR)-based trajectory planning is constructed to explicitly model environmental uncertainties and enforce safety through probabilistic constraints. Finally, comprehensive simulations validate the effectiveness of the proposed models: under stochastic cyber-attacks, the tracking distance error is reduced by about 11%; for lane change tasks, the success rate is increased by 24.6%, with significantly fewer collisions. The results demonstrate the proposed framework’s superiority in robustness, safety, and response efficiency.

In this article, cycle-time configuration is realized using max-plus algebra for a parallel processing system via a synchronous feedback controller. As a key efficiency metric of parallel processing systems, throughput is determined by cycle time, which is threatened by clock asynchrony and the curse of dimensionality. Using instruction dependency and weak linear independence, the parallel processing system is equivalent to a max-plus nonautonomous system to mitigate the curse of dimensionality caused by numerous processing tasks. Based on the max-plus nonautonomous system, the cycle-time configuration is achieved via a synchronous feedback controller while adhering to time restrictions of the parallel processing system. Numerical simulations validate the effectiveness of the proposed cycle-time configuration in parallel processing systems.

In this paper, we focus on trajectory planning for lane change maneuvers and elaborately consider the impacts of dynamic uncertainties from surrounding vehicles. First, a risk-aware corridor is developed to guarantee that the collision probability is below an acceptable risk level. In contrast to the existing studies, a more succinct and intuitive corridor is constructed based on an explicit safety check theorem. Since it relies solely on the distances between autonomous vehicles and surrounding ones, such a corridor can be easily converted into polytopic constraints and seamlessly integrated with an optimization scheme. Furthermore, a computationally tractable and hierarchical scheme is designed to decide the optimal merging instant and generate the expected trajectory. In this scheme, the trade-off between optimality and complexity in calculating merging instants can be well balanced through feasibility estimation and optimum searching. Furthermore, the trajectory is optimized by model predictive control (MPC) technique. Finally, both numerical simulations and naturalistic validations are conducted to verify the effectiveness. The comparison results indicate the convincing superiority of our scheme in achieving safer driving in an uncertain environment. The code is now available athttps://github.com/Aeolson/code_otp.git

Autonomous driving with reinforcement learning (RL) has significant potential. However, applying RL in real-world settings remains challenging due to the need for safe, efficient, and robust learning. Incorporating human expertise into the learning process can help overcome these challenges by reducing risky exploration and improving sample efficiency. In this work, we propose a reward-free, active human-in-the-loop learning method called Human-Guided Distributional Soft Actor-Critic (H-DSAC). Our method combines Proxy Value Propagation (PVP) and Distributional Soft Actor-Critic (DSAC) to enable efficient and safe training in real-world environments. The key innovation is the construction of a distributed proxy value function within the DSAC framework. This function encodes human intent by assigning higher expected returns to expert demonstrations and penalizing actions that require human intervention. By extrapolating these labels to unlabeled states, the policy is effectively guided toward expert-like be-havior. With a well-designed state space, our method achieves real-world driving policy learning within practical training times. Results from both simulation and real-world experiments demonstrate that our framework enables safe, robust, and sample-efficient learning for autonomous driving. The videos and code are available at: https://github.com/lzqw/H-DSAC.

The uncertainty of wireless communication seriously affects the control performance of the platoon. In this paper, a model predictive control (MPC) method based on dynamic event-triggering is proposed to address the impact of non-ideal communications on multi-vehicle systems. Firstly, a hierarchical platoon architecture combining backbone layer and sub-platoon layers is proposed to achieve efficient coordination among multiple platoons, and a communication-aware control mechanism is designed for each vehicle to effectively mitigate the adverse effects of non-ideal communication scenarios. Secondly, a dynamic event-triggering mechanism is established for each vehicle, which is related to time-varying delay and random packet loss parameters, so as to effectively reduce the frequency of MPC solutions and data transmission. Additionally, based on different communication topologies, distributed model predictive controllers are suggested for backbone layer and sub-platoon layer to achieve precise and comfortable tracking. Then, sufficient conditions are provided to ensure the recursive feasibility of the MPC algorithm and the stability of the platoon system. Finally, extensive simulations are conducted, and the results demonstrate the effectiveness of the proposed algorithm in terms of control performance and low computational complexity under non-ideal communication conditions.

In this article, we propose an event-triggered interval observer fault diagnosis scheme for cyber–physical dc microgrids. First, a distributed interval observer is designed for each distributed generation unit interconnected by a power line. Then, an adaptive periodic event-triggered mechanism is put forward for saving communication cost. With the bounded fault signal and disturbances, the observer gain and event-triggered parameter can be determined by involving disturbance robustness, fault sensitivity, nonnegativity conditions, and regional pole placement for fast fault detection simultaneously. Based on it, the convergence rate of the state error dynamics can be enhanced to implement fast fault detection. It is shown that the proposed interval observer does not need to develop the residual evaluation function and threshold generator since zero is a natural threshold. The effectiveness and superiority of the proposed scheme are verified through simulations.

In this article, an event-triggered distributed sliding mode control (SMC) scheme is developed for dc microgrids composed of multiple boost converters in parallel under limited resource bandwidth. By removing the assumption of ideal voltage sources, the multiple boost converters are modeled as imperfect voltage sources that can be well addressed by SMC. A finite-time control method is suggested to design a distributed control framework for realizing voltage regulation and power sharing with high convergence rate and low steady-state error. Furthermore, an event-triggered strategy is proposed to avoid large bandwidth overhead when carrying out tasks with heavy resource burden. Finally, the effectiveness of bandwidth saving and disturbance rejecting is verified by several comparative simulation results and experimental tests over existing literature.

Lane detection is one of the most critical tasks in autonomous driving. In the past few years, due to the development of deep neural networks, lane detection approaches using onboard sensors like cameras and LiDAR have been proven to be effective ways to improve performance. In contrast to the camera-based scheme, LiDAR-based lane detection exhibits remarkable robustness to varying lighting conditions. This paper proposes a novel cyclic-shifting group convolution (CSGConv) module. Compared with classical group convolution, the proposed CSGConv module can efficiently promote information exchange among different group feature channels and reduce the high computational burden in current LiDAR-based lane detection networks. Then, a cross stage partial CSGConv (CSPCSG) block is designed to enlarge the receptive field and improve detection performance, especially when the lanes are curved or occluded. Subsequently, a LiDAR-based lane detection network, CSGNet, is put forward by integrating the CSPCSG block. Experiments and ablation tests on the dataset illustrate that our strategy achieves the highest 83.9% overall F1-score. Compared with the current state-of-the-art LiDAR-based lane detection method LLDN-RW, our results exhibit 2.5 times faster and 39% reduction of FLOPs, which indicates less resources are required in the online process.

This article investigates an event-triggered interval observer (ETIO) fault detection and isolation method for multiagent systems. First, an event-triggered mechanism is developed to reduce unnecessary communication transmission. Then, a distributed ETIO is designed by combining an interval observer and the proposed event-triggered mechanism. Furthermore, for achieving the desired tradeoff between the robustness to disturbances and the sensitivity to faults, the ETIO is formulated as a multiobjective optimization with$ l_{1}$$/$$ H_{\infty}$performance. Second, a bank of ETIOs are interpreted to isolate the faulty agent on a local agent using only the output information from itself and its neighbors. Comparison result with the existing method is given to highlight the superiority of our methodology. Finally, the multiunmanned aerial vehicles system is utilized as the case research, and specific simulation results are presented.

In this article, tube-based model predictive control (MPC) is investigated for a linear parameter-varying (LPV) system with external disturbances. The LPV system comes from an autonomous vehicle with external disturbances and scheduling parameters in trajectory tracking. An interval predictor is designed for state constraints under external disturbances and scheduling parameters. With a robust controller based on the nominal state obtained by solving MPC, antidisturbance ability and dynamic performance are improved for the LPV system with external disturbances. Both recursive feasibility and input-to-state stability are analyzed for the closed-loop system using the tube-based MPC with the interval predictor. The effectiveness of the tube-based MPC is shown by numerical simulation on trajectory tracking for the autonomous vehicle.

In this paper, the mixed traffic scenario with human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs) on freeway is considered. In this partly known nonlinear system, an optimal control algorithm using adaptive dynamic programming (ADP) is proposed to deal with the communication latence and drivers’ reaction time, which can stabilize the system under the influence of dead zone and saturation with minimal cost. There are three contributions in this paper. Firstly, in the used ADP algorithm, a critic neural network (NN) is designed to estimate the optimal value of the cost function, which is updated using online data instead of pre-gathered data. This means that the proposed controller can adapt to different parameters of different systems. Secondly, the reaction time of human driver and the time latence of the V2V communication are considered as the state and input delay of the nonlinear system, by adding the terms of delayed states to the optimal value function, the influence of the time delay can be minimized in the process of the critic NN updating. Thirdly, the saturation and dead zone of actuator are considered, by designing a new utility function of control value, the control value is limited from being out of the expected range. Under this condition, the stabilization of the overall system and the effectiveness of the proposed algorithm is proved and validated by means of simulation results.

This paper proposes a driver-centric data-driven model predictive control (DDMPC) strategy to improve driving comfort while maintaining driving safety of vehicular platoon. This strategy combines a data-driven model predictive controller and the driver-centric driving policy. The data-driven platoon model involving longitudinal-lateral dynamics is established with subspace identification to alleviate the adverse effects of uncertain dynamics. Then, a subspace predictor-based distributed data-driven model predictive controller is developed for vehicular platoon. To overcome the cutting-corner phenomenon on curved roads, the reference point is shifted from the preceding vehicle to an optimal corridor point behind it. In this way, a driver-centric driving policy is designed with a flexible spacing and soft control constraints to balance driving safety and driving comfort in terms of different driving styles. Finally, several experiments with sixty drivers are carried out on a self-developed vehicular platoon platform. The experimental results demonstrate the effectiveness of the proposed DDMPC strategy.

In this article, a speed coupled lateral strategy is developed to enhance the tracking accuracy of unmanned ground vehicles (UGVs) with variable speed. The characteristics of UGVs are described in a linear parameter-varying (LPV) path-following model, where the speed variation is treated as parameter perturbation. Based on this, a model predictive control (MPC) algorithm is put forward to calculate the desired angular velocity as the upper control input. Moreover, in order to obtain the steering wheel angle driving UGVs, an appurtenant convertor based on kinematic model is designed for the transformation of control signals. The recursive feasibility of MPC is guaranteed via a terminal constraint-free approach, while its stability is deduced through min-max approach. Finally, several experiments are conducted to demonstrate the superiority of speed coupled lateral strategy.

This article addresses the differential privacy problem for second-order multiagent systems (MASs) over signed digraph. To this end, both position and velocity states are disturbed by Laplacian noise. As for structurally balanced case, necessary and sufficient conditions for almost sure bipartite consensus are presented, upon which an$\epsilon$-differential privacy algorithm is designed. Along with the devised setup, the tradeoff between system performance and degree of privacy protection is discussed, and the optimal noise is elaborated as well. The proposed privacy preserving scheme is further extended to the case with structurally unbalanced graph, and criteria for almost sure stability or interval bipartite consensus are induced. Finally, numerical simulations and the application to power systems show the effectiveness of the theoretical findings as well as the developed privacy scheme.

In this paper, a composite longitudinal controller for velocity regulation of unmanned ground vehicles (UGVs) is proposed. First, both the empirical model and mixed logical dynamic model are developed in terms of the experimental data processing method. Second, a composite longitudinal control strategy incorporating steady-state controller and active disturbance rejection state feedback controller is designed for velocity regulation control during acceleration and deceleration. Finally, the stability analysis of velocity error is given with the global sector condition, and the comparative experimental tests show the effectiveness and robustness of the proposed composite longitudinal regulation control strategy.

This article proposes a novel data-driven gap metric fault detection and isolation (FDI) approach for small multiplicative fault. First, the scheme of model-based fault classification and gradation is developed by means of the gap metric. Subsequently, the data-driven gap metric is utilized to detect a small fault via the mechanism model. Furthermore, fault detectability criterion is derived with the help of the developed fault detectability indicator. The relationship between fault detectability indicator and fault detection index is then investigated to analyze fault detection performance. To enhance fault isolability, a solution of appropriate fault cluster center model and radius is provided under the condition of fault isolation. Third, a gap metric fault-tolerant control strategy is exploited to guarantee system stability when a large fault is diagnosed by the developed FDI approach. The speed regulation of dc-motor and dc–dc converter are used for simulation and experiment verifications. Moreover, the comparison results and Monte Carlo simulation demonstrate the superiority and reliability of the proposed method.

The DC-DC converter severs as one of the crucial components in DC microgrid applications. In this paper, a novel low frequency current-mode control strategy is proposed to improve overshoot suppression performance for the boost converter against load current and source voltage disturbances. The control strategy is presented as a cascade dual-loop structure composed of a dynamic current-loop controller with asymmetric saturation and a PI form voltage-loop controller. With the low frequency information of disturbances, the designed dynamic current-loop controller delivers not only promising disturbance suppression but also superior reference current tracking. Simulation and experiment results are provided to illustrate the superiority of the proposed strategy on overshoot suppression.

Of particular interest in this paper is to study the active synchronization (act-synchronization) problem for double-integrator network systems without velocity information. To this end, scaling parameters are suggested to quantify the degree of act-synchronization for different agents. And weighted gains are employed to assure that there is a simple zero eigenvalue with the other ones sharing positive real parts. An auxiliary variable is introduced to mitigate the unavailability on velocity information. Thus only the directed spanning tree requirement in the content of algebraic graph theory is required. This exhibits a clear distinction from signed-graph or scaled consensus as non-unitary signs for scaling parameter and weighted gain are allowed. It is shown that the condition for act-synchronization is related to weighted gain, scaling parameter, the real and imaginary parts of the involved eigenvalues. With the constraint on identical weighted gain and scaling parameter for position and auxiliary variables, it is proven that act-synchronization can be exponentially guaranteed using the Lyapunov-based technique. Finally, the multiple unmanned ground vehicles system and the 10-generator in IEEE 39-bus system are given to facilitate the proposed configuration and the theoretical findings.

This article proposes an integrated approach of model-based and data-driven gap metric fault detection and isolation in a stochastic framework. For actuator and sensor faults, an adaptive Kalman filter combining with the generalized likelihood ratio method is suggested. For component faults, especially incipient faults, the model-based scheme maybe not a good choice due to the existence of disturbances or noises. Hence, a novel data-driven gap metric strategy is presented. The design of the appropriate fault cluster center model and radius via the gap metric technique is put forward to enhance the isolability of the incipient faults. Numerical simulation results are given to demonstrate the effectiveness of the proposed fault detection and isolation algorithm.

In this article, we study the consensus problem in the framework of networked multiagent systems with constraint where there exists antagonistic information. A major difficulty is how to characterize the communication among the interacting agents in the presence of antagonistic information without resorting to the signed graph theory, which plays a central role in the Altafini model. It is shown that the proposed control protocol enables us to solve the consensus problem in a node-based viewpoint where both cooperative and antagonistic interactions coexist. Moreover, the proposed setup is further extended to the case of input saturation, leading to the semiglobal consensus. In addition, the consensus region associated with antagonistic information among participating individuals is also elaborated. Finally, the deduced theoretical results are applied to the task distribution problem via unmanned ground vehicles.

We consider the discrete-time distributed bipartite-consensus problem for multiagent systems subject to measurement noises and time-varying random networks, where the information exchange among the agents can be antagonistic and disturbed by both multiplicative and additive noises. The antagonistic information is characterized by a signed random graph. The main challenge is that the coexistence of multiplicative noise and antagonistic information does not allow the multiplicative noise term to be converted into an error equation. Based on the semi-decomposition method and Lyapunov-based technique, we derive sufficient conditions for stochastic approximation step size to assure the mean square bipartite consensus. Moreover, the convergence rate of the consensus error is explicitly formulated, which is tightly linked to the step size and the eigenvalues of the Laplacian matrix. Finally, we verify the main results via a numerical example.

In this article, a composite nonlinear feedback (CNF) controller with anti-windup extended state observer (ESO) is proposed for path-following control of unmanned ground vehicles (UGVs). To describe steering dynamics accurately, a saturation model with bounded disturbances is developed subject to path-following, lateral dynamic, and steering capability. The anti-windup ESO is investigated for real-time disturbance estimation under saturated input. The CNF controller is designed for path-following. In terms of asymptotically null controllability, a practical semiglobal stabilization result is derived for the closed-loop system with saturated input and bounded disturbances. Experimental results show that the proposed strategy is effective for UGVs.

The stabilization problem for switched linear systems with quantization and event-triggered control is addressed in this article. The state information is quantized and transmitted to the controller. An event-triggered mechanism is presented in terms of the quantizer parameter. The updating law of the dynamic quantizer is suggested for the purpose of guaranteeing the state operating within the quantization range. Meanwhile, the contour line of the Lyapunov function is developed to provide an efficient way for system performance improvement. A co-design strategy is put forward to determine the event-triggered parameter and the quantization scheme simultaneously. The asynchronization issue between the controller and the subsystem is solved by designing a suitable switching signal. The Zeno behavior is eliminated by calculating a lower bound of the event-triggered interval. In the end, a strategy for finding the initial value of the quantization parameter is discussed. Simulations are carried out to demonstrate the efficiency of the proposed method.

This article studies the consensus problem of multiagent systems (MASs) subject to Denial-of-Service (DoS) attacks from the perspective of control. The considered DoS attacks blocking the information exchange in the group of agents pose limitations on the frequency and the duration of them. A distributed observer-based controller is proposed to reconstruct the states of the agents. We prove that MASs under DoS attacks can still achieve consensus by the proposed controller. The scenario where DoS attacks simultaneously jam both the communication networks associated with the controller and the observer is also addressed, and a sufficient condition is obtained to maintain the consensus performance of MASs. Finally, numerical simulations are provided to illustrate the theoretical results.

In this paper, a novel reference input and hysteresis quantizer-based active event-triggered control (RIHQAETC) scheme is proposed for nonlinear networked control systems with quantizer, networked induced delay, and packet dropout. Different from the traditional methods, such a design method is constructed involving the structure of the hysteresis quantizer. In view of the network induced delay and the potential packet dropout, our RIHQAETC method is designed to actively compensate the negative effects caused by these two issues. The corresponding coder and decoder are also excogitated on account of the potential packet dropout based on the proposed triggering mechanism. Furthermore, the transmission of the important triggering information can be ensured as well as the finite-gainL2stability performance. It is demonstrated by an example that our RIHQAETC method presents a more balanced updating frequency between the plant and the controller output sides and reduces the number of total triggering.

This article is concerned with the fixed-time active disturbance rejection control approach for nonlinear systems subject to uncertainties and disturbances. First, a new type of extended state observer (ESO) which can attain fixed-time convergence is established to estimate the states and the total disturbance. Then a fixed-time controller based on the above ESO is developed to achieve high precision control performance. Finally, the proposed method is utilized for the wheeled mobile robot where the experiment validates the effectiveness of the theoretical results.

The approach of feedback iteration in control theory is considered for solving the problem of control allocation algorithm for the case of three moments in this paper. The core of our novel idea is to introduce a closed-loop feedback iteration for obtaining an available solution to control allocation, which is referred to as control theoretic control allocation (CTCA). Two types of CTCA are investigated in this paper: linear case and nonlinear case, where, in the former, the control Lyapunov function (Lyapunov optimizing) and conjugate gradient are introduced, and in the latter, a nonlinear feedback theory is utilized. Thus, the control Lyapunov function and Lyapunov optimizing control allocation (CLF/LOCA), conjugate gradient control allocation (CGCA), and nonlinear CTCA (NCTCA) are proposed in this paper. The performance of each algorithm is evaluated for computational efficiency and accuracy by use of linear or nonlinear aircraft models, respectively. The simulation results show that the proposed CTCA algorithm will terminate in finite steps.

This article considers the synchronization problem of drive–response Lurie systems with sampled output error transmitted through a limited network channel with a one-step packet dropout. Two kinds of strategies, that is, quantizer-based triggered control (without packet dropout) and active quantizer and packet dropout-based triggered control (with one-step packet dropout), are put forward. By thoroughly exploring quantizer, sampling interval, and one-step packet dropout information and merging them together, the quantizer and packet dropout related triggering method is proposed to actively deal with the negative effects caused by packet dropout and sampling sensor. With the proposed method, it is demonstrated that synchronization can be ensured and the output error will always be bounded by the quantizer range. In addition, the relationship between the sensor sampling interval and the triggering parameter is provided to match up with our proposed methods. Lower transmission bit rate is also obtained to save more channel resources. Synchronization of two Chua’s circuits is given as an example to demonstrate the validity of our presented results.

This article is dedicated to the consensus problem for interacting agents of the double-integrator dynamics subject to antagonistic reciprocity, described by negative scalar parameters. To this end, we first show the existence of the weighted gains which play an essential role for solving the consensus problem. Then, we establish the relationship between the weighted gains and scalar parameters to guarantee that the underlying "Laplacian" matrix contains a simple zero eigenvalue and the remaining eigenvalues enjoy positive real parts. Based on the above analysis, we further proceed to solve the considered problem. A major difficulty is that the Laplacian matrices, associated with the position and velocity information, are entirely distinct from each other, leading to the failure of the conventional consensus method for the second-order dynamics. We derive some criteria involving the weighted gains, the scaling parameters, and the real/image parts of the Laplacian matrix of the interaction graph. Moreover, some special frameworks, which have been extensively studied in the literature, are also elaborated on. Compared with the Altafini's model, we do not need to redefine a new Laplacian matrix, and more important, the restriction on the digon sign-symmetry property is removed. It is worth mentioning that the proposed consensus algorithm cannot be deduced by the Altafini's model or its variants. Finally, a wheeled multirobot system is formulated to validate the efficiency of the theoretical results.

This paper considers the observer-based event-triggered output control problem with quantization. Both plant-to-controller (measured output) channel and controller-to-plant (control input) channel have their own dynamic uniform quantizers and samplings. Therefore, the whole system has four asynchronous clocks, two quantizer updating clocks, and two sampling updating clocks. The main contribution of this paper is the proposed self-triggered and event-triggered control method based on these four clocks. The whole system is stabilized by two steps, i.e., system and controller synchronization, and controller stabilization. In the synchronization process, novel event-triggered and self-triggered samplings are proposed in terms of dynamic quantizer parameters. While in the stabilization process, event-triggered sampling is designed based on controller states and quantizer parameters. Moreover, it is proved that the Zeno behavior would not occur. The practicality and efficiency of the proposed method are illustrated by a numerical example borrowed from recent literature.

This paper mainly focuses on synchronization of controlled drive-response systems under Lurie form through a limited channel. The main contribution of this paper is the quantizer-based triggered methodology proposed based on three coders. By exploring coder structure information and fusing quantization and trigger errors together, this strategy can reduce transmission burden while increase synchronization speed concurrently. And the final synchronization error can be bounded within a predetermined fixed value. According to the initial output of drive system, different coders are purposely designed. With the proposed trigger schemes, traditional binary coder with memory cannot achieve desired performance. Meanwhile, it is found that the static coder leads to satisfactory performance when initial drive system output is within limited region. Combining the advantages of the above two coders, a mixed coder is designed to overcome the shortcomings. Moreover, synchronization error and transmission bit rate are thoroughly discussed and Zeno behavior is radically prevented. Finally, simulations for two Chua's circuits are given to illustrate the validity of the proposed method.

The features used in many multimedia analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in highly dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple views. We therefore propose a novel multi-view multi-task feature extraction (MVMTFE) framework for handling multi-view features for image classification. In particular, MVMTFE simultaneously learns the feature extraction matrix for each view and the view combination coefficients. In this way, MVMTFE not only handles correlated and noisy features, but also utilizes the complementarity of different views to further help reduce feature redundancy in each view. An alternating algorithm is developed for problem optimization and each sub-problem can be efficiently solved. Experiments on an real-world web image dataset demonstrate the effectiveness and superiority of the proposed method.

This paper concerns the distributed containment problem of multi-agent systems with linear or linearized node dynamics under a directed communication topology. A new class of distributed control protocol based only on the relative outputs of neighboring agents is designed and utilized to achieve containment. Under the assumptions that each agent is stabilizable and detectable, and for each follower there exists at least one leader that has a directed path to that follower, it is theoretically proved that containment in the closed-loop multi-agent system can be guaranteed. A multi-step containment protocol design procedure is further provided. At last, the convergence rate of the containment in multi-agent systems is discussed. Finally, a simulation example is given to verify the effectiveness of the theoretical results.

This brief deals with the problem of stability analysis for a class of recurrent neural networks (RNNs) with a time-varying delay in a range. Both delay-independent and delay-dependent conditions are derived. For the former, an augmented Lyapunov functional is constructed and the derivative of the state is retained. Since the obtained criterion realizes the decoupling of the Lyapunov function matrix and the coefficient matrix of the neural networks, it can be easily extended to handle neural networks with polytopic uncertainties. For the latter, a new type of delay-range-dependent condition is proposed using the free-weighting matrix technique to obtain a tighter upper bound on the derivative of the Lyapunov-Krasovskii functional. Two examples are given to illustrate the effectiveness and the reduced conservatism of the proposed results.

This paper studies the set invariance analysis and gain scheduled control for linear parameter-varying systems with actuator saturation. Based on a descriptor model transformation of the system, a less conservative condition is given which shows that the previous work is a special case of our main result. An LMI optimization procedure is proposed to obtain the optimal time-invariant state feedback controller and gain-scheduled controller. An example is carried out to illustrate the effectiveness of the results.

This paper presents a new approach for quadratic stabilizability via state feedback of linear switched systems with polytopic uncertainties. State feedback means that the switchings among subsystems are dependent on system states. A less conservative result for quadratic stabilizability is given based on a descriptor model transformation of the system. The main result is given within the framework of linear matrix inequalities. The effectiveness of the proposed method is illustrated with an example to compare with the previous result.

In this paper, the problem of H/sub /spl infin// control is studied for a class of linear time-delay systems subject to sector-bounded input nonlinearities. Both the state feedback controller and the dynamic output feedback controller are presented which guarantee the stability of the closed-loop systems with a pre-specified H/sub /spl infin// norm bound. The main results are given in terms of linear matrix inequalities. Finally, an example is carried out to illustrate the effectiveness of the developed methods.

The problems of quadratic stability and stabilization for continuous-time systems subject to nonlinear perturbations are considered in this paper. By using a descriptor model transformation of the system, some less conservative conditions are given in terms of linear matrix inequalities. An optimization procedure is given to stabilize the systems and at the same time, maximize the bounds on the nonlinearities. Some numerical examples are presented to show the effectiveness of our method.

This paper presents a new approach for robust stability of uncertain fuzzy systems with time-varying delays. Some new delay-dependent criteria are given in terms of linear matrix inequalities. Some extra matrices are introduced to reduce the conservatism of the upper bound of the time-delay. The effectiveness of the proposed method is illustrated with two examples to compare with the previous results.