This paper presents a model-agnostic meta-learning (MAML) inspired training framework for a 7-degree-of-freedom (7-DOF) robotic manipulator, equipped with an adaptive controller to perform an object-picking task with unknown payload. Machine learning frameworks typically require large amounts of training data. While traditional meta-learning methods can adapt neural network (NN) parameters with only a few new samples, these approaches are still not fast enough for real-time robotic control tasks. To address this, a task-dependent coefficient is trained to represent the payload, and an adaptive controller is developed to adjust this coefficient in real time. A MAML inspired training algorithm is employed to produce a task-independent neural network that models all unmodeled disturbances. In this study, 10 objects of different weights are used for training, and the manipulator is tested with unknown and new payload. Simulations are conducted to demonstrate the effectiveness of the proposed training and control framework.

Autonomous berthing remains a key challenge for unmanned surface vehicles (USVs), especially in dynamic marine environments where traditional methods relying on GPS, wireless communication, or visual markers face limitations due to signal attenuation and lighting changes. This article proposes a vision-based robust berthing pose estimation framework that integrates infrared light arrays and ArUco fiducial markers, enabling accurate and real-time pose estimation. The YOLO-v5n berth object detection model has been fine-tuned on custom datasets for berthing scenarios, while TensorRT optimization ensures efficient deployment on embedded platforms. The system utilizes adaptive image processing techniques, including Otsu binarization, Canny edge detection, and centroid-based light center localization, to isolate infrared LEDs and extract their geometric features under different illumination conditions. The perspective n-point (PnP) algorithm was used to estimate USV’s relative pose to the light array. The framework has been validated through experiments under different conditions, providing a reliable solution and potential applications for USV autonomous berthing.

Cooperative multi-robot manipulation requires control strategies that achieve precise object trajectory tracking and minimize internal object forces under model uncertainties and external disturbances. This paper proposes a distributed adaptive super-twisting sliding mode impedance (STSMI) control framework for cooperative manipulation. The approach integrates adaptive super-twisting sliding mode control with impedance-based force regulation in task space to ensure robustness, compliance, and stability. Quaternion-based control ensures smooth and stable orientation tracking. The proposed controller balances tracking accuracy and internal force minimization compared to conventional controllers. Simulation results with two 7-degree-of-freedom (DOF) manipulators show improved tracking accuracy, reduced internal forces, and adaptability to various configurations. Experimental validation confirms the controller’s robustness and real-world applicability.

This article addresses the challenge of enhancing collaboration efficiency within a heterogeneous system of multiple unmanned surface vehicles (USVs) while accounting for the impact of ocean currents. In this context, this article introduces an intelligent algorithm called the hexagonal grid-based neural network with parallelogram law (HGNNPL). The algorithm comprises three key components: 1) a bio-inspired neural network (BINN) designed to predict an optimal collision-free path for a multi-USV system, which operates based on hexagonal partitioning grids, ensuring smooth navigation without collisions; 2) an adjustment component plays a crucial role in correcting deviations caused by ocean currents and calculating the associated energy consumption; and 3) an optimal task assignment component responsible for assigning task objectives to the USVs, where distance determined by the BINN and the energy consumption are involved as motion planning costs. This article presents simulation results that compare the performance of the proposed algorithm with an existing algorithm based on square grids, which does not account for the elimination of ocean current effects. These results illustrate the practical effectiveness of the proposed method.

It is well known that a nonlinear system can be represented in a linear lifted feature space according to the Koopman operator theory. However, the approximation errors are always ignored, which may damage the control performance. Therefore, this paper presents a deep Koopman-based two loop control structure, where nonlinearities, uncertainties, and constraints can be handled simultaneously. Namely, a deep Koopman linear model is trained off-line to approximate the dynamic of nonlinear system. Accordingly, an optimization problem is introduced in the outer loop to replan the desired trajectory such that state and input constraints can be satisfied. Considering the model uncertainties introduced by the Koopman linear model, an adaptive robust controller is synthesized in the inner loop to ensure that the optimization result of the outer loop can be strictly tracked. In this way, fast transient response can be reached by the outer loop and the high motion tracking accuracy can be promised by the inner loop. The proposed framework’s advantages and efficacy are evidenced through comparative simulations conducted on a 2-DoF robotic manipulator.

In this paper, the distributed coordination control of path tracking and nash equilibrium seeking of networked automated ground vehicles systems with unknown dynamics is investigated under the framework of graphical games. Different from existing works assuming that the vehicle dynamics are known, each vehicle with completely unknown system dynamics is considered in this paper. To solve this problem, a learning-based data-driven technique is proposed to identify and reconstruct the unknown system matrices. Then, based on the identified system matrices, an offline reinforcement learning (RL) algorithm is proposed to derive both the optimal control policies and the policy iteration solution for graphical games, as well as its corresponding convergence is analyzed. Besides, an online learning algorithm only relying on the online information of states and inputs in an online way is developed to solve the optimal path tracking control problem. As a result, the requirement of relying on the vehicle’s dynamics in the traditional tracking control protocols is completely relaxed by our proposed method. The optimal distributed control policies found by the proposed RL algorithm satisfies the global Nash equilibrium and synchronizes all tracked vehicles to the pinning vehicle. Numerical simulation results are provided to show the effectiveness of the theoretical analysis.

This article considers the output tracking control problem of nonidentical linear multiagent systems (MASs) using a model-free reinforcement learning (RL) algorithm, where partial followers have no prior knowledge of the leader's information. To lower the communication and computing burden among agents, an event-driven adaptive distributed observer is proposed to predict the leader's system matrix and state, which consists of the estimated value of relative states governed by an edge-based predictor. Meanwhile, the integral input-based triggering condition is exploited to decide whether to transmit its private control input to its neighbors. Then, an RL-based state feedback controller for each agent is developed to solve the output tracking control problem, which is further converted into the optimal control problem by introducing a discounted performance function. Inhomogeneous algebraic Riccati equations (AREs) are derived to obtain the optimal solution of AREs. An off-policy RL algorithm is used to learn the solution of inhomogeneous AREs online without requiring any knowledge of the system dynamics. Rigorous analysis shows that under the proposed event-driven adaptive observer mechanism and RL algorithm, all followers are able to synchronize the leader's output asymptotically. Finally, a numerical simulation is demonstrated to verify the proposed approach in theory.

In situations involving teams of diverse robots, assigning appropriate roles to each robot and evaluating their performance is crucial. These roles define the specific characteristics of a robot within a given context. The stream of actions exhibited by a robot based on its assigned role are referred to as the process role. Our research addresses the depiction of process roles using a multivariate probabilistic function. The main aim of this study is to develop a role engine for collaborative multirobot systems and optimize the behavior of the robots. The role engine is designed to assign suitable roles to each robot, generate approximately optimal process roles, update them on time, and identify instances of robot malfunction or trigger replanning when necessary. The environment considered is dynamic, involving obstacles and other agents. The role engine operates hybrid, with central initiation and decentralized action, and assigns unlabeled roles to agents. We employ the Gaussian process (GP) inference method to optimize process roles based on local constraints and constraints related to other agents. Furthermore, we propose an innovative approach that utilizes the environment’s skeleton to address initialization and feasibility evaluation challenges. We successfully demonstrated the proposed approach’s feasibility, and efficiency through simulation studies and real-world experiments involving diverse mobile robots.

This paper presents a novel approach to time-varying formation for multi-agent systems for operation in an unknown environment. The time-varying formation uses a leader-follower system with a dynamic leader selection process. Leaders are determined by calculating the center of formation, and determining the position of a predefined goal point relative to the center. By incorporating a distributed protocol in which each agent calculates an estimate of the center based on incomplete information, the agents are able to determine their roles without requiring a central control system. A role negotiation process is developed for resolving edge cases. Two sets of simulations are conducted; the first set showing the capabilities and limitations of the center of formation estimation method, and the second set showcasing a team of robots navigating a series of environments using the proposed time-varying formation algorithm.

Trust evaluation and trust establishment play crucial roles in the management of trust within a multi-agent system. When it comes to collaboration systems, trust becomes directly linked to the specific roles performed by agents. The Role-Based Collaboration (RBC) methodology serves as a framework for assigning roles that facilitate agent collaboration. Within this context, the behavior of an agent with respect to a role is referred to as a process role. This research paper introduces a role engine that incorporates a trust establishment algorithm aimed at identifying optimal and reliable process roles. In our study, we define trust as a continuous value ranging from 0 to 1. To optimize trustworthy process roles, we have developed a consensus-based Gaussian Process Factor Graph (GPFG) tool. Our simulations and experiments validate the feasibility and efficiency of our proposed approach with autonomous robots in unsignalized intersections and narrow hallways.

This paper investigates the resilient distributed secure output path following control problem of heterogeneous autonomous ground vehicles (AGVs) subject to cyber attacks based on reinforcement learning algorithm. Most existing results are subject to the same attack models for all communication channels, however multiple channels launched by different attackers are considered in this paper. First, a predictor-acknowledgement clock algorithm for each vehicle is proposed to judge whether the communication channel among neighboring vehicles is attacked or not by receiving or transmitting an acknowledgement. Then, a resilient distributed predictor is proposed to predict the pinning vehicle’s state for each vehicle. In addition, a resilient local control protocol consisting of the feedforward state provided by the predictor and the local feedback state of each vehicle is developed for the output path following problem, which is further converted to the optimal control problem by designing a discounted performance function. Discounted algebraic Riccati equations (AREs) are derived to address the optimal control problem. An off-policy reinforcement learning (RL) algorithm is put forward to learn the solution of discounted AREs online without any prior knowledge of vehicles’ dynamics. It is shown that the RL-based output path following control problem of AGVs imposed by cyber attacks can be achieved in an optimal manner. Finally, a numerical example is provided to verify the effectiveness of theoretical analysis.

Teleoperation of multiple agents has the unique advantage to complete tasks with wide range and is an effective solution to help agents avoid obstacles with human intelligence, especially when encounters the local-minima problem. In this article, a novel nonlinear single-master–multislave (SMMS) teleoperation control framework is proposed for multiple mobile agents to achieve obstacles avoidance under delays, nonlinearities, various uncertainties, and nonholonomic constraints. Namely, the slave trajectory planner is designed to cope with the nonholonomic constraints caused by underactuated characteristics of slave agents, while the slave obstacle avoidance planner is designed to cope with obstacles in the environment, which can avoid the obstacles by artificial potential function (APF)-based obstacle avoidance algorithm. Particularly, considering that the APF usually encounters the local-minima problem, a leader selection algorithm is designed for the slave obstacle avoidance planner and a virtual force feedback is designed for the master subsystem, where the slave agents can get rid of local-minima points while teleoperated by human operator with confident force feedback. The global stability of the overall system can be guaranteed under the proposed radial basis function neural network (RBFNN)-based adaptive sliding mode master controller and slave formation controller under delays, nonlinearities and various uncertainties. The comparative experiment is implemented, and the results show the effectiveness of proposed control framework in the achievement of good performance including position tracking, force feedback, and formation and obstacles avoidance while the stability is guaranteed.

This paper considers the distributed time-varying formation tracking control problem of multi-quadcopter systems using affine formation control strategies with multiple virtual leaders. A novel two-layer (formation layer and local control layer) affine formation control structure is established to account for the underactuated nature of the quadcopter dynamics. In the formation layer, a quadcopter is abstracted as a virtual double-integrator agent and affine formation controllers are then designed based on the networked double-integrator dynamics. The resultant virtual affine formation control inputs from the formation layer are converted to the desired attitudes based on the quadcopter dynamics, and a sliding mode controller is then proposed to ensure flnite-time tracking convergence to the desired attitude in the local control layer. Numerical simulations were carried out using a group of six quadcopters in the XY-plane to demonstrate and validate the effectiveness of the developed controllers.

This paper focuses on the distributed consensus seeking of multi-agent systems (MASs) with discrete-time control updating and intermittent communications among agents. Compared with existing linearly coupled protocols, a nonlinear coupled Zeno-free event-triggered controller is first proposed, which is further to project the static and dynamic triggering mechanisms exploited by using the deadband control method. Then, the node-based nonlinear coupled adaptive event-triggered controller with online self-tuning of time-varying coupling weight and its corresponding to static and dynamic deadband-based event-triggered mechanisms are designed, respectively. The exploited adaptive event-triggered controller does not rely on any global information of interaction structure and is implemented in a fully distributed fashion. In addition, two dynamic proposals not only cover existing static strategies as special cases, but also show that the minimal inter-execution time of dynamic one is not smaller than that of static one. Theoretical analysis shows that the proposed static and dynamic deadband-based event-triggered mechanisms can not only ensure the average consensus with Zeno-freeness, but also achieve the data reduction of communication and control. Finally, the proposed algorithms applied to circuit implementation are corroborated to prove its practical merits and validity.

This article studies the problem of dissipativity-based asynchronous state estimation for a class of discrete-time Markov jump neural networks subject to randomly occurring nonlinearities, sensor saturations, and stochastic parameter uncertainties. First, two stochastic nonlinearities occurring in the system are described by statistical means and obey two Bernoulli processes independently. Then, the hidden Markov model is used to characterize the real communication environment closely between the designed estimator and the system model due to the networked-induced phenomenons that also lead to randomly occurring parametric uncertainties of the estimator considered modeled by two Bernoulli processes. A new criterion is established to guarantee that the resulting error system is stochastically stable with predefined dissipativity performance. Finally, we provide a simulation example to validate the theoretical analysis.

This paper proposes a fast terminal sliding mode control (SMC) method for the formation of heterogeneous multi-agent systems (MASs) with a fixed topology using a virtual leader. The MAS consists of quadrotors and two-wheeled mobile robots (2WMRs). Fast terminal SMC is used as a means of directing the agents in order to achieve consensus and formation in a two-dimensional environment. The stability analysis with the resultant system shows that the system is stable given an arbitrary number of agents. Simulation results are shown to demonstrate the effectiveness of the proposed control scheme for a team of three unmanned aerial vehicles (UAVs) and three unmanned ground vehicles (UGVs) in cases with and without disturbances. Experimental results are also provided to help validate the formation controller.

The unprecedented and exponentially growing global senior population is creating an exorbitant and unmet demand for physical rehabilitation. Telerehabilitation with robotic exoskeletons is an emerging, and compelling complementary rehabilitation modality. Some challenges are to overcome the effects of dynamic modeling uncertainties and ensure good tracking performance, stability, safe and compliant motion, and a high degree of telepresence between the two remotely-separated human-robot systems in the presence of nonlinearities, human torques, and communication constraints such as time delays. Two control methods were developed: Adaptive Robust Integral Impedance model (ARII) control and Adaptive Robust Integral Radial Basis Function Neural Networks-based Impedance model (RBFNN-I) control. Both methods implement compliant behaviour using an adjustable impedance model and revealed desirable performance. A novel human torque regulator (HTR) was developed, which provides higher fidelity telepresence for the therapist compared to existing methods to enhance the safety and perception of the closed-loop physical interaction. Unilateral and bilateral simulations were carried out using two-degrees-of-freedom (2-DOF) exoskeletons models and experiments were performed using single-joint robots. Excellent tracking performance, telepresence, and stability was achieved in the presence of large, variable and asymmetric time delays and human torques under numerous parameters variations.

In this paper, the bilateral teleoperation of cooperative manipulators is achieved and experimentally analyzed. The master and slave robots are asymmetrical, and only the master end effector’s task space velocity signals are transmitted through the communication network, while the task space force signals of slave robot are relayed back. A power-based time domain passivity control (PTDPC) approach is employed for the controller design to ensure the passivity of the communication channel in the presence of time-varying delays and are applied to each side of the communication channel at every time constant. This model-free method does not require the dynamic models of the master or slave systems to be known. The slave robot acts as the leader of the remote dual-arm cooperative manipulator system that is used to manipulate a common rigid object. This leader robot is controlled using position control mode to track the trajectory of the master, while the follower robot employs an admittance control method to follow the leader’s motion trend. The follower robot is not required to transmit or receive any communication data, which simplifies the network communication topology. Experimental results are presented to verify the effectiveness and simplicity of the designed framework in the presence of large, time-varying and asymmetric delays.

This paper studies the distributed event-triggered leader-follower formation tracking control problem of general linear multi-agent systems (MASs) with a dynamic leader in a sampled-data setting. A novel asynchronous edge-based event- generator is established for each communication edge to regulate the inter-agent communication at each sampling instant. Then, we propose an edge-state-estimate-based formation tracking algorithm, under which the formation tracking control problem can be formulated as a stability analysis problem of the closed-loop formation error dynamics. The event-generator and formation tracking controller gains can then be co-designed based on the feasible linear matrix inequality (LMI) conditions that guarantee the ultimate boundedness of the closed-loop formation error dynamics. Numerical simulations were carried out using a group of four mobile robots to validate the proposed method.

This paper addresses the distributed event-triggered leader-follower formation tracking control problem for multi- agent systems (MASs) with general linear agent dynamics in a sampled-data environment. A new distributed state-estimate- based event-triggering communication mechanism is proposed to manage the inter-agent communication at each sampling instant. Then, a formation control protocol is designed based on the combined measurement of all locally-available triggered sampled information for each follower agent. The event-generator and formation controller gains are co-designed from the sufficient linear matrix inequality (LMI) conditions that ensure the uniform ultimate boundedness for the closed-loop formation error dynamics. In addition, the free parameters in the derived conditions are optimized to produce event-generator and controller gains to maximize system performance. Lastly, the developed protocols were validated using simulations of a group of linearized miniature quadcopters.

This paper adopts two different approaches, the small-gain technique and the integral quadratic constraints (IQCs), to investigate the synchronization problem of coupled harmonic oscillators (CHOs) via an event-triggered control strategy in a directed graph. First, a novel control protocol is proposed such that every state signal of the CHO decides when to exchange information with its neighbors asynchronously. Then, the resulting closed-loop system based on the designed control protocol is converted into a feedback interconnection of a linear system and a bounded operator, and the stable condition of the feedback interconnection is presented by employing the small-gain technique. In order to better describe the relationship between the input and output, the IQCs theorem is applied to derive the stable condition on the basis of the Kalman-Yakubovich-Popov lemma. Finally, a simulation example is provided to verify the proposed new algorithms.

In this article, the dissipativity-based filtering of the Markovian jump neural networks subject to incomplete measurements and deception attacks is investigated by adopting an event-triggered communication strategy, where the attackers are supposed to occur in a random fashion but obey the Bernoulli distribution. Consider that the information of the system mode is transmitted to the filter over the communication network that is vulnerable to external attacks, which may lead to the undesired performance of the resulting system by injecting malicious information from the attackers. As a result, the filter has difficulty completing information from the original system. Besides, an event-triggered communication mechanism is introduced to reduce the communication frequency between data transmission due to the limited network resources, and different triggering conditions corresponding to different jump modes are developed. Then, based on the above considerations, the sufficient condition is derived to ensure the stochastic stability and dissipativity of the resulting augmented system although the deception attacks and incomplete information exist. A numerical simulated example is provided to verify the theoretical analysis.

Common assumptions in most of the previous nonlinear networked leader–follower systems are that the leader is provided as a constant signal and that the controllers are designed for either the joint-space regulation or the translational Cartesian-space motion control. This article addresses the control issue of the delay-induced horizontal shift effect when the leader is moving with a changing speed. A novel mixed-type feedback is introduced to reduce the horizontal shift effect so as to minimize tracking errors experienced by the follower agents. In this article, we also study the complete pose control using a nonsingular terminal sliding mode (NTSM) method. A stability analysis is provided to prove the finite-time boundedness of the tracking error signals. Quantitative evaluations of the multiple effects on the error bound are conducted to facilitate the subsequent control design to improve the performance. Numerical simulation results of a team of two degree-of-freedoms (DOFs) manipulators demonstrate the improved tracking performance of the end effectors with small bounded errors which are affected by network delays, maximum assigned accelerations, and the selection of control gains. The experimental results are provided to demonstrate the performance of the developed controller.

This paper studies the event-triggered containment control (CC) problem of multiagent systems with a directed graph, where the followers' communication graph is an undirected graph. A novel event-triggered CC protocol is presented to schedule communications between agents, which depends on both local states and control inputs. Different from traditional approaches using broadcasts, a unique property of the proposed protocol is that it enables agent-to-agent data transmission. To do so, each communication link is associated with a specific local event. The estimated relative interagent states are transmitted over a specific link only when the related input-based event is detected. We develop sufficient conditions to solve CC problem under this protocol and extend it to the adaptive event-triggered protocol that does not require the global knowledge on the smallest positive eigenvalue of the Laplacian. For both protocols, we derive positive low bounds on the interevent time intervals generated by individual events, which eliminate the “Zeno phenomenon.” Feasibility of the proposed algorithm is verified by an example.

In this paper, an adaptive mixed feedback controller using fuzzy logic control (FLC) is proposed to improve the performance of the synchronization of a group of leader-follower agents with unknown time-varying communication delays. With the aim to improve the overall system performance while ensuring the stability under delays, Lyapunov-based methods and linear matrix inequality (LMI) techniques are applied to design a distributed control policy that uses agent state information with and without estimated self-delays. FLC is applied to online tune the control gains and weight of the self-delayed state in the controller as a nonlinear function of the total consensus error. Numerical simulations of a leader-follower group of five and seven DC motors are carried out to demonstrate the effectiveness and improvement in overall performance of the proposed controller.

Telerehabilitation with Robotic Exoskeletons is an emerging technology aimed at assisting to restore patients' mobility using a master and slave robotic system. Some of the main challenges for achieving good tracking performance, stability and transparency in telerehabilitation are nonlinearities, uncertain and time-varying parameters in the robot and human models, and communication delays. Additionally, a paramount challenge for this technology is ensuring safe and compliant interaction between the robots and the human operators. This paper presents a novel control approach utilized during unilateral and bilateral teleoperation which address these challenges. An Adaptive Impedance Controller is designed using Lyapunov-based methods for the master exoskeleton while a Proportional-Derivative Impedance Controller is implemented on the slave exoskeleton. Subsequently, a torque limiter technique was implemented on the master side to ensure stability in the presence of time delays. The advantages of these controllers are that they address unknown dynamics, incorporate designed impedance response for rehabilitation applications, and are simple to implement. Simulations for two two-degree-of-freedom robotic exoskeletons are provided to demonstrate the effectiveness of these methods in both passive and assistive telerehabilitation modes, and with time delays.

This paper applies an input-based triggering approach to investigate the secure consensus problem in multiagent systems under denial-of-service (DoS) attacks. The DoS attacks are based on the time-sequence fashion and occur aperiodically in an unknown attack strategy, which can usually damage the control channels executed by an intelligent adversary. A novel event-triggered control scheme on the basis of the relative interagent state is developed under the DoS attacks, by designing a link-based estimator to estimate the relative interagent state between intermitted communication instead of the absolute state. Compared with most of the existing work on the design of the triggering condition related to the state measurement error, the proposed triggering condition is designed based on the control input signal from the view of privacy protection, which can avoid continuous sampling for every agent. Besides, the attack frequency and attack duration of DoS attacks are analyzed and the secure consensus is reachable provided that the attack frequency and attack duration satisfy some certain conditions under the proposed control algorithm. “Zeno phenomenon” does not exhibit by proving that there exist different positive lower bounds corresponding to different link-based triggering conditions. Finally, the effectiveness of the proposed algorithm is verified by a numerical example.

This paper addresses the dissipative asynchronous filtering problem for a class of Takagi-Sugeno fuzzy Markov jump systems in the continuous-time domain. The hidden Markov model is applied to describe the asynchronous situation between the designed filter and the original system. Based on the stochastic Lyapunov function, a sufficient condition is developed to guarantee the stochastic stability of the filtering error systems with a given dissipative performance. Two different methods for the existence of desired filter are established. Due to the Finsler's lemma, the second approach has fewer variables to decide and brings less conservatism than the first one. Finally, an example is provided to demonstrate the correctness and advantage of the proposed approaches.

This paper considers the consensus problem of multiagent systems with the input-based triggering condition. A model-based approach is first given to estimate the relative interagent states between intermittent communications instead of absolute states. A novel consensus protocol consisting of the relative interagent states is proposed for the consensus problem. Besides, compared with some results on the triggering condition consisting of state measurement error, a new triggering condition is constructed based on the control input. Then, the consensus protocol is executed by every agent in a fully distributed way, which has the advantage that the controller design is related to the number of agents instead of using global information. Moreover, the bounds of parameters of the controller and the triggering condition can be obtained by the proposed algorithm simultaneously, which depends on the total number of agents in multiagent networks. The proposed control scheme can ensure that states of all agents can achieve consensus. It is shown that “Zeno behavior” does not appear under the proposed algorithm. Finally, an illustrative example is given to verify the proposed method.

In this paper, the problem of distributed event-triggered pinning control for practical consensus of multiagent systems (MASs) with quantized communication based on a directed graph is investigated. The pinning control for practical consensus of MASs with uniform quantizer is first discussed. Then, in order to decrease communication load of interagent, the event-triggered quantized communication protocol is designed. The nonsmooth analysis and Gronwall's inequality approach is used to guarantee the existence of a solution to the resulting closed-loop system. It is shown that practical consensus is reachable through the event-triggered control and converges to a consensus set. Moreover, “Zeno phenomenon” can be excluded. Finally, an example is given to validate the feasibility and efficiency of the proposed new design method.

In this paper, a decentralized sliding-mode control approach is applied to the control tasks of vehicles in platoons. Using the well-known bicycle model, a robust nonlinear observer is introduced to facilitate the controller design, which needs full-state measurements. The vehicles in platoons can be treated as an interconnected system with a special form. Observer gain and controller gain are properly designed. In addition, appropriate linear matrix inequality (LMI) stability conditions by the Lyapunov method are derived to ensure the stability of the system. The main advantages can be summarized as follows: (1) The linear approximation of the nonlinear vehicle model enables various advanced robust control possibilities. (2) The proposed robust control approach with the nonlinear observer ensures the convergence of the whole interconnected system, given that the system is operated within the stable region of linearization. (3) Stability conditions in the form of LMIs for both observer and controller are rigorously derived. Finally, simulation results for three identical vehicles based on the relative bicycle model are demonstrated to show the performance of the approach.

This paper presents a new control approach with prediction to minimize the effects of time delays while ensuring stability and system performance. Two predictors at the slave and master sides are constructed assuming that the time delays in both transmission channels are measurable. Simulation and experimental results are compared with the scheme without prediction to show the effectiveness of this approach. The influence of data dropout to the proposed teleoperation system is studied in the experiment.

The application of LMI methods to teleoperation with a bounded communication delay is considered. A method to design a state and force feedback controller that guarantees the stability of the system with bounded error related to the rate of change of the operator’s and environment’s exerted force is derived. A numerical example is considered and the means of choosing the design parameters introduced in the derivation are demonstrated. A trade-off between position and force fidelity is outlined, whereby the controller gain could be computed offline and used to adjust the controllers in real-time to suit the current tast. The performance is demonstrated, showing the stability of the system.

In this paper, a new predictive approach is proposed for the impedance control of bilateral drive-by-wire teleoperation systems. The proposed control structure includes two mirror predictors/observers in both the master and slave sides. These predictors/observers are used to simultaneously estimate the master and slave internal dynamics, and thereby to avoid the use of the delayed transmitted information. As a consequence, the influence of the delay on the whole system can be minimized and the performance can be improved. Under a set of suited hypotheses, the proposed control structure is shown to be uniformly ultimate stable, even in the presence of time-varying delays. Simulation results are presented to show the effectiveness of the proposed approach. The behavior of the control structure is also experimentally demonstrated while performing remote steering of a small autonomous vehicle