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In this article, we address the distributed event-triggered leader–follower formation tracking control problem of general linear multiagent systems with a dynamic leader in sampled-data settings. A novel locally computable state-estimate-based event generator is established for each follower agent to regulate the interagent communication at each sampling instant. Then, we propose a distributed formation tracking protocol based on the triggered sampled information such that 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 using the feasible linear matrix inequality conditions that are derived from Lyapunov-based stability-analysis methods that guarantee the ultimate boundedness of the closed-loop formation error dynamics. Finally, numerical simulations along with experiment implementations were conducted for a group of linearized unicycle-type mobile robots to demonstrate the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

A distributed control policy is designed for a group of Euler–Lagrange (EL) agents in a leader–follower-based communication network with time-varying delays. The nonsingular terminal sliding mode control (NTSMC) policy is integrated with mixed-type feedback and time-varying, adaptive control parameters. The control gain and proportions of feedback with and without estimated self-delays are tuned online with fuzzy logic control (FLC). The total and maximum tracking errors of a group of EL agents are assessed to demonstrate an improvement in synchronization performance with the proposed NTSMC+FLC approach compared to the NTSMC approach with constant parameters. Simulation and experimental results of a group of Phantom Omni manipulators are presented to validate the proposed control policy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article presents a novel model-based control design scheme for a commercially-available micro quadcopter. A simple yet effective attitude dynamics model was identified using the closed-loop instrumental variable method with an iterative minimization search algorithm, since the actual model of the onboard attitude controller was unknown. Model-based integral-linear quadratic regulator, backstepping, and nested saturation controllers were proposed and compared for hovering control of the micro quadcopter based on the identified attitude dynamics model and its theoretical translational model. The proposed hovering controllers were first evaluated in simulations and then validated experimentally. For the conditions used in this research, it was observed that the nested saturation controller generally had the best performance among the three proposed controllers when hovering in the presence of external wind disturbances, while the backstepping controller exhibited the worst performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article, we focus on the experimental validation of the developed adaptive nonsingular terminal sliding mode (ANTSM) controller for a networked manipulator system. The proposed control approach is designed to deal with a combination of adverse and inexactly known conditions in practice, including parametric uncertainties, frictions, exogenous disturbances, and random time-varying network delays. Another issue addressed in this article is the tradeoff between achieving a smooth convergence and a high tracking accuracy in the physical implementation of the ANTSM controller. Hence, a novel time-varying gain in a form of logistic function is introduced to provide converging smoothness and meanwhile improve the tracking accuracy. Experimental results and empirical analysis are provided to demonstrate the effectiveness and performance improvement of the proposed controller with a time-varying gain. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article, an adaptive nonsingular terminal sliding-mode (ANTSM) method is proposed for the motion tracking control of a bilateral teleoperation system. Efforts in this article seek to improve the position tracking performance of nonlinear systems subject to time-varying network delays, parametric uncertainties, and unknown external disturbances and frictions. Another issue addressed in this article is the common delay-induced phase shift of tracking profiles in many control methods, which is greatly reduced by introducing a novel mixed type of feedback signals in the ANTSM control design. Furthermore, the proposed adaptive control design with two online-estimated compensatory bounds removes the requirement of exact knowledge of network delays and disturbance bounds as a prior. In the master side, a force predictor is used to estimate the current environmental force for the reference signal generator. Therefore, the direct transmission of force signals is avoided. By comparing with the existing model-based and model-free methods, numerical simulation results with six-degree-of-freedom manipulators illustrate the merits of the developed robust and adaptive controllers. Experimental results with two Phantom Omni devices are also provided to demonstrate the effectiveness and the significant performance improvements of the proposed controllers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The realism or transparency of haptic interfaces is becoming more critical as they are applied to training in fields like minimally invasive surgery (MIS). Surgical training simulators must provide a transparent virtual environment (VE) at a high update rate. Complex, deformable, cuttable tissue models have nonlinear dynamics and are computationally expensive, making it difficult to provide sufficient update rates. The objective of this work is to improve the transparency for this type of VE by formulating the unknown nonlinear dynamics as a quasi-linear parameter varying (LPV) system and designing a predictor to provide an output at a much higher update rate. An adaptive controller based on gain-scheduled prediction is considered for a nonlinear haptic device and a nonlinear, delayed, and sampled VE. The predictor uses feedback from the more accurate but slow-updating VE to update a simplified dynamic model. The predictor is designed based on numerical solutions to a linear matrix inequality derived using Lyapunov-based methods. Experimental results demonstrate the effectiveness of the gain-scheduled predictor approach and compare it to previous work using a constant-gain predictor. The gain-scheduled predictor results in significant performance improvements compared to a haptic system without prediction, but less significant improvement compared to the constant-gain approach. [ABSTRACT FROM AUTHOR]