A novel non-singular terminal sliding mode controller (NTSMC) has been developed for the purpose of tracking and stabilizing tasks in uncertain electro-hydraulic robot manipulators. It is supposed that the controller communicates with the robot through a network that is vulnerable to cyber-attacks. To reduce the communication burden on the network layer and achieve resiliency against cyber-attacks, an event-based strategy is employed. For this purpose, the event-triggering rule is derived so that the Zeno-free behavior is guaranteed. Then, based on the cyber-attack characteristics, i.e., frequency and duration of the attacks, the resilient behavior of the proposed scheme in the presence of denial of service attacks, unmodelled dynamics, and external disturbance are analyzed. Moreover, to capture the nonlinear nature of the robot an experimentally validated analytical model of an uncertain 7-DoF manipulator with a hydraulic model of the joints and actuators, namely, Brokk-Hydrolek, is employed. Finally, the merits of the proposed methodology in terms of resiliency, robustness, and preservation of the communication resources are validated, and the results are compared to the state-of-the-art approaches based on the$\rho$index criterionNote to Practitioners—The aim of this study is to address the problem of network-based control of robotic manipulators. These systems, relying on the network layer for data collection and control commands, are highly vulnerable to catastrophic cyber-attacks. Furthermore, they should comply with network restrictions, such as limited bandwidth, to achieve the desired performance. Therefore, due to the collaborative behavior of robot manipulators in industries, it is vital for engineers and practitioners to be assured of achieving desired performance in the presence of these threats and limitations. As a first step to deal with these issues, a 7-DoF robotic manipulator model is mathematically formulated and experimentally validated. Then, a controller design procedure that guarantees the desired performance in spite of model uncertainties, denial-of-service cyber-attacks, and network restrictions is derived. Additionally, a clear relation between cyber-attack characteristics and designed parameters is defined while resilient behavior is maintained. Note that the proposed approach can be applied to a wide range of network-based nonlinear dynamic systems.

This article studies the resilient finite-time consensus tracking problem for high-order nonholonomic chained-form systems against denial-of-service (DoS) attacks. The first step is to develop a novel secure distributed observer for each follower in which the tangent hyperbolic function is used to accelerate the convergence speed of the observer by inducing a high-gain effect. The paralyzed-connectivity graphs resulting from DoS attacks are repaired to the initially connected graphs by integrating both acknowledgment-based attack detection techniques and the communication recovery process. In addition, it is demonstrated that the duration of DoS attacks directly affects the convergence time of the proposed scheme. Then, a fast finite-time backstepping control (FFTBC) algorithm is established for each follower to track the estimated leader's information, ensuring fast convergence performance regardless of whether the follower states are near or far from the equilibrium point. An approximation-based approach is also presented for reducing the conservatism of the upper estimate of the settling time. An evaluation of the proposed control algorithm under DoS attacks is conducted using a group of wheeled mobile robots.

This article investigates the finite-time leader–follower tracking problem for consensus control of nonholonomic MASs with chained-form dynamics under unknown time-varying disturbances. First, a finite-time distributed observer, consisting of the tangent hyperbolic function with an induced high gain effect, is constructed to estimate the leader’s information both quickly and accurately for each follower leading to communication loop avoidance. Besides, to cope with the adverse impact resulting from external disturbances, a finite-time disturbance observer is developed to provide accurate estimations of unknown terms within a finite time. A finite-time backstepping control scheme with a fast convergence rate is then proposed for each follower based on estimated disturbances to track the estimated states of the leader. Regardless of how close or how far away from the equilibrium point the follower states are, this method accelerates the convergence rate. An approximation technique using piecewise functions is also employed to bring the upper estimate of the convergence time closer to its real value. Finally, the efficiency of the presented control protocol is verified by some simulations on a number of connected wheeled mobile robots under external disturbances.

Deep brain stimulation (DBS) is an effective and promising therapy to control Parkinson’s tremor movement in patients with advanced Parkinson’s disease (PD). This paper proposes a new alternative medication that has several advantages, including compatibility with individual needs and low side effects. There has been a rapid improvement in the literature on the development of the dynamic computational model of neuroscience, alongside the development of DBS. A combination of DBS and model-based control strategies opens up a new vision for Parkinson’s disease treatment. Despite the numerous studies on basal ganglia (BG) modeling, researchers are required to employ adaptive and robust strategies to eliminate Parkinson’s patients’ tremors. This paper proposes a new adaptive optimal fast terminal sliding mode control (AOFTSMC) method to mitigate tremors by tuning GABA thorough DBS. This approach represents finite-time convergence law, a new method to stimulate the inner nuclei of BG in a robust and optimum manner that leads to removing tremors of PD fluctuation signal in the presence of uncertainties. Finally, simulation results of the basal ganglia model under the addressed approach are adopted to demonstrate the effectiveness of the proposed method.

In this article, a new approach is proposed for stability analysis and controller design of nonlinear discrete-time positive systems by means of the Takagi–Sugeno fuzzy model. The closed-loop stability and the positivity constraint are guaranteed by synthesizing a linear co-positive Lyapunov function and by applying the parallel distributed compensation controller. In contrast to the state-of-the-art approaches for ensuring the$\ell _{1}$-stability of the positive system which are based on bilinear matrix inequalities, the proposed optimal robust control design under$\ell _{1}$-induced performance is derived based on linear programming framework. It has been shown that the computational complexity of the proposed optimization problem can be effectively reduced. Finally, a numerical example and the Leslie population model are adopted to show the capabilities of the proposed method.

In this study, first, a comprehensive model is introduced to model actuator faults, and then a novel fault-tolerant control (FTC) strategy is proposed to compensate the loss of actuator’s effectiveness in networked control systems (NCSs). A Markov chain is exploited to represent networked-induced random delays, and data packet dropouts as well as disorders to address the stochastic characteristic of the network issues. Accordingly, the resulting closed-loop system lies in the framework of Markovian jump systems (MJSs). Moreover, partly unknown transition probabilities are considered in the current study since the identification of the exact value of transition probabilities of the Markov chain is difficult or even impractical due to the complex structure of the network. Sufficient conditions for the stochastic stability are derived by means of the solutions of a finite set of linear matrix inequalities (LMIs) to design a novel robust FTC through the output feedback technique, which requires only the outputs. A numerical example and an engineering benchmark system are presented to verify the capability of the proposed method in practical applications.

In this study, a constrained nonlinear model predictive control (NMPC) is presented for the purpose of voltage and current stabilization of a DC microgrid that supplies constant power loads. The proposed technique can handle all constraints of the stand-alone DC microgrid in different configurations. Furthermore, in order to reduce the complexity of the NMPC, a novel approach, which is called fast NMPC is proposed, and it is shown that it has superior performance than NMPC.

This paper deals with the problem of robust H∞fault-tolerant controller design for fuzzy networked control systems using static state feedback. The stability of networked control systems is affected by delay, sensor failure and data drift. Therefore, a Lyapunov–Krasovskii functional are exploited to establish asymptotic stability conditions for the underlying system considering these imperfections. It is assumed that sensor failure and data drift, which occur during data transmission over the network, are modeled by a stochastic variable with Bernoulli distribution. The design conditions are presented in terms of linear matrix inequalities, and the efficiency of the proposed approach is shown through a numerical example.

This paper is devoted to studying the effect of false data injection attacks on the state estimation of discrete linear time-invariant systems in the presence of unknown disturbance. The proposed scheme firstly decouples the disturbance signal from the estimation error by exploiting the concepts of unknown input observers. Then, the observer gain has been designed based on the Kalman filter algorithm while a saturation term has been assigned to the output error in the update rule of the estimated states. Thanks to the saturation-limit dynamics introduced into the error dynamics of the Kalman filter-based estimation, the proposed method is applicable for the real-time applications. The effectiveness of the proposed scheme has been validated through a numerical example by taking two different scenarios into considerations. First, the comparative results show the superiority of the proposed scheme in state estimation under the presence of high-frequency measurement noise. Next, further to the high-frequency measurement noise, it is assumed that the sensed measurements are also manipulated by an adversary, leading to outliers in the measurements. As for this scenario, the attained results show how successfully the proposed scheme can mitigate the effect of the outliers in the presence of unknown disturbances.

This paper presents a new approach for broken rotor bars detection in induction motors. When broken rotor bar occurs, the rotor resistance will increase. Furthermore, thermal effects of rotor resistance are considered to address practical aspects. Therefore, a suitable method to detect broken rotor bar is rotor resistance estimation. An applicable method in state estimation is Cubature Kalman Filter (CKF). To display the advantages of the CKF, simulation results are compared with Unscented Kalman Filter (UKF). In addition to not needing setting parameters, this filter is more accurate in rotor resistance estimation compared to the UKF.

This work deals with the problem of filter design for disturbance decoupling in discrete-time linear fractional order systems (FOS) under noisy environments. To this end, Fractional Unknown Input Filter (FUIF) is developed based on Fractional Kalman Filter (FKF) framework. Accordingly, the proposed structure can result in robustness against unknown inputs (UIs) in noisy environments. This algorithm can be used for robust fault detection since the disturbance is decoupled from state estimation error. The designed filter is applied to a fractional order (FO) model of an ultra-capacitor (UC), under noisy condition, for the state estimation and fault detection purposes. Simulation results illustrate the benefits of the proposed approach.

This paper investigates the problem of network-based fault-tolerant controller design for networked control systems (NCSs) in the presence of random delays and data packet dropouts. A novel actuator fault model which is more general and practical than the conventional actuator fault models is developed. Considering this new fault model, the NCSs are firstly modeled as a Markovian jump system (MJS) with partly unknown transition probabilities (TPs), upon which sufficient conditions based on linear matrix inequalities (LMIs) are then developed to design the output feedback fault-tolerant controller to ensure the stochastic stability of the NCS. Finally, simulation results are provided to illustrate the effectiveness and superiority of the proposed method compared to the existing approaches in the literatures.

This paper deals with the problem of robust unknown input fault detection observers (UIFDOs) design for interval type-2 Takagi-Sugeno (T-S) fuzzy systems with immeasurable premise variables. It has been shown that choosing the system states, which may be immeasurable, as premise variables, will able us to model a larger class of nonlinear systems. Accordingly, the premise variables of underlying system are considered to be immeasurable. However, the design procedure of a stable observer for such systems are more challenging. Furthermore, the system is supposed be affected by time-varying delays and unknown inputs. The UIFDO is exploited so as to generate a residual signal with the most possible sensitivity to fault and the least sensitivity to exogenous signals. In this paper, this issue is investigated thoroughly, in this respect the design procedure consists of two sections: 1) measurable and 2) immeasurable premise variables. Sufficient design conditions are provided in terms of linear matrix inequalities for both cases. The effectiveness of the proposed UIFDO in detection of two different kinds of faults is illustrated during the simulation of a numerical example. Moreover, a fair comparison has been drawn between the proposed UIFDO and an existent reference to indicate that interval type-2 T-S fuzzy model is more superior than type-1. Finally, the faulty behavior of a one-link manipulator is investigated to express the applicability of the proposed method.

In this paper, a supervised fuzzy-neighborhood density-based clustering approach is proposed for the fault diagnosis of induction motors' bearings. The proposed approach makes use of the labeled data regarding the actual classes of faulty and fault-free cases, in order to train the fuzzy-neighborhood density-based clustering algorithm in a supervised manner, by resorting to an invasive weed optimization algorithm that aims to minimize an error-based objective function. The proposed classifier can properly classify multi-class data with complex and variously shaped decision boundaries among the different classes of faults and the fault-free state, and is robust against noise. This is due mainly to the fact that the classifier is constructed using the fuzzy-neighborhood density based clustering method, which is not sensitive to the geometrical shape of clusters in the feature space.