A new integrated fault detection and control scheme of islanded DC microgrids: A resilient approach.

This paper presents a new resilient integrated fault detection and control module for a DC microgrid operating in islanded mode. The proposed design is unique in its ability to simultaneously improve microgrid reliability and mitigate variations through a multi-objective approach that considers both control and diagnosis objectives in the form of Linear Matrix Inequalities through a presented algorithm. The module is designed by setting out desired performance indices to ensure system stability, mitigate disturbances and uncertainties, and detect faults while controlling the DC-link voltage. Additionally, the module is robust to its own variations and capable of detecting non-compensable faults for further diagnosis. The efficacy of the proposed approach is demonstrated through a comparative study that highlights the vulnerability of robust design to parameter variations.

Current sharing and voltage regulation of parallel DC-DC buck converters: Switching control approach.

In this paper, a new framework for distributed switching control of parallel DC-DC buck converters is investigated, which represents voltage regulation and current sharing as two decoupled control design problems. The problem is described as a cascaded switched affine system with the new variables output voltage, total current, and current difference of the load, and by employing distributed min-projection switching technique, switching control signals are provided to achieve both voltage regulation and current sharing control objectives. Stability analysis based on relay control is carried out to guarantee the asymptotic stability of the error signals. Finally, the performance and effectiveness of the proposed control strategy are demonstrated by simulation studies as well as experiments conducted on a laboratory prototype.

Adaptive super-twisting control for leader-following consensus of second-order multi-agent systems based on time-varying gains.

In this paper, robust distributed consensus control is designed based on adaptive time-varying gains for a class of nonlinear multi-agent systems (MAS) in the presence of uncertain parameters and external disturbances with unknown upper bounds. Due to various conditions and constraints, different dynamical models for the agents can be considered in practice. On the basis of a continuous homogeneous consensus method which has been proposed for the nominal nonlinear MAS, the discontinuous and continuous adaptive integral sliding mode control strategies are particularly designed and extended to accomplish exact and precise consensus for non-identical MASs influenced by imposed perturbations. However, it is noted that in practical problems, the exact upper bound of perturbations is unknown. Then, the proposed controllers have been improved in an adaptive scheme to overcome this drawback. In addition to the adaptive estimation strategy and time-varying gains, which address considered uncertain parameters in the dynamics of the following agents, the designed distributed super-twisting sliding mode strategy for nonlinear agents adjusts the gain of the control inputs and guarantees that the proposed protocol performs properly without any setbacks of the chattering phenomenon. The illustrative simulations depict the robustness, accuracy, and effectiveness of the designed methods.

Fuzzy-SIRD model: Forecasting COVID-19 death tolls considering governments intervention.

Modeling the trend of contagious diseases has particular importance for managing them and reducing the side effects on society. In this regard, researchers have proposed compartmental models for modeling the spread of diseases. However, these models suffer from a lack of adaptability to variations of parameters over time. This paper introduces a new Fuzzy Susceptible-Infectious-Recovered-Deceased (Fuzzy-SIRD) model for covering the weaknesses of the simple compartmental models. Due to the uncertainty in forecasting diseases, the proposed Fuzzy-SIRD model represents the government intervention as an interval type 2 Mamdani fuzzy logic system. Also, since society's response to government intervention is not a static reaction, the proposed model uses a first-order linear system to model its dynamics. In addition, this paper uses the Particle Swarm Optimization (PSO) algorithm for optimally selecting system parameters. The objective function of this optimization problem is the Root Mean Square Error (RMSE) of the system output for the deceased population in a specific time interval. This paper provides many simulations for modeling and predicting the death tolls caused by COVID-19 disease in seven countries and compares the results with the simple SIRD model. Based on the reported results, the proposed Fuzzy-SIRD model can reduce the root mean square error of predictions by more than 80% in the long-term scenarios, compared with the conventional SIRD model. The average reduction of RMSE for the short-term and long-term predictions are 45.83% and 72.56%, respectively. The results also show that the principle goal of the proposed modeling, i.e., creating a semantic relation between the basic reproduction number, government intervention, and society's response to interventions, has been well achieved. As the results approve, the proposed model is a suitable and adaptable alternative for conventional compartmental models.

Adaptive backstepping quantized control for a class of unknown nonlinear systems.

In this paper, the stability problem for a class of nonlinear systems in the form of strict-feedback with applying input quantization has been addressed. By considering a sector-bounded hysteresis quantizer, signal quantization has been achieved. The employed quantizer can reduce the potential chattering which can occur in some approaches. By using a common Lyapunov function (CLF) and the backstepping method, a control scheme has been introduced to stabilize the uncertain nonlinear system. Compared with the recent papers, in order to handle the quantization error, one of the sector-bounding features has been utilized straightly instead of decomposing the quantized input into linear and nonlinear parts, in this case, the possible disturbance-like term has been ignored. The designed control scheme does not need the global Lipschitz assumption over the system mismatched nonlinearities. Besides, the asymptotic stability of system trajectories to the origin is guaranteed and the imposed restrictions over quantization design parameters such as quantization density have been eliminated. Finally, in the simulation results, the accuracy and efficiency of the this control scheme are shown.

Exponential stability of bilateral sampled-data teleoperation systems using multirate approach.

This paper develops the stability analysis for linear bilateral teleoperation systems exposed to communication constraints and multirate samplers. Dynamics of the master and slave robots are assumed to be continuous-time with discrete-time controllers. The proposed multirate design guarantees the exponential stability of the teleoperation system over a communication networks. Different sampling rates are imposed on position/velocity signals in both master and slave sides and by exerting input-delay approach, the multirate dynamics is transformed into a continuous-time system. Sufficient Krasovskii-based stability criteria are provided to preserve the exponential stability of the linear discrete-time system for asynchronously sampling intervals and update rates. This analysis is formulated as a convex optimization problem in terms of linear matrix inequalities (LMI) to calculate the longest interval between two successive sampling periods. The proposed multirate approach demonstrates that selecting adequate intervals for samplers and proper update rates for actuators has a considerable effect on the stability of the system. In order to validate the stability criteria and present the efficacy of the proposed multirate scheme, pair of two degree of freedom manipulators will be investigated and maximum allowable sampling periods and update rates are computed to preserve the exponential stability for different multirate cases.

Interconnection and damping assignment control based on modified actor-critic algorithm with wavelet function approximation.

Passivity-based control (PBC) is model-based, and as a result, it is affected by uncertainties. Besides, for some PBC designs, partial differential equations (PDEs) should be solved in order to obtain the parameters of the controller. Actor-critic (AC) algorithm has been used to regulate PBC parameters instantaneously and solve PDEs online. Despite the benefits of this algorithm, it cannot handle disturbance. The main purpose of this paper is to modify the AC algorithm with an online wavelet function approximation to improve its performance. The proposed method uses PBC and the modified AC algorithm to attenuate the effects of uncertainties. Moreover, the stability analysis is investigated for a nonlinear system. The results show that the new method can handle disturbance and uncertainties more proper than other methods.

Adaptive synchronization of chaotic systems with hysteresis quantizer input.

This paper proposes two new designing methods of adaptive controllers in order to synchronize uncertain nonlinear chaotic systems with input quantization. The hysteresis quantizer, which is a class of sector-bounded quantizers, has been used to quantize the control signal. This can avoid the possible chattering caused by some conventional controllers. Two adaptive robust schemes are proposed to accomplish chaos synchronization of master and slave systems in presence of unknown parameters and uncertainties. The proposed controllers in this paper do not require the restrictive conditions for quantized parameters in contrast to some available control techniques for systems with input quantization. In addition, asymptotic stability of the proposed adaptive controllers is also verified analytically. Finally, the proposed controller is applied to a chaotic gyroscope and also to a Micro-Electro-Mechanical-System to validate its efficiency and robustness.

EEG Artifacts Handling in a Real Practical Brain-Computer Interface Controlled Vehicle.

One of the main issues restricting the practical efficiency of brain-computer interface (BCI) systems is the inevitable occurrence of physiological artifacts during electroencephalography (EEG) recordings. The effects of the artifacts are, however, mostly discarded in practical BCI systems, due to the time-consuming and complicated computational processes. This paper presents the influences of the artifacts and the efficiency of reducing these influences in a practical BCI. Ocular and muscular artifacts are considered due to the high-amplitude and frequent presence. The paradigm is designed based on the mental controlling of a radio-control (RC) car. Two motor imagery commands, containing the imagination of movement of left/right hand, are used to navigate the BCI-based RC car to turn left/right. The results indicate that the artifacts can highly affect the system performance; reducing their influence significantly improves the efficiency.

Synchronization of different fractional order chaotic systems with time-varying parameter and orders.

In this paper, synchronization of time-varying fractional order chaotic systems, is introduced. Parameters of system play an important role in chaotic systems. A time-varying parameter is selected for chaotic systems, also orders of systems are considered as time-varying orders. A reliable controller is designed to synchronize two different fractional order chaotic systems based on Lyapunov stability theory on fractional order systems. Numerical simulations and practical implementation of proposed method are presented to verify the results.

Indirect predictive type-2 fuzzy neural network controller for a class of nonlinear input - delay systems.

In this paper a new indirect type-2 fuzzy neural network predictive (T2FNNP) controller has been proposed for a class of nonlinear systems with input-delay in presence of unknown disturbance and uncertainties. In this method, the predictor has been utilized to estimate the future state variables of the controlled system to compensate for the time-varying delay. The T2FNN is used to estimate some unknown nonlinear functions to construct the controller. By introducing a new adaptive compensator for the predictor and controller, the effects of the external disturbance, estimation errors of the unknown nonlinear functions, and future sate estimation errors have been eliminated. In the proposed method, using an appropriate Lyapunov function, the stability analysis as well as the adaptation laws is carried out for the T2FNN parameters in a way that all the signals in the closed-loop system remain bounded and the tracking error converges to zero asymptotically. Moreover, compared to the related existence predictive controllers, as the number of T2FNN estimators are reduced, the computation time in the online applications decreases. In the proposed method, T2FNN is used due to its ability to effectively model uncertainties, which may exist in the rules and data measured by the sensors. The proposed T2FNNP controller is applied to a nonlinear inverted pendulum and single link robot manipulator systems with input time-varying delay and compared with a type-1 fuzzy sliding predictive (T1FSP) controller. Simulation results indicate the efficiency of the proposed T2FNNP controller.