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In this paper, a new modified fuzzy Two-Level Control Scheme (TLCS) is proposed to control a non-inverting buck-boost converter. Each level of fuzzy TLCS consists of a tuned fuzzy PI controller. In addition, a Takagi–Sugeno–Kang (TSK) fuzzy switch proposed to transfer the fuzzy PI controllers to each other in the control system. The major difficulty in designing fuzzy TLCS which degrades its performance is emerging unwanted drastic oscillations in the converter output voltage during replacing the controllers. Thereby, the fuzzy PI controllers in each level of TLCS structure are modified to eliminate these oscillations and improve the system performance. Some simulations and digital signal processor based experiments are conducted on a non-inverting buck-boost converter to support the effectiveness of the proposed TLCS in controlling the converter output voltage. [ABSTRACT FROM AUTHOR]

Urban traffic network model is illustrated by state-charts and object-diagram. However, they have limitations to show the behavioral perspective of the Traffic Information flow. Consequently, a state space model is used to calculate the half-value waiting time of vehicles. In this study, a combination of the general type-2 fuzzy logic sets and the Modified Backtracking Search Algorithm (MBSA) techniques are used in order to control the traffic signal scheduling and phase succession so as to guarantee a smooth flow of traffic with the least wait times and average queue length. The parameters of input and output membership functions are optimized simultaneously by the novel heuristic algorithm MBSA. A comparison is made between the achieved results with those of optimal and conventional type-1 fuzzy logic controllers. [ABSTRACT FROM AUTHOR]

Static synchronous compensator (STATCOM) provides the means to improve quality and reliability of a power system as it has the functional capability to handle dynamic disturbances, such as transient stability and power oscillation damping as well as to providing voltage regulation. In this paper, a robust adaptive PI-based optimal fuzzy control strategy is proposed to control a STATCOM used in distribution systems. The proposed intelligent strategy is based on a combination of a new General Type-II Fuzzy Logic (GT2FL) with a simple heuristic algorithm named Teaching Learning Based Optimization (TLBO) Algorithm. The proposed framework optimally tunes parameters of a Proportional-Integral (PI) controller which, similar to most of other researchers regarding control of STATCOM, are in charge of controlling the device. The proposed controller guaranties robustness and stability against uncertainties caused by external disturbances or ever-changing nature of the power systems. The TLBO optimizes the parameters of the controller as well as the input and output membership functions. To validate the efficiency of the proposed controller, the obtained simulation results are compared with those of the two most recent researches applied in this field, namely, conventional Proportional Integral (PI) controller and Optimal Fuzzy PI (OFPI) controller. Results demonstrate the successfulness and effectiveness of the proposed online-TLBO General Type-2 Fuzzy PI (OGT2FPI) controller and its superiority over conventional approaches. [ABSTRACT FROM AUTHOR]

[Display omitted] • In response to the aforementioned issues, an intelligent control strategy has been established for reducing hand tremors and rigidity in Parkinson's patients, respectively. • An ULM controller was developed in a model-free framework to mitigate simultaneously tremor and rigidity by stimulating the basal ganglia system in the current study. In the ULM controller, an SMO is used to estimate the ULM's poorly understood dynamics. By adopting the input and output of the BG system, the ULM controller was designed without the need for the model identification of the basal ganglia system. • The learning capability of DDPG is used to reduce tremor and rigidity by interacting the agent with the BG model. The Actor and Critic of the learning control are trained in a model-free way by introducing a reward function as the optimizing goal. • The hardware-in-the-loop (HiL) simulations under various scenarios of the dynamic system were accomplished for real-time analysis of the designed controllers. Parkinson's disease (PD) is one of the really frequent disorders, with hand and head tremors and rigidity being the most common sequelae. Deep brain stimulation (DBS) is a common treatment used to alleviate the symptoms of this disease. This work investigates an ultra-local model (ULM) based on a sliding mode observer (SMO) to simultaneously reduce hand tremor and rigidity. specifically, a deep deterministic policy gradient (DDPG) controller is adaptively designed in the current study to reduce observer estimation error and improve the nonlinear dynamic features of a central neural network (CNN). The DDPG is designed with an actor that produces policy demands and a critic that measures the effectiveness of the actor's policy orders. The offered methodology employs a DDPG-based mechanism to compensate for the shortcomings of the ULM-based SMO. In the present mechanism, training of the weight values of both networks (actor and critic) is by the gradient descent way that relies on the tremor fault's reward return. Finally, the following methodology is analyses by computer simulation in a variety of contexts (robustness and controller performance) and compared to current practices to prove the benefits and adaptability of the procedure with varied models and patients. Additionally, the controllers are implemented in the hardware-in-the-loop (HiL) simulations testbed to validate the performance of the developed scheme's profitability from a realistic perspective. [ABSTRACT FROM AUTHOR]

The goal of this study is to introduce a novel robust load frequency control (LFC) strategy for micro-grid(s) (MG(s)) in islanded mode operation. Admittedly, power generators in MG(s) cannot supply steady electric power output and sometimes cause unbalance between supply and demand. Battery energy storage system (BESS) is one of the effective solutions to these problems. Due to the high cost of the BESS, a new idea of Vehicle-to-Grid (V2G) is that a battery of Electric-Vehicle (EV) can be applied as a tantamount large-scale BESS in MG(s). As a result, a new robust control strategy for an islanded micro-grid (MG) is introduced that can consider electric vehicles׳ (EV(s)) effect. Moreover, in this paper, a new combination of the General Type II Fuzzy Logic Sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) technique is applied for adaptive tuning of proportional-integral (PI) controller. Implementing General Type II Fuzzy Systems is computationally expensive. However, using a recently introduced α -plane representation, GT2FLS can be seen as a composition of several Interval Type II Fuzzy Logic Systems (IT2FLS) with a corresponding level of α for each. Real-data from an offshore wind farm in Sweden and solar radiation data in Aberdeen (United Kingdom) was used in order to examine the performance of the proposed novel controller. A comparison is made between the achieved results of Optimal Fuzzy-PI (OFPI) controller and those of Optimal Interval Type II Fuzzy-PI (IT2FPI) controller, which are of most recent advances in the area at hand. The Simulation results prove the successfulness and effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nonlinear model predictive controller (MPC) in terms of linear matrix inequalities (LMIs). The proposed MPC is based on Takagi–Sugeno (TS) fuzzy model, a non-parallel distributed compensation (non-PDC) fuzzy controller and a non-quadratic Lyapunov function (NQLF). Utilizing the non-PDC controller together with the Lyapunov theorem guarantees the stabilization issue of this MPC. In this approach, at each sampling time a quadratic cost function with an infinite prediction and control horizon is minimized such that constraints on the control input Euclidean norm are satisfied. To show the merits of the proposed approach, a nonlinear electric vehicle (EV) system with parameter uncertainty is considered as a case study. Indeed, the main goal of this study is to force the speed of EV to track a desired value. The experimental data, a new European driving cycle (NEDC), is used in order to examine the performance of the proposed controller. First, the equivalent TS model of the original nonlinear system is derived. After that, in order to evaluate the proficiency of the proposed controller, the achieved results of the proposed approach are compared with those of the conventional MPC controller and the optimal Fuzzy PI controller (OFPI), which are the latest research on the problem in hand. [ABSTRACT FROM AUTHOR]

This paper proposes a new method of fault detection and classification in asymmetrical distribution systems with dispersed generation to detect islanding and perform protective action based on applying a combination of wavelet singular entropy and fuzzy logic. In this method, positive components of currents at common coupling points are decomposed to adjust detailed coefficients of wavelet transforms and singular value matrices, and expected entropy values are calculated via stochastic process. Indexes are defined based on the wavelet singular entropy in positive components and three phase currents to detect and classify the fault. This protection scheme is put forward for fault detection and is investigated in different types of faults such as single-phase to ground, double-phase to ground, three-phase to ground and line to line in distribution lines in the presence of distributed generations, and different locations of faults are verified when the distributed generation is connected to the utility. The major priority of the proposed protection scheme is its reduction in time (10 ms from the event inception) in distinguishing islanding and protection transmission lines in the presence of distributed generations. [ABSTRACT FROM AUTHOR]

Developing controller for uncertain nonlinear systems in the presence of disturbances is an important and still challenging problem. Adaptive control method asserts to adapt system parameters against uncertainties, if only uncertainties change sufficiently slowly. Alternatively, if uncertainties stay in known bounds, robust control approaches claim to ensure system stability. In this paper, a Proportional–Integral (PI) indirect adaptive Least Squares Support Vectors Regression (LS-SVR) control scheme for a class of uncertain nonlinear system in the presence of large and fast disturbances is proposed. The LS-SVR is used to approximate the nonlinear uncertainty which must be bounded, whereas in comparison to robust control methodologies no requirement needs for bounds to be known. The asymptotic stability of the control scheme is proved by using Lyapunov synthesis. The simulation study is performed on a second-order inverted pendulum system in the presence of fast against slow and large against small disturbances to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Micro-grids consist of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. Controlling these systems is more difficult than ordinary form of power systems since, in most of them, their energy is provided by renewable energies which have uncertain and varying nature. These fluctuations in the generated power might cause some problems in the function of conventional controllers. As a result, modern power systems require increased intelligence and flexibility in the control and optimization to ensure the capability of maintaining a generation-load balance, following serious disturbances. In this issue, the emotional controller which has a self-tuning nature is used to overcome these difficulties. This controller is based on emotional learning process of the human brain and can provide an appropriate control against changes in the system structure and occurrence of uncertainties. To evaluate the performance of the proposed controller, the results are compared with those obtained by conventional PI and fuzzy controllers, which is the latest research in the problem in hand. Simulation results show the effectiveness of the emotional controller. [ABSTRACT FROM AUTHOR]

The primary aim of the Automatic Generation Control (AGC) is to maintain system frequency and tie-line interchanges in a predestine limits by regulating the power generation of electrical generators, in case of fluctuations in the system frequency and tie-line loadings. This paper proposes a new online intelligent strategy to realize the control of multi-area load frequency systems. The proposed intelligent strategy is based on a combination of a novel heuristic algorithm named Self-Adaptive Modified Bat Algorithm (SAMBA) and the Fuzzy Logic (FL) which is used to optimally tune parameters of Proportional–Integral (PI) controllers which are the most popular methods in this context. The proposed controller guaranties stability and robustness against uncertainties caused by external disturbances and impermanent dynamics that power systems face. To achieve an optimal performance, the SAMBA simultaneously optimizes the parameters of the proposed controller as well as the input and output membership functions. The control design methodology is applied on four-area interconnected power system, which represents a large-scale power system. To evaluate the efficiency of the proposed controller, the obtained results are compared with those of Proportional Integral Derivative (PID) controller and Optimal Fuzzy PID (OFPID) controller, which are the most recent researches applied to the present problem. Simulation results demonstrate the successfulness and effectiveness of the Online-SAMBA Fuzzy PI (MBFPI) controller and its superiority over conventional approaches. [ABSTRACT FROM AUTHOR]

DVR is one of the custom power devices for compensating power quality indices. A self-tuning controller with a bi-objective structure is presented for controlling the DVR compensator in order to improve the THD and voltage sag indices of a sensitive load in the network. In this paper, the emotional controller which is based on emotional learning of human brain is proposed for controlling the DVR compensator. This controller has such a structure that makes it capable of considering a second objective in the control process of the system. So far, this capability of the emotional controller has not been used in any researches. The results of the paper demonstrate that compensating and controlling the voltage THD signal in the control process has caused more improvement in the voltage sag of the sensitive load. It was reported that the performance of the emotional controller depends on the selection of the values of its coefficients. Therefore, in order to better improve the proposed controller, these coefficients are tuned by an optimization algorithm. Teaching–learning-based optimization algorithm is considered as optimization algorithm to regulate these coefficients. According to simulation results, it works significantly better than classic PI controller and some intelligent controllers that have introduced in other researches already. [ABSTRACT FROM AUTHOR]

In this study, a novel ultra-local model (ULM) control structure using multi-agent system fuzzy Q learning (MAS-FQL) is proposed for the air-feed system of a polymer electrolyte membrane fuel cell (PEMFC). The primary aim of the control goal is to optimize the net power output of the fuel cell while also preventing oxygen starvation. This is achieved by effectively managing the oxygen excess ratio to maintain it at its optimal value, particularly during rapid load fluctuations. In this study, a new advanced control structure for PEMFCs is first presented to effectively manage the oxygen excess rate in the PEMFC system. This work uses an ULM technique in conjunction with an extended state observer (ESO) to effectively manage the control-related concerns connected with the PEMFC. Furthermore, the inclusion of the MAS-FQL has been used to dynamically manage the gains of the ULM controller in an online adaptive manner. The analysis findings demonstrate that the controller exhibits robustness and has satisfactory performance when subjected to load fluctuations. Across all scenario assessments, the proposed controller consistently exhibits an improvement in oxygen excess ratio regulation of more than 31.32% compared to the proportional integral derivative (PID) controller, more than 17.51% compared to the model-free sliding mode control (SMC) controller, and more than 11.40% compared to the fuzzy PID controller across different performance criteria. • A novel control strategy is proposed for the PEMFC air-feed system control. • The MAS-FQL is employed to dynamically regulate the gains of the ULM controller. • An ESO is included in PEMFC's ULM design. [ABSTRACT FROM AUTHOR]

The Isolated Hybrid Distributed Generation (IHDG) studied in this paper is consisted of a wind turbine generator and a diesel engine generator. The equivalent inertia of power grid reduces by increasing influence of variable speed wind turbines in power systems. Consequently, when a disturbance occurs in the power system the frequency fluctuations increases. To overcome this problem, a supplementary control loop is added to the converter of the variable speed wind turbine in order to share the inertia of the turbines in the power grid. But the appropriate rate of this contribution depends on the amount of load and must be suitably changed based on the load. In this paper, a Takagi–Sugeno (T–S) fuzzy system is designed to determine the contribution coefficient of variable speed wind turbine in such a way that variable wind turbine shares the maximum value of its inertia to compensate the reduced production in the power grid. However, the turbine does not pass its minimum speed limit while sharing the maximum value of inertia in the grid and prevents the cause of another disturbance in the power grid. In the proposed method, first, by using Particle Swarm Optimization (PSO) algorithm, the optimal values of contribution coefficient of wind turbines are attained proportional to the load in such a way that the minimum speed constraint is not violated. In the next stage, the initial T–S fuzzy system is extracted from the obtanied the optimal values of contribution by using subtractive clustering algorithm. In addition, Recursive Least Square (RLS) algorithm is used to adjust the consequent part of the T–S system. The efficiency of the proposed method is demonstrated through the simulation for different amount of load. [ABSTRACT FROM AUTHOR]

The design and implementation of a new control system for reactive power compensation and mechanical torque, voltage regulation and transient stability enhancement for wind turbines equipped with fixed-speed induction generators (IGs) in power systems is presented in this study. The designed optimal linear quadratic regulator (LQR) controller provides an acceptable post fault performance for both small and large perturbations. Large disturbance simulations demonstrate that the designed controller enhances voltage stability as well as transient stability of the system during low-voltage ride-through transients and thus enhances the LVRT capability of fixed-speed wind generators. Further verifications based on detailed time-domain simulations are also provided. Calculations, simulations and measurements confirm how the increased STATCOM rating can provide an increased transient stability margin and consequently enhanced LVRT capability. A concept of critical clearing time has been introduced and its utility has been highlighted. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-objective evolutionary algorithm method for Distribution feeder reconfiguration (DFR) with distributed generators (DG) in a practical system. Considering the low inertia constant of DG units in order to take the transient stability of DGs into account is one of the major issues in power systems. Especially when the penetration of DGs is low, the impacts of them on the distribution system transient stability may be neglected. However, when the penetration of DG increases, the transient stability of them must be taken into account (more DGs, more transient issues). To this end, the DFR problem has been solve by an enhanced Gravitational Search Algorithm (EGSA) to improve the transient stability index and decrease losses and operation cost in a distribution test system with multiple micro-turbines. The effectiveness of the proposed approach is studied based on a typical 33-bus test system. For getting close to the practical condition and considering the detailed dynamic models of the generators and other electric devices in power system, simulation and programming of this approach are done by the DIgSILENT® Power Factory software. [ABSTRACT FROM AUTHOR]

In this paper, at the aim of the fast trajectory-following control of the Unmanned Aerial Vehicle (UAV) systems subject to uncertainty and disturbance, the adaptive prescribed performance control based recursive nonsingular terminal sliding mode control is suggested. Afterward, for the fast trajectory-following control of the uncertain and perturbed quad-rotor system, a recursive nonsingular terminal sliding mode strategy is recommended. Since the better transient and steady-state response of the sliding mode surface is of the utmost importance in the design of the controller, the prescribed performance control scheme is proposed in which the transformed prescribed form of the previously proposed recursive nonsingular terminal sliding surface is obtained to prove that the recursive nonsingular terminal sliding surface can converge to a preset region. Whereas unknown bounded uncertainty and disturbance always exist in the UAV system's dynamical model which decreases its performance and efficiency, the adaptive mechanism is applied to approximate uncertain bounds of uncertainty and disturbance to advance the implementation of the UAV system. Later, quick reachability of the prescribed form switching surface is demonstrated using Lyapunov concept. Ultimately, simulation results using MATLAB/Simulink over a quad-rotor system and real-time simulation using the Speedgoat Real-Time Target Machine platform are conducted to validate the feasibility and performance of the proposed method. [ABSTRACT FROM AUTHOR]

In light of the reliable operation of renewable shipboard microgrids, this paper implements a fractional-order theory for flexible modeling for an accurate study of shipboard microgrids. Then, it introduces a novel variable structure control strategy based on a fractional-order reduced chatter nonlinear sliding mode. This strategy incorporates a time delay estimation and an optimization routine for parameter adjustments, aiming to achieve nearly time-optimal control performance and faster frequency accommodation. The proposed method exploits the benefits of fractional order modeling, reduced chatter sliding mode, and variable structure approaches. While the adapted reduced chatter strategy may decrease convergence speed, the variable structure strategy compensates for this by accelerating settling speed. These features, along with continuous, soft, and limited control signals, make the proposed method suitable for the challenging conditions of low inertia, high dynamics, uncertainty, and a noisy environment in nonlinear renewable shipboard microgrids. The Lyapunov stability proof and simulation results demonstrate the proposed method's fast, high accuracy, and reliable performance. Furthermore, the validation process, which involves comparisons with conventional sliding-mode strategies, fractional-order proportional-integral-derivative controllers, and experimental test results using the Speedgoat real-time target machine in conjunction with Simulink real-time, serves to further establish the superiority of the proposed approach. • Introduces a novel approach using fractional-order theory for a unique variable structure control strategy tailored for shipboard microgrids. • Presents a cutting-edge control strategy based on fractional-order chatter-free nonlinear sliding mode for nearly time-optimal control performance. • Exploits the advantages of fractional order modeling, chatter-free sliding mode, and variable structure approaches to tackle challenges faced by renewable shipboard microgrids. • Mitigates the convergence speed reduction caused by chatter free solution using continuous, soft variable structure, and limited control signals. [ABSTRACT FROM AUTHOR]

In recent years, fractional order Laser chaotic systems have gained popularity in both theory and applications, and various classes of these systems have been introduced. This paper presents a dynamic-free sliding mode control (SMC) methodology to synchronize a class of unknown fractional order (FO) Laser chaotic systems with input saturation. The proposed method uses a defined continuous function instead of the discrete sign function and is based on the FO version of the Lyapunov stability theorem. The result is a novel finite-time SMC (FTSMC) methodology that effectively suppresses chaotic behavior in FO Laser chaotic systems without undesirable chattering. This approach is designed to take advantage of the boundedness feature of the FO chaotic system. The efficacy of the FTSMC is demonstrated by applying the method to a chaotic FO Laser system at two different non-integer orders, and its practical applicability is demonstrated by using it to encrypt/decrypt color pictures. The suggested encryption/decryption methodology uses an adaptation of the pre-diffusion permutation-diffusion structure to increase security. Performance and security analyses, including histogram analysis, neighboring pixel correlation analysis, and information entropy analysis, provide further support for the suggested encryption system's superiority. [ABSTRACT FROM AUTHOR]

One of the main issues in engineering is the identification of nonlinear systems. Because of the complicated as well as unexpected behaviours of these chaotic systems, it is introduced as special nonlinear systems. A minute change in the primary conditions of such systems would lead to significant variations in their behaviours. On the other hand, due to simple structure of Permanent Magnet Synchronous Motors (PMSM) and its high applications in industry, the use of this machine is dramatically increasing these days. The reflection of a chaotic behaviour as the Permanent Magnet Synchronous Motor is positioned in a particular area. In the model of PMSM, the exact parameters of the system are required to properly control and spot the error. In this paper, Self-Adaptive Learning Bat-inspired Optimization algorithm is used for solving both offline and online parameter estimation problems for this chaotic system. In addition, noise is considered as one of influential factors in control of PMSM. According to simulation results, it can be claimed that the proposed algorithm is a very powerful algorithm for online parameter identification for PMSM. [ABSTRACT FROM AUTHOR]

Highlights: [•] In this method the PV output power has a ripple of a lower frequency. [•] The drop which occurs when MPPT system starts to operate is minimized. [•] The ESC approach for MPPT in this paper uses a series combination of LPF and HPF. [•] To carry out MPPT in PV panels, under PSCs a method based on ESC is introduced. [•] Under PSC, system can eliminate local MPP to make PV array running at global MPP. [Copyright &y& Elsevier]

• Feasibility assessment of a laser-based WPT method for drones is investigated. • Next-generation drones with unlimited operating time is evaluated. • A two-phase cooling system is proposed to enhance WPT mechanism. • Performance of the system is studied with some commercial PV materials. Utilizing drones in many applications such as video and photography, farms, search and rescue operations, construction industry for mapping and site monitoring has increased. However, their operating time is limited according to the capacity of their batteries. Therefore, many researchers attempt to enhance the operating time of drones. In this study, to the best of the authors' knowledge for the first time, the feasibility assessment of a laser-based wireless power transfer (WPT) mechanism is theoretically investigated on drone applications to increase their operating time. In this method, drones can be charged during operation wirelessly at a considerable distance with acceptable efficiency. The Analytical code is developed in Engineering Equation Solver (EES) software. The acquired results for the main components of the system have a good agreement with published papers of other researchers. In this study, three commercial photovoltaic (PV) materials were evaluated. The results show that an equipped drone with laser-based WPT system is able to approximately receive net power of 73.5, 62.6, and 33.2 W at a 500 m distance by GaAs, CdTe, and c-Si PV materials, respectively, while the laser module consumes around 600 W electrical power. Moreover, since the optical power of the laser is concentrated on PV panel, a two-phase cooling system is suggested for PV panel to reduce the temperature of the under radiate area. Results demonstrate that the temperature of GaAs PV material without a two-phase cooling system is approximately three times more than the temperature of PV panels with a two-phase cooling system at high power transfer rates. [ABSTRACT FROM AUTHOR]