Frequency regulation of an islanded microgrid using integral sliding mode control

This paper presents a robust frequency control in an islanded ac microgrid (MG). The microgrid considered in this paper consists of distributed energy sources (DES). These include micro-turbine (MT), diesel engine generator (DEG), fuel cell (FC), renewable energy sources like wind turbine (WT), photovoltaic panels (PV), and energy storage systems like battery energy storage system (BESS) and flywheel energy storage system (FESS). The load fluctuation, wind power fluctuation and solar irradiation fluctuation often influence the frequency of the MG. So to tackle this problem, a proportional-integral-derivative controller (PID) is designed and optimized by using the stochastic fractal search (SFS) algorithm. The proposed controller is very effective and able to eliminate the frequency deviation in a short time. The time-domain simulation results obtained by SFS based PID controller are compared with the results obtained by SFS based PI controller to show the effectiveness of the proposed controller. From the comparison, it can be observed that proposed controller gives better frequency stability and hence improves the frequency of the MG significantly.

This research presents a novel approach for frequency control in an islanded AC Microgrid (MG) system, which integrates multiple renewable energy sources such as solar and wind energy. Variations in the output of these renewable sources can cause fluctuations in both frequency and power within the microgrid. To address these disturbances, a suitable controller is necessary. In this study, a Tilt Integral Derivative (TID) controller is designed for the microgrid, with its parameters optimized and tuned using a novel hybrid Grey Wolf and Cuckoo Search (HGW-CS) algorithm to mitigate these fluctuations. The proposed microgrid consists of various distributed generation (DG) sources, including a Diesel Engine Generator (DEG), Flywheel Energy Storage System (FESS), Battery Energy Storage System (BESS), Micro Turbine (MT), Fuel Cell (FC), and Aqua-electrolyze (AE), all working together to meet the load demand. The performance of the novel HGW-CS-based TID controller is evaluated under different scenarios, demonstrating its robustness and superior performance even in adverse conditions. All simulations were conducted in MATLAB, and the results indicate that the proposed controller outperforms conventional controllers in terms of stability and response to disturbances.

In this paper, sliding mode controller (SMC) and integral sliding mode controller (ISMC) are designed for a variable speed wind turbine. The main objective of the controller is to optimize the energy captured from the wind at below rated power. To overcome the steady state error, the ISMC is introduced. However, in the presence of large uncertainties, ISMC control approach produces oscillatory phenomenon due to the higher needed gain. To eliminate the tradeoffs between the ISMC tracking performance and the high gain at the control input, we have introduced a new method based on the modification of the output tracking error through the use of the exponential function in order to eliminate the reaching phase. The stability of the proposed ISMC is analyzed by Lyapunov theory and the control action didn't show any chattering. The simulation results of ISMC are presented and the control performance is compared with conventional SMC.

Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero.

Conventional sliding mode controllers have been successfully applied to control various types systems including the Stewart platform manipulator. But chattering, lack of robustness in the reaching phase and sensitivity to unmatched uncertainty are the basic problems which delayed their practical implementations. To avoid the non robust reaching phase to compensate unmatched uncertainties, integral sliding mode controllers have been proposed but only SISO system examples have been tested. Hence in this paper, MIMO implementation of integral sliding mode controller is tested using a 6 DOF Stewart platform manipulator. The integral sliding mode controller is implemented using MATLAB simulation and the result is compared with PD and conventional sliding mode controller. It was found that the integral sliding mode controller performs better in avoiding uncertainties due to load variation and actuator friction.

In this paper, a proportional-integral sliding mode control (PISMC) method is presented to achieve the attitude and position control of an unmanned helicopter system for autonomous hovering. The PISMC includes the advantages of both conventional sliding mode control (SMC) and integral sliding mode control (ISMC). The proposed PISMC algorithm is capable of stabilized attitude and position control for unmanned helicopter autonomous hovering, and it offers a performance improvement over the conventional SMC and ISMC algorithms as well. The system performance is quantified and an experimental unmanned helicopter system is developed to access the performance of the PISMC. Both simulations and actual flight tests are conducted to verify the PISMC attitude and position control. The simulation results show that the PISMC tracking error is smaller and that PISMC converges faster than SMC does. The flight test results prove that the proposed PISMC is effective in the attitude and position control of unmanned helicopter autonomous hovering.

The integration of renewable energy sources (RESs) at the edge of the grid plays a contributory role in advancing the transition to a sustainable energy future. However, this transition introduces significant variability, which challenges the maintenance of frequency stability in power systems. To address the impacts of RES variability, various robust control strategies have been developed. This paper compares the performance of different advanced sliding mode control (SMC) approaches, i.e., super twisting (ST) algorithm‐based second order sliding mode control (STSOSMC), second order sliding mode control (SOSMC), double integral sliding mode control (DISMC) and integral sliding mode control (ISMC) with conventional sliding mode control (SMC) for load frequency control with stochastic RESs such as biogas generators (BG) and wind‐based doubly fed induction generators (DFIG). The impact of the advanced sliding mode controllers on the voltage and rotor angle stability of the system is also explored. The efficacy of the controllers is tested under different load disturbances, considering nonlinearities such as communication delay (CD), sensor inaccuracy (SI), governor dead band (GDB), and generation rate constraint (GRC). The results demonstrate that STSOSMC achieves superior frequency regulation with minimal chattering and outperforms other controllers in terms of settling time, overshooting, and undershooting in frequency deviation. The result also demonstrates the effectiveness of STSOSMC in managing the variability and disturbances of RESs. Therefore, it could be considered the most robust control mechanism from the edge of the grid system with high penetration of RESs.

The work presented here is concerned with the robust control problem for the inverted pendulum with mismatched disturbances via a non-linear disturbance observer (NDO)-based sliding mode control (SMC) method. The NDO-based SMC method provides a solution to the main limitation of conventional first-order sliding mode controllers that is the presence of high frequency oscillations in the control input known as chattering while retaining the nominal performance. To show the effectiveness of NDO-based SMC, both the conventional SMC and the integral SMC (I-SMC) methods are employed in the simulation studies for the purpose of comparison studies and results demonstrate the merits of NDO-based SMC.

This paper proposes an integral sliding mode control with a novel model-based iterative feedforward compensating the hysteresis to finish high-precision positioning control of the piezoactuated stage. The Bouc-Wen model is established to design a novel iterative feedforward control (IFC) method. The IFC reduces the effects of inherent hysteresis on the positioning precision of the piezo-actuated stage. The positioning error of the IFC is inevitable because of the model uncertainty. Subsequently, an integral sliding mode control algorithm is implemented to further decrease the tracking error and enhance the anti-disturbance capability of the IFC. To reduce the chattering of the proposed controller, a boundary layer method based on Fal-function is designed. The stability of the integral sliding mode control is analyzed via Lyapunov stability theory. The feasibility of the proposed controller is certified by a series of simulation results. Compared with the IFC and conventional sliding mode control (CSMC) method, the proposed controller has higher performance in terms of the positioning precision of piezo-actuated stages.

In this paper, to improve the robustness and realize the precision positioning as well as speed control of the servo drive system, an integrated sliding-mode control (ISMC) optimized by differential evolution (DE-ISMC) algorithm is proposed. ISMC guarantees that a motor can reach at given position with given speed, and given speed is set first. ISMC, which is combined with speed regulator and position regulator, is different from the conventional sliding-mode control (SMC) replacing speed regulator. ISMC can guarantee that motor speed is controlled under position control mode, which is a novelty and an advantage of ISMC in this paper. To achieve good control performance of ISMC, parameters of it should be chosen exactly, so DE algorithm is selected to optimize them. The most significant influence factor of DE algorithm is the optimization iteration. Once parameters of ISMC are optimized under convergent iteration, servo system performance reaches given indices in the shortest time. Experimental results indicate that robustness of the servo system is improved; correctness and effectiveness of DE-ISMC are verified.

Islanding operation mode has been highly used in the distribution networks where the distance between generation regions and rural areas are longer. Therefore, many issues have been addressed in islanding Microgrids (MGs) especially during renewable energy resources (RESs) integration. For example, fluctuation in power transfer, changing in load voltage profile resulting to load demand response, and the reliability of MG during distributed generation insertion. Energy storage systems (ESSs) have an important role in improving the reliability of islanding MG system in presence of wind power generation (WPG). This paper presents the impact of superconducting magnetic energy storage (SMES) on the operation of MG-islanding mode. Fuzzy logic controller (FLC) used in controlling SMES active power transfer between SMES coil and MG at WPG insertion period. The obtained results indicate the effectiveness of SMES control in enhancing the reliability of islanding MG during WPG insertion and sudden load rejection on distribution systems.

In this study, a single sensor‐based maximum power point tracking (MPPT) algorithm is presented for permanent magnet synchronous generator‐based wind energy conversion system (WECS). The MPPT method used in this study uses only the wind speed sensor to generate the reference point required for the maximum power. A discrete‐time controller is proposed to bring the system to the reference point determined by the MPPT algorithm. The designed MPPT controller consisting of a new discrete‐time reaching‐law approach and a discrete‐time integral sliding mode control (SMC). The designed discrete‐time SMC‐based controller is implemented by using an Acorn RISC Machine (ARM)‐based LM4F120 microcontroller, and its performance is examined on a WECS emulator prepared in the laboratory. The results got from the real‐time studies prove that the proposed controller has the satisfactory performance to bring the system to the MPP. The performance of the proposed controller is also compared with the conventional sliding mode controller and perturb and observe method. The obtained results proved that the proposed controller has a better performance than the other controllers in terms of the settling time and output voltage's ripple.

To advance the efficiency and dynamic responsiveness of electric vehicles (EVs), this study introduces a comprehensive intelligent control and energy-management framework that couples a superconducting magnetic energy storage (SMES) assisted hybrid energy storage system (HESS) with a synchronous reluctance motor (SynRM) drive. The proposed configuration exploits the high power density and instantaneous response of SMES to enhance performance during transient load changes. To mitigate nonlinear behaviors, parameter deviations, and external disturbances within both HESS and SynRM subsystems, two resilient nonlinear controllers are designed: an Adaptive Barrier Function based Super-Twisting Sliding Mode Controller (ABC-STSMC) and a Synergetic Controller. The adaptive gains of the ABC-STSMC are tuned automatically through the Whale Optimization Algorithm (WOA) to ensure optimal robustness. Moreover, a deep reinforcement learning–driven energy management system (RL-EMS) governs real-time power allocation among the SMES, supercapacitor, and fuel cell, thereby maximizing overall efficiency and component longevity across variable driving conditions. The framework is validated through MATLAB/Simulink simulations and Hardware-in-the-Loop (HIL) experimentation. Quantitative comparisons with conventional Sliding Mode Control (SMC) and Integral Sliding Mode Control (ISMC) demonstrate that theWOA-optimized ABC-STSMC improves transient response by 28%, enhances tracking accuracy by 35%, and strengthens disturbance rejection. Simultaneously, the RL-EMS lowers switching losses and ensures balanced energy utilization. Collectively, the integrated control and management scheme offers higher reliability, robustness, and adaptability, positioning it as a promising solution for future high-performance electric mobility systems.

Fixed wing Unmanned Aerial Vehicles (FWUAVs) are widely utilized in both military and civilian sectors due to their ability to perform risky, inaccessible operations. The mathematical model of FWUAVs is complex, incorporating physical laws and coordinate systems with transformation matrices. In addition, controlling FWUAVs is challenging due to their nonlinear and coupled dynamics. This paper focuses on tracking trajectories for fixed wing unmanned aerial vehicles (FWUAVs) using a Robust Fractional Order Sliding Mode Controller (RFOSMC). An RFOSMC, which combines a conventional Sliding Mode Controller (SMC) with flexible fractional calculus, is proposed in this paper. Particle Swarm Optimization (PSO) is used to tune the control gains of RFOSMC. External disturbances and parameter variation are added to evaluate the controller’s performance. Comparative studies have been done with Linear Quadratic Regulator (LQR), Fractional Order PID (FOPID), SMC, and robust FOSMC. RFOSMC performs better than LQR, FOPID, and SMC in tracking accuracy, response speed, and overshoot. In addition, the performance comparison of RFOSMC and conventional SMC has been done based on performance index values of errors (ITAE), and RFOSMC showed a 76.5918% improvement. For a step input, RFOSMC showed the smallest settling time of 0.835s compared with 2.724s for SMC and 4.8573s for FOPID. Open-loop model verification and overall control system of FWUAV are done using MATLAB/Simulink software.

Adaptive fuzzy sliding mode control of magnetic levitation system based on Interval Type-2 Fuzzy Neural Network Identification with an Extended Kalman–Bucy filter