Development of hot and cold thermal energy storage system integrated with fuzzy logic control

Experimental evaluation of model predictive control and fuzzy logic control for demand response in buildings

The increasing integration of wind power into modern power systems has fostered the demand for reliable frequency regulation strategies, with inertial control emerging as a key solution that utilizes the kinetic energy stored in the wind turbine rotors. Traditional inertial controllers, however, usually depend on fixed gain parameters, which restrict their adaptability under changing grid conditions. This paper introduces a new inertial control strategy that combines a fuzzy logic controller with the Extended Optimized Power Point Tracking (OPPTE) algorithm to improve the frequency response of wind turbines. The fuzzy logic system allows adaptive control by responding dynamically to both frequency deviations and their rate of change, thereby adjusting the turbine’s operating point during emergency events. By shifting the operating point, the system can release more kinetic energy at critical moments, resulting in improved active power injection. The proposed approach was tested through simulation studies in MATLAB/Simulink R2024b using a detailed wind turbine model under various contingency scenarios. The results obtained demonstrate that the proposed strategy surpasses the conventional OPPTE method by significantly improving the maximum value of active power injected into the electrical grid by 6.56% and 9.68% under constant wind and wind series conditions, respectively, as well as reductions in the frequency nadir of 9.6% and 6.4%, and decreases in the frequency change rate of 5% and 4.57% in the exact scenarios. These results demonstrate that combining fuzzy logic with dynamic operating point adjustment provides a practical and effective way to strengthen inertial support and improve grid stability in power systems with high wind power integration.

Abstract The current study presents an active suspension (AS) system-based control for an eight-wheel vehicle to enhance ride comfort. The equations of motion were derived for an eleven-degrees-of-freedom (11-DOF) vehicle ride model consisting of the vertical, pitch, and roll body motions and the vertical motion of each wheel, and verified through comparison with high-fidelity TruckSim software. Three single-input fuzzy logic controllers (SFLCs) were developed to compute the vehicle’s desired vertical force, pitch moment, and roll moment. A decoupling transformation (DT) was utilised to distribute these computed force and moments into individual suspension actuator forces. The simulation results under road input tests demonstrated that SFLC-based AS can achieve reductions in root mean square error up to 56.88% for vertical displacement, 42.66% for pitch angle, and 56.91% for roll angle compared to a passive suspension system. In terms of maximum absolute values, reductions of vertical displacement, pitch angle, and roll angle are up to 56.73%, 41.98%, and 56.92%, respectively. These results confirmed the capability of the SFLC-based AS approach in enhancing ride comfort for eight-wheel vehicles.

This paper introduces a comprehensive and modular control strategy for a quadcopter employing six decoupled fuzzy logic controllers, each dedicated to controlling one degree of freedom: roll, pitch, yaw, altitude, and horizontal positions (x, y). The key novelty of this work lies in the integration of a novel multi-objective Self-Adaptive Bonobo optimizer to optimize eighteen controller gains simultaneously, leveraging Pareto-front principles for the first time in this context to effectively balance tracking accuracy with control effort. The proposed controllers robustly handle the nonlinear and coupled dynamics of the quadcopter while maintaining resilience against disturbances and model uncertainties. The obtained results validate the efficacy of this approach, demonstrating significant improvements, including up to a 51% reduction in root mean square error and 76% reduction in mean absolute error in lateral motion over baseline configurations, without compromising altitude stability and with enhanced response times compared to random gains selection. Furthermore, the control scheme is validated via real-time implementation on a dSPACE 1202 Processor-in-the-Loop platform under aggressive trajectory conditions. Finally, a novel conceptual framework is proposed for deploying these optimized fuzzy controllers directly on embedded flight hardware under aggressive trajectories, creating efficient autonomous onboard control and representing a practical step towards real-world Unmanned Aerial Vehicles applications.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-24152-y.

This paper proposes a hybrid maximum power point tracking (MPPT) controller that integrates Modified Particle Swarm Optimization (MPSO) with Adaptive Fuzzy Logic Control (AFLC) for robust and efficient power tracking under partial shading conditions (PSC). The MPSO component initially explores the global search space to locate the approximate MPP, effectively navigating through the local MPPs. Once the MPP is identified, the AFLC component fine-tunes the duty cycle using linguistic rules, ensuring rapid convergence to the global MPP with minimal oscillations. MATLAB/Simulink tests across five different partial shading patterns demonstrated that the MPSO-AFLC achieves a tracking efficiency of 99.92% ± 0.03%, convergence times between 0.006 and 0.011 s, an average settling time ≤ 0.011 s, and steady-state power oscillations below 5 W. When compared to meta-heuristic algorithms (MPSO, CSA, and GWO) and five recent hybrid methods, the proposed controller demonstrates superior robustness, faster convergence, and smaller steady-state oscillations.

Instances of excessive vehicle roll is a critical issue in vehicle safety since it reduces roll stability, comfort and handling of the vehicle. The rotation of tire is regarded as either a source of excitation or a conduit for vibrations to reach the steering system. The suspension and tie rods transmit the dynamic forces to the steering system from tire, which creates a vehicle dynamic imbalance. Though the active suspension system (ASS) controls the rollover, a significant amount of energy is needed to pump the high-pressure fluid into the ASS chambers. The energy needed to achieve variable damping force is obtained via the proposed roll interconnected semi active suspension system (RISAS). Variable damping levels are attained through the utilization of a directional control valve (DCV) in instances where suspension operation takes place. Pressure developed during suspension compression/expansion will be used instead of external pressure source to supply the fluid into the RISAS chambers. A fuzzy logic controller (FLC) regulates the quantity of fluid that passes through the DCV, where roll angle and its derivative are used as input for FLC. In order to evaluate the roll-resistant effectiveness of the proposed RISAS, double lane change maneuver (DLC) simulation was performed with 14 degrees of freedom (DOF) vehicle model in Matlab/Simulink software. Load transfer ratio (LTR) of the proposed system provides 53.6% performance enhancement than passive suspension without anti-roll bar (WO-ARB). Similarly, the roll angle of RISAS system provides 56.8% performance improvement than WO-ARB.

This paper investigates the feasibility of vehicle-integrated photovoltaic (VIPV) systems for light vehicles by developing and simulating an intelligent solar integration design based on the Tesla Model 3. The proposed system incorporates roof and bonnet-mounted photovoltaic modules, each managed by independent buck converters employing maximum power point tracking (MPPT) for optimal energy extraction. A novel fuzzy logic controller was designed to dynamically allocate auxiliary battery charging between the traction battery and the solar subsystem, using real-time irradiance and state-of-charge (SOC) inputs. The system was implemented in MATLAB/Simulink with location-specific data for Melbourne, Australia. Simulation results demonstrate high converter efficiencies of 94–95%, stable MPPT convergence within 0.5 s and an estimated annual solar contribution of 930 kWh, confirming effective control and energy management under varying conditions. This work highlights the innovative application of adaptive fuzzy control and dual MPPT coordination within VIPV systems and provides a validated basis for future optimisation and real-world integration.

This study presents an innovative adaptive non-linear fractional-order PID (FOPID) tuning methodology for a flow meter controller in a desalination plant, integrating a hybrid Particle Swarm Optimization (PSO) and Deep Q-Network (DQN)-based Reinforcement Learning (RL) strategy with a dynamic weighting mechanism to optimize control of non-linear systems with time delays and disturbances. By utilizing fractional-order parameters, the PSO-DQN-RL framework ensures global optimization and real-time adaptability under fluctuations in operational parameters. Results demonstrate superior performance over traditional methods and advanced techniques such as Genetic Algorithms (GA), Fuzzy Logic Controller (FLC), Neural Network-based PID (NN-PID), and PSO, offering faster response times, reduced overshoot, and minimal steady-state error compared to the slower and less precise outcomes of FLC, the static limitations of PSO, the rigid parameter settings of GA, and the inconsistent performance of NN. The hybrid method’s enhanced robustness and dynamic parameter evolution surpass the modest adaptability of PSO. Despite its computational complexity, the offline-online balance and real-time GUI enable scalable deployment, positioning this scientifically novel approach as a benchmark for FOPID tuning in various applications.

Traditional heat diffusion systems are typically regulated using Proportional–Integral–Derivative (PID) controllers. PID controllers still remain the backbone of numerous industrial control applications due to their simplicity, robustness, and efficiency. However, traditional tuning methods—such as Ziegler–Nichols or Cohen–Coon—often exhibit limitations when applied to systems with nonlinear dynamics, time-varying behaviors, or parametric uncertainties. To address these challenges, Fuzzy Logic Controllers (FLC) have emerged as a promising hybrid strategy, by translating quantitative and imprecise linguistic inputs into quantitative control actions, thereby enabling more adaptive and precise regulation. This is achieved through the integration of fuzzy inference mechanisms that dynamically adjust PID gains in response to changing system conditions. This study proposes a fuzzy logic control strategy for a heat diffusion system and conducts a comparative analysis against conventional PID control. The methodology encompasses system modeling, design of the fuzzy inference system, and simulation studies. To improve transient response and address time delays, additional features such as Anti-Windup compensation and a Smith Predictor are integrated into the control scheme. The final validation step involves the introduction of simulated environmental disturbances, including abrupt temperature drops, to evaluate the controller’s robustness. Simulation results demonstrate that the proposed FLC provides superior dynamic performance compared to the conventional PID controller, achieving approximately 5–7% faster rise time and 8–10% lower settling time. The incorporation of an anti-windup mechanism did not yield significant benefits in this application. In contrast, the integration of a Smith Predictor further reduced oscillatory behavior and substantially improved disturbance rejection, tracking accuracy, and adaptability under simulated thermal variations. These results underscore the effectiveness of the FLC in handling systems with time delays and nonlinearities, reinforcing its role as a robust and adaptable control strategy for thermal processes with complex dynamics.

The increasing adoption of high-performance DC motor control in embedded systems has driven the development of cost-effective solutions that extend beyond traditional software-based optimization techniques. This work presents a refined hardware-centric approach implementing real-time particle swarm optimization (PSO) directly executed on STM32 microcontroller for DC motor speed control, departing from conventional simulation-based parameter-tuning methods. Novel hardware-optimized composition of an interval type-2 fuzzy logic controller (FLC) and a PID controller is developed, designed for resource-constrained embedded systems and accounting for processing delays, memory limitations, and real-time execution constraints typically overlooked in non-experimental studies. The hardware-in-the-loop implementation enables real-time parameter optimization while managing actual system uncertainties in controlling DC micro-motors. Comprehensive experimental validation against conventional PI, PID, and PIDF controllers, all optimized using the same embedded PSO methodology, reveals that the proposed FT2-PID controller achieves superior performance with 28.3% and 56.7% faster settling times compared to PIDF and PI controllers, respectively, with significantly lower overshoot at higher reference speeds. The proposed hardware-oriented methodology bridges the critical gap between theoretical controller design and practical embedded implementation, providing detailed analysis of hardware–software co-design trade-offs through experimental testing that uncovers constraints of the low-cost microcontroller platform.

Seismic Control of High-Rise Buildings by Combining Fuzzy Logic and Higher-Order Fractional Order Controllers Using an Optimized MR+TMD

This research presents an interleaved bi-directional non-inverting buck-boost converter designed for public electric vehicle battery swapping stations (BSS). This study solves the critical problem of BSS vulnerability to main power outages, which threatens their operational reliability. The developed solution is a device that not only performs efficient charging but also functions as an emergency power source, utilizing power from connected batteries during a grid failure. The methodology incorporates an interleaved topology and a multi-stage constant current (MSCC) charging method controlled by a fuzzy logic controller (FLC). Experimental results show the interleaved operation successfully increased power capacity up to 1.1 kW, achieving an average efficiency of 93.44%. A distinctive feature of the result is the reduction of the output current ripple by 47.7% down to 0.92%. This is explained by the ripple-cancellation effect inherent to the interleaved design, which is a key feature for preserving long-term battery health. Furthermore, the MSCC method achieved a 13.7% reduction in execution time compared to the conventional constant current-constant voltage (CC-CV) method, with a total charging duration of 66.8 minutes. This validated prototype successfully demonstrated a seamless and automatic emergency mode transition during a power failure, directly answering the BSS reliability challenge. The prototype also confirmed its bidirectional functionality and seamless mode transition from standard charging to emergency power supply mode. The scope of this research provides a practical and high-performance integrated solution for BSS, effectively addressing vulnerability issues by improving reliability and charging time efficiency, ensuring continuous service.

Heating, ventilation, and air conditioning (HVAC) systems account for a substantial proportion of global energy consumption. While numerous control strategies have been developed to reduce energy consumption, conventional methods fail to balance energy efficiency and thermal comfort effectively due to the inherent nonlinearities, time-varying dynamics, and multivariable coupling effects of HVAC systems. Although interval type-2 fuzzy logic controllers (IT2 FLCs) can mitigate such uncertainties, their performance is critically dependent on the optimal configuration of membership function (MF) parameters, which conventional methods often fail to achieve. To address these issues, this paper proposes an HVAC-adapted IT2 FLC and develops an improved subtraction-average-based optimization algorithm (ISABO) with adaptive mechanisms to enhance MF parameter tuning. Benchmark tests demonstrate that ISABO achieves faster convergence and higher solution accuracy than the original subtraction-average-based optimization algorithm. The results of the simulation experiments verify the superiority of the proposed method: temperature tracking errors are reduced to near-zero values, humidity ratio deviations decrease to 2.8%, and energy consumption is lowered by 22.8% compared with conventional fuzzy controllers.

Under both typical and partial shading conditions, this research seeks to assess how two maximum power point tracking (MPPT) solutions, Perturb and Observe (P&O) and fuzzy logic control (FLC), help maximize power extraction from a photovoltaic (PV) system. Applying MATLAB SIMULINK, a DC-DC converter and a PV generator were simulated to run these MPPT systems. The comparison focuses on the extracted power, the performance of each technique, and their ability to follow the global maximum power point (MPP). The simulation findings show that in standard and partial shading conditions, both P&O and fuzzy logic algorithms can effectively track the MPP. The fuzzy logic controller, however, turned out to be more accurate and efficient (≥98% efficiency vs. P&O's 97%) with minimal power oscillation, while the P&O algorithm had a faster response time.

Renewable generation units, along with storage systems, are being integrated into shipboard microgrids (SHMGs) for enhanced operational efficiency and cost-effective benefits. However, the direct current (DC) voltage profiles of SHMGs are affected by sudden changes in load, intermittent renewable inputs, and unmodeled dynamics, which necessitate advanced control strategies to address them. To address the above problem, an adaptive data-driven controller has been proposed for voltage regulation of SHMGs with hybrid energy storage units (HESUs) to preserve robust stability across a wide range of operational situations. The proposed data-driven voltage regulator is developed in two stages: (i) an ultra-local model control (ULMC) is designed to stabilize the voltage outcomes of SHMG using the input/output (I/O) data. In this stage, a regularized actor-critic (RAC) using deep neural networks is adopted to adaptively adjust coefficients of ULMC, and (ii) in the next stage, the un-modeled phenomena and disturbances included in the marine power system are approximated by non-integer extended state observer (NIESO). In this approach, deep neural networks of RAC learning are trained in such a way that they obtain an optimal policy using a reward function that is defined based on the regulation requirements of SHMG. The proposed control framework provides this possibility for the system to have a robust estimation and compensation to tackle the largely unknown nonlinear disturbances and unmodeled dynamics. Hardware-in-the-loop (HiL) simulation using OPAL-RT has been employed as a powerful real-time testbed to evaluate the feasibility and applicability of the proposed voltage compensator under realistic operations of SHMG. HiL outcomes reveal that the proposed controller based on RAC achieves significant enhancement in performance indexes, with improvements of 44.08% over the fuzzy logic controller and 36.85% over the model predictive controller (MPC).

In this modern era, most household appliances require electrical energy as an energy source. The use of energy in large and sustainable amounts will cause an energy crisis. Where fossil fuels will run out and cannot be renewed. Therefore, renewable alternative energy is needed that can be used as an energy substitute for one of the solutions. One of the alternative energies is solar cell energy that to supply the energy needs of electric stoves. This study discusses photovoltaic system that use 10 solar cells each with a power of 100 WP and 90 VDC. The electrical energy generated from the solar cell is 1000 WP. From the solar cell, the voltage is increased using a Single-Ended Primary-Inductor Converter (SEPIC) converter and controlled using Fuzzy Logic Control (FLC). The output voltage is used to meet the power needs of an electric stove with a maximum power of 650 W which has an average efficiency of 93%.

Abstract Unicycles possess inherent instability, requiring cohesive design and control mechanisms to maintain balance. This review examines various control strategies and structural designs that enhance unicycle stability. The position of the center of gravity plays a critical role, and it is influenced by factors such as chassis design, supporting structures, and battery placement. Control systems process data from sensors and make adjustments using methods that range from simple methods ranging from simple proportional-integral-derivative (PID) controllers to advanced approaches such as fuzzy logic and predictive control. Sensors, including accelerometers and gyroscopes, detect inclination and movement that maintain an upright position. The ability to perform real-time adjustments can be enhanced by components such as motors and reaction wheels. Additionally, hardware such as Arduino and SoPCs enhances stability control by providing real-time computing capabilities. This review highlights gaps in current knowledge by gathering insights from scholarly articles, research papers, and patents. This research contributes ideas and knowledge toward improving electric unicycles in terms of stability, efficiency, and suitability for real-world applications.

This paper proposes an intelligent control strategy to improve the power quality in grid-connected photovoltaic (PV) systems. Conventional controllers such as proportional-integral (PI) and proportional-resonant (PR) controllers are studied and compared with intelligent counterparts, specifically fuzzy logic control (FLC) and fuzzy logic control optimized using Particle Swarm Optimization (FLC-PSO). The PV system with power electronic converters is modeled and simulated in MATLAB/Simulink under nonlinear load and different test conditions. Results show that the intelligent controllers significantly outperform the conventional control algorithms: FLC-PSO yields a total harmonic distortion (THD) of 4.7% while PI control achieves 36.1%; the power factor simultaneously increases from 0.86 (PI) to 0.99 (FLC-PSO). From the study it is proved that optimized fuzzy logic control not only reduces harmonics but also improves the grid compliance and energy conversion efficiency. These findings provide useful guideline for designing and implementing high-performance control strategies in modern PV systems, highlighting the practical potential of simulation-driven analysis for PV system integration.

ABSTRACT Extended -range fuel cell electric vehicles (FCEVs) have attracted widespread attention due to their high efficiency and environmental friendliness. However, traditional energy management strategies based on fixed parameters are insufficiently adaptable in complex and variable driving conditions, limiting the improvement of the overall economic performance of the vehicle. To address this issue, this study proposes an adaptive equivalent consumption minimization strategy, which introduces a driving condition identification and prediction mechanism to achieve real-time dynamic adjustment of the equivalent factor. This strategy combines fuzzy logic control and dynamic driving condition switching algorithms, enabling adaptive optimization of energy allocation based on the current and predicted driving scenarios. This paper establishes a high-fidelity FCEV power system simulation platform and builds a driving condition identification and prediction model using radial basis function neural networks and machine learning methods to achieve precise scene classification and short-term driving condition prediction. Simulation results show that, under the WLTC cycle, compared with the conventional strategy, the proposed method can reduce hydrogen consumption by 2.49%, equivalent hydrogen consumption by 2.54%, energy loss rate by 1.42%, and terminal SOC closer to the target value, demonstrating better energy balance and condition adaptability.