Search Results (644)

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work. Full article

This paper addresses the insufficient speed control accuracy observed in traditional seeding systems. This paper proposes an electric drive seeding control method that incorporates a composite control strategy combining the Rime optimization algorithm (RIME) with a backpropagation neural network (BPNN). Firstly, the architecture including radar/proximity switch dual-mode speed measurement, STM32F103 main control, and asymmetric half-bridge drive was constructed. Based on the kinematic model, a motor speed-plant spacing mapping relationship was derived to complete the selection of a brushless DC motor. Secondly, this study addresses the issues of large overshoot in traditional PID control, response lag in fuzzy PID, and local optima in BP-PID. To overcome these challenges, the RIME algorithm is employed to optimize the weight-updating mechanism of the backpropagation neural network (BPNN). The soft RIME search facilitates multi-directional exploration, while the hard RIME puncture enhances global optimization capability, significantly improving the adaptive accuracy of the parameters. The simulation results showed that the adjustment time of the proposed RIME-BP-PID in the step response is 73.8% shorter than the BP-PID, and the overshoot is reduced to 0.23%. The square wave tracking error is 27.8% of the traditional PID. The bench test was carried out at 6–12 km/h speed and 200–300 mm. The results showed that, compared with BP-PID, the qualified index of RIME-BP-PID increased by 1.67–1.94 percentage points, the missed seeding index decreased by 1.25–1.80 percentage points, and the coefficient of variation decreased by 4.90–5.82 percentage points. The algorithm effectively solves the problem of the strong nonlinear time-varying control of a seeding system and provides theoretical support for the research and development of precision agricultural equipment. Full article

To address the issue of low fertilizer proportioning accuracy in irrigation and fertilization systems due to neglecting the influence of target ions in raw water, this study designed a high-precision online automatic water–fertilizer mixing device that can directly mix raw water (without water purification treatment) with fertilizer stock solution. This device is capable of preparing mixed fertilizer solutions containing N, K, and Ca elements. It employs ion-selective electrodes and flow meters for online detection and feedback of target ion concentrations in the fertilizer solution and flow rate information, and adopts an online fertilizer mixing control strategy that uses a constant raw water flow rate and a fuzzy PID control method to dynamically adjust the pulse frequency of metering pumps, thereby changing the injection volume of nutrient solution. Simulation and experimental analyses show that the piping system of the device is reasonably designed, ensuring stable and smooth fertilizer injection. The temperature-compensated concentration detection models for the three target ions in the fertilizer solution, constructed using a stepwise fitting method, achieve average relative detection errors of 1.94%, 1.18%, and 2.87% for K⁺, NO₃⁻, and Ca²⁺, respectively. When preparing single-element or mixed fertilizer solutions, the device achieves an average steady-state error of no more than 4% and an average steady-state time of approximately 40 s. Compared with deionized water, the average relative errors for potassium ions, nitrate ions, and calcium ions when preparing fertilizer solutions with raw water are 1.33%, 1.12%, and 1.19%, respectively. Compared with the theoretical errors of fertilizer preparation with raw water, the fertilizer proportioning errors of this device for potassium ions, nitrate ions, and calcium ions can be reduced by a maximum of 10.55%, 66.84%, and 62.71%, respectively, which is superior to the performance requirements for water–fertilizer integration equipment specified in the national industry standard DG/T 274-2024. Additionally, the device achieves accurate and stable fertilizer proportioning with safe and reliable operation during 6 h of continuous operation. This device significantly reduces the impact of raw water on fertilizer proportioning accuracy, improves the adaptability of the device to irrigation water sources, and provides theoretical basis and technical support for water-fertilizer integration systems in cost-sensitive agriculture. Full article

In the case of small disturbances in the power grid, virtual synchronous generators (VSGs) often exhibit active power steady-state errors and significant frequency overshoot, and it is difficult to balance the reduction of active power steady-state errors and the mitigation of frequency overshoot. This paper proposes an improved control method based on active power differential compensation (APDC). First, an active power differential compensation loop is introduced, effectively addressing the issues of active power steady-state deviation and frequency overshoot caused by fixed parameters in the traditional VSG. Secondly, by incorporating a fuzzy logic control (FLC) algorithm, an adaptive PID tuning strategy is proposed as a replacement for the traditional fixed virtual inertia; the PID parameters are dynamically adjusted in real time according to the power–angle deviation and its rate of change, thereby enhancing the small-disturbance dynamic performance of the VSG. Finally, MATLAB R2020b/Simulink simulations and StarSim hardware-in-the-loop simulations validate the effectiveness and accuracy of the proposed control strategy. Simulation results indicate that, compared to traditional control strategies, under peak regulation conditions, the frequency overshoot is reduced by approximately 4.4%, and the active power overshoot is reduced by approximately 5%; under frequency regulation conditions, the frequency overshoot is reduced by approximately 0.26%, and the power overshoot is reduced by approximately 12%. Full article

Open-channel irrigation systems often face constraints due to water supply uncertainty and insufficient gate control precision. This study proposes an integrated framework for canal water allocation and gate control that combines interval-based uncertainty analysis with intelligent optimization to address these challenges. First, we predict the inflow process using an Auto-Regressive Integrated Moving Average (ARIMA) model and quantify the range of water supply uncertainty through Maximum Likelihood Estimation (MLE). Based on these results, we formulate a bi-objective optimization model to minimize both main canal flow fluctuations and canal network seepage losses. We solve the model using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to obtain Pareto-optimal water allocation schemes under uncertain inflow conditions. This study also designs a Fuzzy Proportional–Integral–Derivative (Fuzzy PID) controller. We adaptively tune its parameters using the Particle Swarm Optimization (PSO) algorithm, which enhances the dynamic response and operational stability of open-channel gate control. We apply this framework to the Chahayang irrigation district. The results show that total canal seepage decreases by 1.21 × 10⁷ m³, accounting for 3.9% of the district’s annual water supply, and the irrigation cycle is shortened from 45 days to 40.54 days, improving efficiency by 9.91%. Compared with conventional PID control, the PSO-optimized Fuzzy PID controller reduces overshoot by 4.84%, and shortens regulation time by 39.51%. These findings indicate that the proposed method can significantly improve irrigation water allocation efficiency and gate control performance under uncertain and variable water supply conditions. Full article

With the rapid development of precision seeding technology, which plays a vital role in promoting large-scale cultivation, reducing seed loss, increasing crop yield, and improving land use efficiency, a maize precision seed metering control system based on KF-RTS fusion speed measurement has been developed to address the issues of ground wheel slippage and chain bounce in Chinese precision planters during high-speed operation, as well as the problems of speed measurement delay, motor control lag, and susceptibility to interference in existing electric drive seeders. The system comprises an STM32 master controller, a speed acquisition unit, a seed metering drive unit, and a human–machine interaction interface. By employing a multi-rate KF-RTS (Kalman Filter-Rauch-Tung-Striebel Smoother) fusion algorithm that integrates RTK-GNSS and accelerometer data, it significantly enhances the accuracy and real-time performance of forward speed measurement. A control strategy combining Kalman filtering with a fuzzy PID controller, optimized by a particle swarm algorithm, enables the control system to converge rapidly within 0.10 s with a steady-state error below 0.55%, achieving precise and stable regulation of the seed metering shaft speed. Field test results demonstrate that the qualified index of seed spacing reaches no less than 94.11% under the fusion speed measurement method. Compared to the RTK-GNSS speed measurement alone, the coefficient of variation in seed spacing is reduced by 3.85% to 6.93%, effectively resolving seed spacing deviations caused by speed measurement delays and improving seeding uniformity. Full article

The traditional ground taxiing method of aircraft has the drawbacks of low efficiency and excessive fuel consumption. In this paper, an aircraft engine-off taxi towing system (AEOTTS) is proposed to provide high-speed traction for the aircraft throughout the entire ground movement. This will be a more efficient intelligent taxiing mode for aircraft. However, the new braking control strategy for the AEOTTS under complex conditions is not yet mature. Based on the motion and mechanical symmetry of the AEOTTS and combined with the contact model of the pick-up and holding system (PUHS), a coupling dynamic model of the AEOTTS is established. On this basis, a state estimator of the AEOTTS is established using the unambiguous Kalman filtering (UKF) method. The follow-up braking control system of the AEOTTS is constructed with the goal of minimizing the towing force on the aircraft’s nose landing gear (NLG), combined with the optimization of braking force distribution and the fuzzy PID control method. By comparing the braking performance of three follow-up braking control systems under wet runway conditions and runway unevenness conditions, the results show that compared with the other two control methods, the follow-up braking control system proposed in this paper can effectively reduce the towing force on the aircraft’s NLG and the braking distance of the AEOTTS, ensuring the safety of the taxiing and traction braking process. Full article

To address issues such as chassis attitude deviation, reduced operational efficiency, and diminished precision when agricultural machinery operates in complex terrains—including steep slopes and fragmented plots in hilly and mountainous regions—a servo electric cylinder-based active suspension levelling system has been designed. Real-time dynamic control is achieved through a fuzzy PID algorithm. Firstly, the suspension’s mechanical structure and key parameters were determined, employing a ‘servo electric cylinder-spring-shock absorber series’ configuration to achieve load support and passive vibration damping. Secondly, a kinematic and dynamic model of the quarter-link suspension was established. Finally, Simulink simulations were conducted to model the agricultural machinery traversing mountainous, uneven terrain at segmented stable operating speeds, thereby validating the suspension’s control performance. Simulation results demonstrate that the system maintains chassis height error within ±0.05 m, chassis height change rate within ±0.2 m/s, and response time ≤ 0.8 s. It rapidly and effectively counteracts terrain disturbances, achieving precise chassis height control. This provides theoretical support for designing whole-vehicle levelling systems for small agricultural machinery in hilly and mountainous terrains. Full article

Experience shows that knowledge transfer and understanding of fundamental FPGA principles are greatly improved by exercising laboratory practices and manual hands-on operations. Hence, a case study was performed on two didactic platforms for students of intelligent control techniques that were upgraded with FPGAs to be involved in laboratory practices. Among others, platforms allow implementation of traditional linear control algorithms, such as PID, or modern non-linear control algorithms, such as fuzzy logic or artificial neural networks. Initially, the underlying physics can be carefully studied, and the mathematical model can be derived. Then, such a model can be discretized into its digital form, an appropriate controller can be designed, and its performance can be compared to the known benchmark. Controllers and control parameters can be practiced by students themselves, offering underlying potential for improving students’ understanding of the fundamentals of FPGA. Full article

In modern industrial automation, optimizing the performance of Programmable Logic Controller (PLC)-based PID controllers is critical for ensuring precise process control. This study presents a novel methodology for the dynamic tuning of built-in Proportional-Integral-Derivative (PID) controllers in PLCs using a hybrid algorithm that combines Particle Swarm Optimization (PSO) and Multiple-Adaptive Neuro-Fuzzy Inference System (MANFIS). Classical PID tuning methods, such as Ziegler–Nichols and Cohen–Coon, have traditionally been employed in industrial control systems. However, these methods often struggle to address the complexities of nonlinear, time-varying, or highly dynamic processes, resulting in suboptimal performance and limited adaptability. To overcome these challenges, the proposed PSO-MANFIS hybrid algorithm leverages the global search capabilities of PSO and the adaptive learning abilities of MANFIS to optimize PID parameters in real-time dynamically. Integrating MATLAB (R2021a) with industrial automation systems via an OPC (OLE for Process Control) server utilizes advanced optimization algorithms within MATLAB to obtain the best possible parameters for the industrial PID controller, enhancing control precision and optimizing production efficiency. This MATLAB-PLC interface facilitates seamless communication, enabling real-time monitoring, data analysis, and the implementation of sophisticated computational tools in industrial environments. Experimental results demonstrate superior performance, with reductions in rise time from 93.01 s to 70.98 s, settling time from 165.28 s to 128.84 s, and overshoot eliminated from 0.0012% to 0% of the controller response compared to conventional tuning. Furthermore, the proposed approach achieves a reduction in Root Mean Square Error (RMSE) by approximately 56% to 74% when compared with the baseline performance. By integrating MATLAB’s computational capabilities with PLC-based industrial automation, this study provides a practical and innovative solution for modern industries, offering enhanced adaptability, precision, and reliability in dynamic control applications, ultimately leading to optimized production outcomes. Full article

Deploying deep reinforcement learning (DRL) in safety-critical nuclear control is limited less by raw performance than by the absence of licensable, audit-ready evidence. We introduce a Deterministic Assurance Framework (DTAF) that converts controller behavior into licensing-grade proof by combining the following: (i) deterministic licensing gates tied to formal safety and performance limits (e.g., Total Time Unsafe (TTU) = 0; bounded Transient Severity Score (TSS); and minimum Grid Load-Following Index (GLFI)); (ii) a portfolio of adversarial stress tests representative of off-nominal operation; and (iii) a traceability and explainability package that renders every evaluated action auditable. The DTAF is demonstrated on a high-fidelity pressurized-water-reactor (PWR) simulation model used as a software-in-the-loop testbed. Three governor architectures are evaluated under identical, fixed scenarios: a curriculum-trained Soft Actor–Critic (SAC) agent, and Differential-Evolution-optimized Proportional–Integral–Derivative (PID-DE) and Fuzzy-Logic (FLC-DE) Controllers. Performance is assessed deterministically via gate-aligned metrics—TTU, TSS, GLFI, cumulative control effort (CE_sum), valve-reversal count (V_rev), and speed overshoot (OS_ω). Across the adversarial portfolio, the SAC controller meets the predeclared licensing gates in single-run evaluations, whereas the strong conventional baselines violate gates in specific high-severity cases; where all methods remain within the safe envelope, the SAC delivers a higher GLFI and lower CE_sum, with fewer reversals and reduced overshoot. All licensing conclusions derive from deterministic single-run tests; a small, fixed-seed check (three seeds with descriptive intervals) is reported separately as non-licensing supplementary analysis. By producing transparent, reproducible artifacts, the DTAF offers a regulator-oriented pathway for qualifying DRL controllers in grid-interactive nuclear operations. Full article

In order to solve the problems of thermal management efficiency and temperature control accuracy in the passenger compartment of electric vehicles, the phase change thermal storage design concept and the model-free adaptive control method are applied to the thermal management temperature control system of the passenger compartment. Aiming at the characteristics of waste heat utilization of the whole vehicle and the preheating demand of the passenger compartment, an integrated vehicle thermal management model with a heat exchanger and storage function and an intelligent temperature control system scheme for the passenger compartment is designed. Aiming at the demand for adaptive control of the thermal management system of the passenger compartment of the whole vehicle, a composite strategy of PID control of compressor speed and model-free adaptive control of water pump speed are proposed, and the effect of the application of different control strategies under the demand for temperature control of the passenger compartment is compared and analyzed in simulation. The study shows that the phase change heat storage system and its model-free adaptive control in this paper are more stable, with smaller overshoot and high temperature regulation accuracy; the overshoot of PID control and fuzzy PID control is 14.17% and 8.58%, respectively, while the overshoot of model-free adaptive control is only 0.42%, which verifies the superiority of the designed thermal management system and the effectiveness of the control algorithm, and will effectively enhance the thermal comfort of the passenger compartment of electric vehicles. Full article

This study examines dual-area load–frequency control (LFC) in the context of significant renewable energy integration, energy storage systems (ESSs), and collective electric vehicle (EV) involvement. We propose a RBBMO-FO-FuzzyPID+PI(1+DD) hybrid controller in which fractional-order fuzzy regulation shapes the ACE, while an auxiliary PI(1+DD) path adds damping without steady-state penalty. Across ideal linear plants, 3% governor-rate constraints (GRC), and stressed conditions that include contract violations in Area-2, renewable power variations, and partial EV State of Charge (SoC 50–70%), EV participation yields systematic gains for all controller families, and the magnitude depends on the control architecture. Baseline methods improve by 15–25% with EVs, whereas advanced designs—especially the proposed controller—benefit by 25–45%, revealing a clear synergy between controller intelligence and EV flexibility. With EV support, the proposed controller limits frequency overshoot to 0.055 Hz (a 20–55% reduction versus peers), caps the nadir at −0.065 Hz (15–41% better undershoot), and attains 3.5–4.5 s settling (25–61% faster than competitors), while holding tie-line deviations within ±0.02 Hz. Optimization studies confirm the algorithmic advantage: RBBMO achieves 30% lower cost than BBOA and converges 25% faster; EV integration further reduces cost by 15% and speeds convergence by 12%. A strong correlation between optimization cost and closed-loop performance (r² ≈ 0.95) validates the tuning strategy. Collectively, the results establish the proposed hybrid controller with EV participation as a new benchmark for robust, system-wide frequency regulation in renewable-rich multi-area grids. Full article

Traditional water–fertilizer control systems often suffer from poor precision and slow response, limiting precision agriculture development. This study developed an electrical conductivity (EC) control system for water–fertilizer integration using a fuzzy Proportional-Integral-Derivative (PID) controller optimized by particle swarm optimization (PSO) and integrated with IoT technology. MATLAB/Simulink simulations showed the proposed controller achieved the smallest overshoot (7.64–8.15%), with average settling time reduced by 62.48 s and 20.38 s compared to conventional PID and fuzzy PID controllers, respectively (p < 0.001). Field experiments on winter wheat demonstrated a mean absolute EC deviation of 0.01125 ms/cm, with root-mean-square error (RMSE) of 0.0217 ms/cm, indicating high precision under field conditions. The system also maintained soil moisture in the optimal range (19–25%) with high irrigation uniformity (Christiansen’s coefficient Cu = 97.6%). The system maintained soil moisture in the optimal range (19–25%) while supporting stable soil nutrient levels and crop growth parameters. This study provides a validated technical solution for precision EC control while establishing a foundation for future fully integrated water–fertilizer management systems. Full article

To address the issues of high labor intensity, high missed harvest rates, and high damage rates associated with traditional subsurface crop harvesters, this paper takes carrots as the research object and designs an automatic row-following device based on collaborative perception and intelligent control. Firstly, the physical characteristic parameters and planting agronomic requirements of carrots in a harvest period were systematically measured and analyzed, and a collaborative control architecture with ‘lateral row-following and longitudinal profiling’ as the core was established. The architecture was composed of a lateral detection mechanism and a ridge surface floating detection mechanism. Building on this, this paper designed a control system with a STC12C5A60S2 single-chip microcomputer as the control core and a fusion fuzzy PID algorithm. By collaboratively driving the lateral and vertical stepper motors, the system achieved a precise control of the digging device’s position and posture, significantly improving the response speed and control stability under complex ridge conditions. Through the simulation of SolidWorks (2019) and RecurDyn (2023), the structural reliability and dynamic profiling effect of key components were validated from both static and dynamic perspectives, respectively. The parameter optimization results based on the response surface method show that the lateral motor speed and the forward speed are the dominant factors affecting the lateral accuracy and the vertical accuracy, respectively. Under the optimal parameter combination, the mean lateral deviation of the device measured in the field test was 1.118 cm, and the standard deviation was 0.257 cm. The mean vertical deviation is 0.986 cm, and the standard deviation is 0.016 cm. This study provides a feasible technical solution for the mechanized agronomic operation of carrots and other subsurface crops. Full article