Search Results (90)

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

Current power systems are facing noticeable power quality (PQ) performance deterioration, which has been attributed to nonlinear loads, distributed generation, and extensive renewable energy infiltration (REI). These conditions cause voltage sags, harmonic distortion, flicker, and disadvantageous power factors. The traditional PI/PID-based scheme of control, when applied to Flexible AC Transmission Systems (FACTSs), demonstrates low adaptability and low anticipatory functions, which are required to operate a grid in real-time and dynamic conditions. Artificial Intelligence (AI) opens proactive, reactive, or adaptive and self-optimizing control schemes, which reformulate FACTS to thoughtful, data-intensive power-system objects. This literature review systematically studies the convergence of AI and FACTS technology, with an emphasis on how AI can improve voltage stability, harmonic control, flicker control, and reactive power control in the grid formation of various types of grids. A new classification is proposed for the identification of AI methodologies, including deep learning, reinforcement learning, fuzzy logic, and graph neural networks, according to specific FQ goals and FACTS device categories. This study quantitatively compares AI-enhanced and traditional controllers and uses key performance indicators such as response time, total harmonic distortion (THD), precision of voltage regulation, and reactive power compensation effectiveness. In addition, the analysis discusses the main implementation obstacles, such as data shortages, computational time, readability, and regulatory limitations, and suggests mitigation measures for these issues. The conclusion outlines a clear future research direction towards physics-informed neural networks, federated learning, which facilitates decentralized control, digital twins, which facilitate real-time validation, and multi-agent reinforcement learning, which facilitates coordinated operation. Through the current research synthesis, this study provides researchers, engineers, and system planners with actionable information to create a next-generation AI-FACTS framework that can support resilient and high-quality power delivery. Full article

With the increasing demand for sustainable food production, the facility agriculture is progressively developing towards automation and intelligence. Traditional control techniques such as PID, fuzzy logic, and model predictive control have been widely applied in greenhouse planting for years. Existing greenhouse management systems still face challenges such as limited adaptability to fluctuating outdoor climates, and difficulties in maintaining both productivity and cost-effectiveness. Recently, with the development of greenhouse systems towards comprehensive environmental perception and intelligent decision-making, a large number of intelligent control and modeling technologies have provided new opportunities for the technological update of greenhouse management systems. This review systematically summarizes recent progress in greenhouse regulation and crop growth control technologies, emphasizing applications of intelligent techniques, involving adaptive strategies, neural networks, and reinforcement learning. Special attention is given to how these methods improve system robustness and control performance in terms of environmental stability, crop productivity, and energy efficiency, which are key performance indicators of greenhouse systems. Their advantages over conventional strategies in agricultural greenhouse systems are also analyzed in detail. Furthermore, the integration of intelligent technologies with greenhouse system modeling is examined, covering both greenhouse environmental models and crop growth models. The strengths and weaknesses of different techniques, such as mechanism, computational fluid dynamics (CFD), and data-driven models, are analyzed and discussed in terms of accuracy, computational cost, and applicability. Finally, future challenges and research opportunities are discussed, emphasizing the need for real-time adaptability, sustainability, and cluster intelligence. Full article

Runway surface roughness is recognized as a principal cause of passenger aircraft vibration during taxiing, adversely affecting ride comfort, safety, and even human health. Effective mitigation of such vibrations is therefore essential for improving passenger experience and operational reliability. Previous studies have investigated passive, semi-active, and intelligent controllers such as PID, H∞, and ANFIS; however, the comprehensive application of a robust adaptive neuro-fuzzy inference system (RANFIS) to active landing-gear control has not yet been addressed. The novelty of this work lies in combining robustness with adaptive learning of fuzzy rules and neural network parameters, thereby filling this critical gap in the literature. To investigate this, a six-degrees-of-freedom aircraft dynamic model was developed, and three controllers were comparatively evaluated: model-based neural network (MBNN), adaptive neuro-fuzzy inference system (ANFIS), and the proposed RANFIS. Performance was assessed in terms of rise time, settling time, peak value, and steady-state error under stochastic runway excitations. Simulation results show that while MBNN and ANFIS provide satisfactory control, RANFIS achieved superior performance, reducing vibration peaks to ≤0.3–1.0 cm, shortening settling times to <1.5 s, and decreasing steady-state errors to <0.05 cm. These findings confirm that RANFIS offers a more effective solution for enhancing comfort, safety, and structural durability in next-generation active landing-gear systems. Full article

Facing global pressures such as population growth, shrinking arable land, and climate change, intelligent agriculture has emerged as a critical pathway toward sustainable and efficient agricultural production. Control algorithms serve as the core enabler of this transition, finding applications in crop production, pest management, agricultural machinery, and resource optimization. This review systematically examines the performance and applications of both traditional (e.g., PID, fuzzy logic) and advanced control algorithms (e.g., neural networks, model predictive control, adaptive control, active disturbance rejection control, and sliding mode control) in agriculture. While traditional methods are valued for simplicity and robustness, advanced algorithms better handle nonlinearity, uncertainty, and multi-objective optimization, enhancing both precision and resource efficiency. However, challenges such as environmental heterogeneity, hardware limitations, data scarcity, real-time requirements, and multi-objective conflicts hinder widespread adoption. This review contributes a structured, critical synthesis of these algorithms, highlighting their comparative strengths and limitations, and identifies key research gaps that distinguish it from prior reviews. Future directions include lightweight algorithms, digital twins, multi-sensor integration, and edge computing, which together promise to enhance the scalability and sustainability of intelligent agricultural systems. Full article

To address the challenge of effective roof support in fully mechanized excavation roadways, this paper proposes an adaptive control method for the initial support force of self-shifting temporary supports based on pressure sensors. First, the mechanical characteristics of the roof in fully mechanized excavation faces were analyzed, a static model of the roadway roof thin plate was established, the mechanical criteria for heading support were determined, and the reasonable calculation of the initial support force and working resistance for heading support was completed. Then, the pressure-control system of the hydraulic cylinder was modeled, achieving real-time online adjustment of PID control parameters based on fuzzy neural network control, and an adaptive control system for initial support force based on feedback from pressure sensors inside the hydraulic cylinder was constructed. Finally, comparative experiments of fuzzy neural network PID (FNN-PID) and fuzzy PID control were conducted in both the AMESim 2304 and Matlab/Simulink 2016 co-simulation environment and real physical scenarios. The effectiveness and advancement of the proposed control algorithm were verified. Full article

High-power laser diodes (LDs) inherently generate considerable heat during current loading, which presents substantial challenges to the stable operation of laser systems. This study reports a machine learning-based approach that is to be applied to LD temperature control systems, in which a fuzzy neural network (FNN) algorithm is integrated with a proportional-integral-derivative (PID) controller to create an FNN-PID control architecture. The proposed algorithms synergistically integrate fuzzy rule-based systems with neural network learning frameworks, and, furthermore, facilitate adaptive parameter optimization while preserving the interpretability of the decision-making process. Applying the optimized algorithm temperature controller to the LD with output optical power of 110 W @ 888 nm, compared with the conventional PID, the FNN-PID algorithm has shortened the temperature settling time by 77% during 100 W heat generation in LD, the long-term temperature fluctuation is decreased from ±0.126% to ±0.06%, the corresponding optical power steady-state precision is decreased from ±0.09% to ±0.04%, and the step response time of temperature and corresponding power are reduced by 73.4% and 70% from 25 °C to 27 °C, respectively. The FNN-PID outperforms conventional methods (the PID algorithm and the Fuzzy-PID algorithm) in managing thermal fluctuations, and it offers potential for precise laser control applications to enhance beam quality and stability. Full article

Aiming at the complex construction environment and autonomous navigation challenges in underground coal mine roadways, this paper proposes a path navigation and deviation correction control method for tracked roadheaders in confined roadway spaces. First, a two-dimensional planar grid model of the working scenario was constructed, with dimensionality reduction in the roadway model achieved through a heading reference influence degree threshold of the tracked roadheaders. Based on the kinematics theory of tracked roadheaders, kinematic and dynamic models for deviation correction in fully mechanized excavation roadways were established. Subsequently, a path planning and tracking correction algorithm was developed, along with a heading deviation correction control algorithm based on fuzzy neural network PID. Online optimization of the particle swarm algorithm was realized through crossover-mutation operations, enabling optimal strategy solving for construction path planning and precise control of travel deviation correction. Finally, simulation experiments evaluating algorithm performance and comparative simulations of control algorithms validated the feasibility and superiority of the proposed method. This research provides strategic guidance and theoretical foundations for rapid precision deployment and intelligent deviation correction control of tracked engineering vehicles in confined underground coal mine spaces. Full article

In space gravitational wave detection, various physical effects in the spacecraft, such as self-gravity, electricity, and magnetism, will introduce undesirable parasitic stiffness. The coupling noise between stiffness and the motion states of the test mass critically affects the performance of scientific detection, making accurate stiffness identification crucial. In response to the question, this paper proposes a method to optimize the test mass motion state for enhancing stiffness identification performance. First, the dynamics of the test mass are studied and a recursive least squares algorithm is applied for the implementation of on-orbit stiffness identification. Then, the motion state of the test mass is parametrically characterized by multi-frequency sinusoidal signals as the variable to be optimized, with the optimization objectives and constraints of stiffness identification defined based on convergence time, convergence accuracy, and engineering requirements. To tackle the dual-objective, computationally expensive nature of the problem, a multigranularity surrogate-assisted evolutionary algorithm with individual progressive constraints (MGSAEA-IPC) is proposed. A fuzzy radial basis function neural network PID (FRBF-PID) controller is also designed to address complex control needs under varying motion states. Numerical simulations demonstrate that the convergence time after optimization is less than 2 min, and the convergence accuracy is less than 1.5 × 10⁻¹⁰ s⁻². This study can provide ideas and design references for subsequent related identification and control missions. Full article

Two-wheeled self-balancing robots (TWSBRs) are underactuated, inherently nonlinear systems that exhibit unstable dynamics. Due to their structural simplicity and rich control challenges, TWSBRs have become a standard platform for validating and benchmarking various control algorithms. This paper presents a comprehensive and structured review of control strategies applied to TWSBRs, encompassing classical linear approaches such as PID and LQR, modern nonlinear methods including sliding mode control (SMC), model predictive control (MPC), and intelligent techniques such as fuzzy logic, neural networks, and reinforcement learning. Additionally, supporting techniques such as state estimation, observer design, and filtering are discussed in the context of their importance to control implementation. The evolution of control theory is analyzed, and a detailed taxonomy is proposed to classify existing works. Notably, a comparative analysis section is included, offering practical guidelines for selecting suitable control strategies based on system complexity, computational resources, and robustness requirements. This review aims to support both academic research and real-world applications by summarizing key methodologies, identifying open challenges, and highlighting promising directions for future development. Full article

Aquaculture has emerged as a sustainable alternative to meet the growing demand for aquatic products while preserving natural ecosystems. This study presents the design, simulation, and experimental validation of an intelligent multivariable control system for aquaculture tanks aimed at cultivating Mugil incilis, a native species of the Colombian Caribbean. The system integrates three control strategies: a classical Proportional-Integral-Derivative (PID) controller, a fuzzy logic–based PID controller, and a neural network predictive controller. All strategies were evaluated in simulation using a third-order transfer function model identified from real pond data. The fuzzy PID controller reduced the mean squared error (MSE) by 66.5% compared to the classical PID and showed faster settling times and lower overshoot. The neural predictive controller, although anticipatory, exhibited high computational cost and instability. Only the fuzzy PID controller was implemented and validated experimentally, demonstrating robust, accurate, and stable regulation of potential hydrogen (pH), dissolved oxygen, and salinity under dynamic environmental conditions. The system operated in real time on embedded hardware powered by a solar kit, confirming its suitability for rural or off-grid aquaculture contexts. This approach provides a viable and scalable solution for advancing intelligent, sustainable aquaculture practices, particularly for sensitive native species in tropical regions. Full article

The deep and ultra-deep oil and gas resources often have the characteristics of high temperature and high pressure, with complex pressure systems and narrow safety density windows, so risks such as gas invasion and overflow are easy to occur during the drilling. In response to the problems of low management efficiency and large gas kick by traditional gas invasion treatment methods, this paper respectively established and compared three intelligent control models for bottom hole pressure (BHP) based on a PID controller, a fuzzy PID controller, and a fuzzy neural network PID controller based on the non-isothermal gas–liquid–solid three-phase transient flow heat transfer model in the annulus. The results show that compared with the PID controller and the fuzzy PID controller, the fuzzy neural network PID controller can adjust the control parameters adaptively and optimize the control rules in real-time; the efficiency of the fuzzy neural network PID controller to deal with a gas kick is improved by 45%, and the gas kick volume in the process of gas kick is reduced by 63.12%. The principal scientific novelty of this study lies in the integration of a fuzzy neural network PID controller with a non-isothermal three-phase flow model, enabling adaptive and robust bottom hole pressure regulation under complex gas invasion conditions, which is of great significance for reducing drilling risks and ensuring safe and efficient drilling. Full article

This study proposes an enhanced derivative control strategy, named $PI$-$D^{\AE }$, designed to overcome key limitations of the derivative ($D$) term, such as noise amplification, derivative kick $(D$-$k)$, and tuning difficulties. These issues often arise in high-frequency or rapidly changing systems, in which traditional PID controllers struggle. The proposed solution introduces a novel adaptive exponent algorithm (Æ) that dynamically modulates the $D$ term based on the evolving relationship between system output and setpoint. This yields the $PI$-$D^{\AE }$ controller, which adapts in real time to changing conditions. The results show significant performance improvements. Simulation results on two systems demonstrate that $PI$-$D^{\AE }$ achieves a 90% faster response time, a 35% reduction in peak time, and a 100% improvement in settling time compared with conventional PID controllers, all while maintaining a near-zero steady-state error even under external disturbances. Unlike more-complex alternatives such as fuzzy logic, neural networks, or sliding mode control, $PI$-$D^{\AE }$ retains the simplicity and robustness of PID, avoiding high computational costs or intricate setups. This adaptive exponent strategy offers a practical and scalable enhancement to classical PID, improving performance and robustness without added complexity, and thus provides a promising control solution for real-world applications in which simplicity, adaptability, and reliability are essential. Full article

Precise temperature control in high-temperature furnaces is challenged by nonlinearities, parameter drift, and high thermal inertia. This study proposes an adaptive control strategy combining a classical PID loop with real-time gain updates from a feed-forward artificial neural network (ANN). Implemented on an 18 kW retrofitted Blue-M furnace, the system was characterized by second-order transfer functions for heating and forced convection cooling. A dataset of 9702 samples was built from eight fixed PID configurations tested under a multi-ramp thermal profile. The selected 3-64-64-32-2 ANN, executed in Python and interfaced with LabVIEW, computes optimal gains in 0.054 ms while preserving real-time monitoring capabilities. Experimental results show that the ANN-assisted PID reduces the mean absolute error to 5.08 °C, limits overshoot to 41% (from 53%), and shortens settling time by 20% compared to the best fixed-gain loop. It also outperforms a fuzzy controller and remains stable under ±5% signal noise. Notably, gain reversals during cooling prevent temperature spikes, improving transient response. Relying on commodity hardware and open-source tools, this approach offers a cost-effective solution for legacy furnace upgrades and provides a replicable model for adaptive control in high-temperature, safety-critical environments like metal processing, battery cycling, and nuclear systems. Full article

Controlling systems with highly nonlinear or uncertain dynamics present significant challenges, particularly when using conventional Proportional–Integral–Derivative (PID) controllers, as they can be difficult to tune. While PID controllers can be adapted for such systems using advanced tuning methods, they often struggle with lag and instability due to their integral action. In contrast, fuzzy Proportional–Derivative (PD) controllers offer a more responsive alternative by eliminating reliance on error accumulation and enabling rule-based adaptability. However, their industrial adoption remains limited due to challenges in manual rule design. To overcome this limitation, Fuzzy Neural Networks (FNNs) integrate neural networks with fuzzy logic, enabling self-learning and reducing reliance on manually crafted rules. However, most fuzzy neural network PD (FNNPD) controllers rely on mean square error (MSE)-based training, which can be inefficient and unstable in complex, dynamic systems. To address these challenges, this paper presents a Soft Fuzzy Reinforcement Neural Network PD (SFPD) controller, integrating the Soft Actor–Critic (SAC) framework into FNNPD control to improve training speed and stability. While the actor–critic framework is widely used in reinforcement learning, its application to FNNPD controllers has been unexplored. The proposed controller leverages reinforcement learning to autonomously adjust parameters, eliminating the need for manual tuning. Additionally, entropy-regularized stochastic exploration enhances learning efficiency. It can operate with or without expert knowledge, leveraging neural network-driven adaptation. While expert input is not required, its inclusion accelerates convergence and improves initial performance. Experimental results show that the proposed SFPD controller achieves fast learning, superior control performance, and strong robustness to noise, making it effective for complex control tasks. Full article