Intelligent Control Approach Research Articles

A multi-agent reinforcement learning based approach for intelligent traffic signal control

A multi-agent reinforcement learning based approach for intelligent traffic signal control

Control of Thermal Uniformity in Microwave Heating Process by BPNN and Adaptive Particle Swarm Optimization

As China advances its green economy, innovative methods are being employed to enhance energy optimization and conservation within energy-intensive industries. Among these methods, microwave heating stands out due to its superior heating efficiency and lack of pollution. Nonetheless, uniform heating remains a challenge because the microwave absorption capacity of the heated medium varies with changes in heating time and temperature. To address this issue, an Adaptive Particle Swarm Optimization (APSO) neural network microwave system based on the Back Propagation Neural Network (BPNN) is proposed. This system leverages the fundamental principles of Particle Swarm Optimization (PSO), categorizing particle swarms into three types and iterating them through distinct processes to achieve optimal results. An APSO controller is designed based on system identification, adjusting the controller parameters according to the error between the real-time system output and the identification model. The feedback error is used as the fitness function in the PSO algorithm, continuously adjusting the weights and thresholds of the neural network. This intelligent control approach optimizes the microwave oven's input power to minimize the error between the actual temperature output and the identified temperature. The APSO controller is designed based on system identification, with the intelligently controlled microwave heating system adjusting its input power to minimize the error between the identified and actual temperature outputs. Unlike traditional Proportional Integral Differential (PID) and BPNN controllers, this approach calculates the output of the identified model and the error of the actual model, feeding this information back to the controller. The feedback error serves as the fitness function in the PSO algorithm, enabling continuous adjustment of the network's weights and thresholds to regulate the microwave equipment's output power, thereby ensuring the output temperature closely follows the preset temperature curve. Experimental results demonstrate that the actual output value of the system closely aligns with the original preset temperature curve, with a root mean square error of only 0.74. This designed identification and control system effectively achieves the desired outcome.

Robust and intelligent power control for three-bladed horizontal axis wind turbine system using a real wind profile of the northern Morocco

In this work, a robust and intelligent control approach is developed for three-bladed horizontal axis variable-speed wind turbine (VSWT) operating under real climatic conditions (whether the WT is onshore or offshore). Below the rated power, the main purpose of the controller is to optimize the extraction of energy from wind and ensure the full exploitation of the installation, all while minimizing mechanical stress in the system in the presence of uncertainties and external disturbances. In order to achieve optimized wind energy capture without chattering problems or oscillatory phenomena, this study proposes combining the Integral Sliding Mode Control (ISMC) with an artificial neural network technique such as the Radial Basis Function (RBF) to enhance controller performances. Additionally, the RBF neural network (RBFNN) approach is implemented to estimate the wind turbine (WT) model uncertain part, allowing the use of a lower switching gain to achieve faster convergence and avoid steady-state error. Thus, by means of Lyapunov theory, the stability of the system with this controller can be demonstrated. Simulation results of the proposed Neural Network Integral Sliding Mode Control (NN-ISMC) controller are compared with the usual SMC and the standard ISMC. The comparative analysis indicate a superior efficiency of the developed control strategy in terms of decreasing tracking error and eliminating chattering behavior. Moreover, this approach provides a faster transient system response and higher robustness in presence of uncertainties compared to the other controllers.

A new industrially magnetic capsule MedRobot integrated with smart motion controller

Capsule medical robots with unique application advantages have broad prospects for gastrointestinal detection, targeted drug delivery, medical assistance, and other fields. However, due to the complexity of the gastrointestinal environment and the limitations of the space magnetic field, robust motion control of capsule robots is still a challenge. Using a permanent magnet (NdFeB) as a space magnetic source, a capsule robot manipulation instrument with an integrated motion intelligent control approach is proposed in this work. First of all, a steering fixture controlling a permanent magnet is designed, and a manipulation instrument capable of five degrees of motion freedom is built with low cost and high accuracy. Furthermore, an integrated motion control approach, consisting of a primary motion subsystem and an auxiliary motion subsystem, is presented, where the motion route of the capsule robot is planned. A parallel optimization strategy is adopted to improve the traditional Gray Wolf Optimization Algorithm, the improved version of which is utilized to tune the parameters of controllers. Finally, some experiments of the designed capsule robot are carried out in different planes and slopes, as well as simulated stomach and real pig stomach, respectively. The results show that the motion characteristics are continuous and stable, and the average position error is 4.2 mm, which meets the application requirements.

Machine Learning-Based Control of Autonomous Vehicles for Solar Panel Cleaning Systems in Agricultural Solar Farms

This paper presents a machine learning (ML)-based approach for the intelligent control of Autonomous Vehicles (AVs) utilized in solar panel cleaning systems, aiming to mitigate challenges arising from uncertainties, disturbances, and dynamic environments. Solar panels, predominantly situated in dedicated lands for solar energy production (e.g., agricultural solar farms), are susceptible to dust and debris accumulation, leading to diminished energy absorption. Instead of labor-intensive manual cleaning, robotic cleaners offer a viable solution. AVs equipped to transport and precisely position these cleaning robots are indispensable for the efficient navigation among solar panel arrays. However, environmental obstacles (e.g., rough terrain), variations in solar panel installation (e.g., height disparities, different angles), and uncertainties (e.g., AV and environmental modeling) may degrade the performance of traditional controllers. In this study, a biologically inspired method based on Brain Emotional Learning (BEL) is developed to tackle the aforementioned challenges. The developed controller is implemented numerically using MATLAB-SIMULINK. The paper concludes with a comparative analysis of the AVs’ performance using both PID and developed controllers across various scenarios, highlighting the efficacy and advantages of the intelligent control approach for AVs deployed in solar panel cleaning systems within agricultural solar farms. Simulation results demonstrate the superior performance of the ML-based controller, showcasing significant improvements over the PID controller.

Integrating Model Predictive Control With Stormwater System Design: A Cost‐Effective Method of Urban Flood Risk Mitigation During Heavy Rainfall

AbstractThe integration of green‐gray infrastructures with advanced control approaches is revolutionizing the stormwater system retrofitting, emerging as an innovative strategy to mitigate urban flood risks. However, a major challenge lies in balancing the substantial investments of these infrastructure projects with their environmental benefits, such as reduced flooding volume and lower peak flow. Model predictive control (MPC), a dynamic and intelligent control approach, optimizes these environmental benefits but is underutilized in the system design phase for cost‐effectiveness analysis. This study introduces a multi‐scenario model framework that incorporates MPC and other control approaches into stormwater system designs, including the implementation of controlled storage tanks and green infrastructures. This framework provides comprehensive modeling tools for practitioners to evaluate the flood control benefits and costs across various infrastructure designs and control scenarios, ultimately identifying solutions that are both environmentally and economically viable. A case study conducted in a small urban catchment area in Shenzhen City, China, demonstrates the effectiveness of this framework. The results indicate that MPC outperforms other control scenarios, particularly under heavy or extreme rainfall conditions. Notably, MPC not only provides superior environmental benefits but also yields considerable cost savings, ranging from 1,787 to 9,371 USD per hectare compared to static control, equating to a 5% reduction in cost relative to rule‐based control. Such findings suggest that integrating MPC is a cost‐effective alternative to extensive infrastructure expansion for flood management, which significantly enhances the benefit contribution of controlled infrastructures without substantial additional expenses.

A review of automotive intelligent and adaptive headlight beams intensity control approaches

The automotive headlight stands out as a critical vehicle component, particularly emphasized during nighttime driving. The high beam, designed for optimal driver visibility on long-distance roads, traditionally relies on manual control by the driver. However, this manual control poses challenges, particularly when the high beam light temporarily blinds oncoming drivers. The resultant dazzle for drivers of opposing vehicles is a significant concern. In response to these issues, there is a growing demand for adaptive and intelligent headlights that can autonomously adjust beam intensity. The intelligent headlight system takes on the responsibility of modifying the beam intensities without requiring explicit input from the drivers. This study aims to systematically review various approaches to controlling intelligent headlight beam intensity. The paper identifies four prominent approaches to intelligent headlight beam intensity control, recognized as widely used techniques. Furthermore, the study uncovers intriguing connections between some of these intensity control approaches. A survey on utilization rates indicates that sensor-based and machine learning (ML)-based intensity control approaches are the most commonly employed methods by automotive headlight designers. The paper concludes by providing insights into the future prospects of intelligent headlight technology, offering guidance for future researchers in this field.

A digital twin framework for innovating rural ecological landscape control

BackgroundBecause cities prioritize economic development and face ecological space and resource constraints, the development of rural areas, which have untapped potential, should receive increased attention. Consequently, rural ecological landscapes should be constructed through the control of land use types and quality to fully ensure the sustainable development of urban and rural ecosystems. The digital twin is a philosophy and a methodology that connects the digital and physical realms, facilitating realistic and dynamic mapping simulations of the real world. This capability offers valuable insights for digital decision-making, maintenance, and optimization of rural ecological landscapes. Given the digital transformation of rural ecological landscape control, this paper proposes a rural intelligent control approach based on the digital twin concept and new technology.MethodsFive components of the rural ecological landscape digital twin framework are selected to collectively facilitate the monitoring and analysis of rural conditions, formulate strategic solutions, implement management and control behaviors, and enhance participant interaction. The method includes three steps: mapping and fusing information, constructing and managing a database, and constructing a digital platform. Data mining and spatial fusion are performed through targeted mapping methods, and Oracle and ArcGIS SDE are utilized for database construction and fused data management. The twin platform is generated via HTML, desktop application development and geographic information system development technologies using a distributed system as the core.ResultsBased on multiple case studies, our platform efficiently gathers system information on rural ecological landscapes using a twin model. Through evaluation and analysis, it determines landscape governance zones, adjusting them based on land use conditions. The platform refines control schemes with feedback from diverse users, ensuring effective control in various scenarios. Its key advantages include high development efficiency, flexible access, and smooth cross-platform integration. Although implemented in rural China, the proposed digital twin framework is applicable to any rural area requiring ecological landscape digital control.ConclusionsThe value of the platform lies in its powerful information processing capability, overcoming the limitations of time and space and enabling the presentation and integration of fictional scenes Thus, the platform provides a reference for the digital transformation of rural ecological landscape control.

Environment‐aware intelligent numerology control approach for 5G and beyond systems

Summary5G and beyond (5GB) systems show a flexible characteristic to meet different user communications requirements in parallel. Multi‐numerology waveform design is an important part of this human centric characteristic, however, it is necessary to make multi‐numerology planning. In this paper, the integrated sensing and communications (ISAC) concept is used with machine learning (ML) techniques to develop an intelligent numerology control mechanism for 5GB. Thanks to the ISAC capabilities, an environment‐aware system is designed, and several useful sensing information (USI) that affect the wireless channel characteristic is utilized. It is assumed that the USI is obtained through 5GB smart city networks. At that point, synthetic data generation is performed to form a new dataset with USI‐based features. Additionally, a novel feature control algorithm is proposed in the paper. Lastly, simulation results are presented for different type of ML models including several ensemble methods.

A novel intelligent control approach for wind energy conversion systems with synchronous reluctance generators

SummaryThis study addresses a significant research gap in the field of wind energy, focusing on the underutilized potential of synchronous reluctance generators (SynRG) in wind power conversion systems (WPCS). Despite notable advancements in wind energy controls, the capabilities of SynRG within these systems have not been fully explored. In response, we introduce an innovative model‐free control (MFC) approach, termed intelligent proportional (iP), specifically designed and optimized for WPCS equipped with SynRG. This research encompasses the modeling, control, and simulation of SynRG‐based WPCS, aiming to optimize system performance and enhance the quality of grid power supply. The proposed vector control (VC) method utilizes a MFC approach (VC‐iP) to address the limitations inherent in traditional VC methods with proportional‐integral (PI) controllers (VC‐PI). Although widely used for their simplicity, VC‐PI methods often lead to power fluctuations and decreased power and current quality, consequently reducing overall system efficiency. In contrast, VC‐iP markedly enhances power quality and system efficiency, especially in situations involving fluctuating power demand and variable wind speeds. The efficacy of VC‐iP is demonstrated through simulations in two distinct test scenarios that include variations in power demand and random wind speed fluctuations. The results affirm the superiority of VC‐iP in maintaining stable, high‐quality power output from WPCS. This method notably minimizes fluctuations, improves current quality, reduces harmonic distortion and steady‐state error, ensures reliable grid integration, and contributes to a more efficient and sustainable energy system.

Frequency Regulation of Microgrid with Renewable Sources Using Intelligent Adaptive Virtual Inertia Control Approach

Frequency control of small inertia microgrids (MGs) with amalgamation of renewable energies like wind and solar is a difficult job. Virtual inertia control (VIC) scheme is the concept of enhancing the MGs inertia employing storage elements. In the present study, an optimized fuzzy adaptive virtual inertia control strategy has been proposed for adaptive allocation of virtual inertia constant. To tune the suggested controllers, an improved golden jackal optimization (i-GJO) is projected in which priority is given to the better jackal’s during the search process, and also sine cosine adopted scaling factor is used to modify the jackal’s position throughout search process. The effectiveness of i-GJO method is evaluated by comparing the results with golden jackal optimization (GJO) and other similar recently proposed optimization algorithms. Then, proportional-integral-derivative controllers are tuned in VIC environment with fixed virtual inertia constant (KVT ) using proposed i-GJO technique, and the superiority of i-GJO over genetic algorithm, particle swarm optimization, and GJO is demonstrated. Finally, fuzzy based controller with constant KVT as well as fuzzy adaptive KVT has been designed using the proposed i-GJO technique. The sensitivity of the controller is also examined under irregular load changes and various rates of RES amalgamation.

PV fed modular multilevel inverter with PI and fuzzy control for marine water pumping applications

In this paper, we describe the outcomes of our investigation into the design and construction of a modular multilevel inverter (MMI) using intelligent control approaches to manage the induction motor (IM) drive in marine water pumping applications. Specifically, we were interested in controlling the speed of the IM. The multi-model input multi-output (MMI-IM) drive coupled with a fuzzy logic controller is used in the solution that has been suggested. A range of robustness measurements, including as peak overshoot, inverter settling time, and total harmonic distortion (THD), are used to assess the performance of the controllers. The intelligent controller, IM drive, and MMI are all components of the suggested control approach for water pumping applications that are used in the marine industry. To enhance both the efficiency and usefulness of the system, the methodology that has been suggested makes use of several intelligent control strategies. This is the aspect that sets it apart from other methods.

Enhancing heat transfer from a circular cylinder undergoing vortex induced vibration based on reinforcement learning

Machine learning approaches have been proven to be effective and efficient in mediating flow dynamics so as to realize drag reduction, vibration suppression, etc. In this article, an intelligent active flow control approach is proposed to enhance heat transfer of a heated circular cylinder at Re=100 undergoing vortex induced vibration in the lock-in condition. A high-fidelity solver based on the generalized interpolation-supplemented lattice Boltzmann method (GILBM) with moving reference frame (MRF) is used to establish the real-time numerical environment with structural motion, momentum transport, and heat transport. A reinforcement learning (RL) agent realized by proximal policy optimization (PPO) is adopted to establish the model-free controller, which receives sensorymotor cues as feedback signals and determines the real-time velocity issued from the jet as actuation. Through a group of trainings, each of whom involves 200 independent cases, the RL agent eventually learns an effective and efficient control strategy that improves the average heat transfer rate by 76.7%, quantified by the dimensionless Nusselt number. Through analyzing the spatiotemporal variations of vorticity and temperature fields, local Nusselt number and pressure coefficient along the cylinder surface, as well as the statistical flow quantities, it is proved that that the enhanced heat transfer is contributed by both the mediated vortex formation and shedding in vicinity of the blowing jet, as well as the augmented vibration. Extra tests also confirm that the synthetic jets play a much more significant role than the augmented vibration. Furthermore, the success of the control strategy is attributed mainly by the phase lag between the maximum vibration displacement and the moment when the synthetic jets oppose their directions. Moreover, the more realistic but more complicated case with the non-Oberbeck-Boussinesq (NOB) approximation is also tested, confirming good self-adaptivity of the learnt strategy. Through this study, it is verified that the RL to be a powerful tool for mediating flows involving complicated momentum and heat transport.

GBNN algorithm enhanced by movement planner for UV‐C disinfection

AbstractIn order to maintain adequate levels of cleanliness and sanitation in public facilities, prevent the buildup of viruses and other harmful pathogens, and ensure health and safety, health and labor authorities have repeatedly warned of the need to adhere to proper disinfection protocols in the workplace. This is particularly important in public places where food is handled, where there are more vulnerable people, including hospitals and health care centers, or where there is a large concentration of people. One promising approach is the combination of ultraviolet‐C (UV‐C) light and mobile robots to automate disinfection processes. Being this technology effective for disinfection, an excessive dose of UV can damage the materials, limiting its applicability. Therefore, a major challenge for automatic disinfection is to find a route that covers the entire surface, ensures cleanliness, and provides the correct radiation dose while preventing environmental materials from being damaged. To achieve this, in this paper a novel intelligent control approach is proposed. A bio‐inspired Glasius neural network with a motion planner, an UV estimation module, a speed regulator, and pure pursuit controller are combined into one intelligent system. The motion planner proposes a sequence of movements to go through the space in the most efficient way possible, avoiding obstacles of the environment. The speed controller adjusts the dose of UV‐C radiation and the pure pursuit regulator ensures the following of the path. This approach has been tested in various simulation scenarios of increasing complexity and in four different areas of dosing requirements. In simulation, a 44% reduction of the maximum dose is achieved, 17% less distance travelled by the robot and, what is more important, 229% more locations with the appropriate dose.

Trajectory control of a hydraulic system using intelligent control approach based on adaptive prediction model

Electro-hydraulic actuators (EHAs) have become a preferred alternative to traditional hydraulic actuators with valve control systems due to their numerous advantages, making them an ideal choice for applications requiring high-precision force or position control. However, the highly complex nonlinear nature of EHAs makes modelling and controlling them a challenging task. To address this challenge, a new position control approach has been proposed for an EHA system using a combination of a feedforward online-tuning PID (FOPID) controller and an adaptive grey predictor (AGP), known as the feedforward online-tuning adaptive grey predictor (FOAGP). The FOPID controller is constructed based on PID controller and fuzzy logic algorithm to control the EHA system towards referred trajectory, while an updating rule that consists of robust checking terms optimizes its parameters online to minimize control error. The AGP predictor is an important aspect of the proposed approach. It consists of a self-tuning step size mechanism, which estimates the performance of the plant to tune the parameters of the controller and create an additive control signal that is used to counteract environment noises and perturbations. This approach significantly improves control performance by reducing the effect of disturbances and sensor noises on the system. The FOAGP approach was tested in simulation to investigate its effectiveness. The results showed that the proposed approach outperformed other existing control methods, with a higher accuracy and better control performance. One of the significant advantages of the FOAGP approach is its ability to learn and adapt to changing system dynamics. The learning mechanism used in the FOPID controller allows the system to optimize its parameters online, which is especially useful in systems with varying operating conditions. The AGP predictor also continuously adjusts its parameters to accurately estimate the system output, making it an effective tool for controlling EHAs. The proposed approach offers a significant improvement in control performance, making it a better alternative to traditional control methods. This approach can be applied to various EHA systems, including those used in aerospace, automobile, and robotic applications, among others.

A novel learning-based data-driven [formula omitted] control strategy for vanadium redox flow battery in DC microgrids

Vanadium redox flow battery (VRB) is one of the most promising batteries at present. In order to enhance the stability and anti-interference ability of VRB in microgrids, a novel learning-based data-driven H∞ control approach is proposed for the VRB, which uses a new integral reinforcement learning algorithm to produce excellent steady-state and dynamic responses only by measurements. Compared to the model-based control methods, it is insensitive to model parameter variations. Furthermore, compared to most of the existing artificial intelligent control approaches that require large amounts of experimental data for offline neural network (NN) training, the proposed control strategy contributes to eliminate the offline training process and therefore, does not need the costly and tedious training data acquisition process. More importantly, the proposed control offers guaranteed closed-loop control stability, which cannot be achieved by nearly all the control methods that purely rely on the offline trained NNs. In this paper, the rigorous proof of stability is given, and the validity of the proposed method is verified by simulation results.

Federated Discrete Reinforcement Learning for Automatic Guided Vehicle Control

Under the federated learning paradigm, the agents learn in parallel and combine their knowledge to build a global knowledge model. This new machine learning strategy increases privacy and reduces communication costs, some benefits that can be very useful for industry applications deployed in the edge. Automatic Guided Vehicles (AGVs) can take advantage of this approach since they can be considered intelligent agents, operate in fleets, and are normally managed by a central system that can run in the edge and handles the knowledge of each of them to obtain a global emerging behavioral model. Furthermore, this idea can be combined with the concept of reinforcement learning (RL). This way, the AGVs can interact with the system to learn according to the policy implemented by the RL algorithm in order to follow specified routes, and send their findings to the main system. The centralized system collects this information in a group policy to turn it over to the AGVs. In this work, a novel Federated Discrete Reinforcement Learning (FDRL) approach is implemented to control the trajectories of a fleet of AGVs. Each industrial AGV runs the modules that correspond to an RL system: a state estimator, a rewards calculator, an action selector, and a policy update algorithm. AGVs share their policy variation with the federated server, which combines them into a group policy with a learning aggregation function. To validate the proposal, simulation results of the FDRL control for five hybrid tricycle-differential AGVs and four different trajectories (ellipse, lemniscate, octagon, and a closed 16-polyline) have been obtained and compared with a Proportional Integral Derivative (PID) controller optimized with genetic algorithms. The intelligent control approach shows an average improvement of 78% in mean absolute error, 75% in root mean square error, and 73% in terms of standard deviation. It has been shown that this approach also accelerates the learning up to a 50 % depending on the trajectory, with an average of 36% speed up while allowing precise tracking. The suggested federated-learning based technique outperforms an optimized fuzzy logic controller (FLC) for all of the measured trajectories as well. In addition, different learning aggregation functions have been proposed and evaluated. The influence of the number of vehicles (from 2 to 10) on the path following performance and on network transmission has been analyzed too.

Learn-to-supervise: Causal reinforcement learning for high-level control in industrial processes

Possessing efficient supervisory control systems is crucial for maintaining the desired operational performance of complex industrial processes. Several challenges face the developers of these systems, such as requiring accurate physical models, dealing with the variability and uncertainty of process operating conditions and coordinating between local controllers to reach desired global performance. This paper proposes an intelligent supervisory control approach based on causal reinforcement learning (CRL) to effectively manipulate the controllers’ setpoints of the process in a way that optimizes its key performance indicators (KPIs), thereby improving the energy efficiency of the process. The approach adopts deep reinforcement learning (DRL) to develop an efficient control policy through interaction with a process simulation. The DRL training history is then exploited using interpretable machine learning and process mining to build a discrete event system (DES) model, in the form of a state-event graph. The DES model identifies causal relationships between events and provides interpretability to the control policy developed by the DRL method. The DES discovered is exploited as a Markov decision process to apply the Q-learning algorithm as a CRL supervisor. The supervisor incorporates causal knowledge into its training process, thus improving the DRL control policy developed and identifying the event paths that optimize the process’s KPIs. The proposed approach is validated using two heat recovery systems in a pulp & paper mill. It successfully achieves a control policy that reduces energy consumption by up to 15.6% for the first system and 5.02% for the second, compared to the expert’s baseline methods.

Intelligent Secondary Control of Islanded AC Microgrids: A Brain Emotional Learning-Based Approach

This paper proposes a distributed intelligent secondary control (SC) approach based on brain emotional learning-based intelligent controller (BELBIC) for power electronic-based ac microgrid (MG). The BELBIC controller is able to learn quick-auto and handle model complexity, non-linearity, and uncertainty of the MG. The proposed controller is fully model-free, indicating that the voltage amplitude and frequency deviations are regulated without previous knowledge of the system model and parameters. This approach ensures low steady-state variations with higher bandwidth and maintains accurate power-sharing of the droop mechanism. Furthermore, primary control is realized with a robust finite control set-model predictive control (FCS-MPC) in the inner level to increase the system frequency bandwidth and a droop control in the outer level to regulate the power-sharing among the distributed generations. Finally, experimental tests obtained from a hardware-in-the-loop testbed validate the proposed control strategy for different cases.

Designing a Ship Autopilot System for Operation in a Disturbed Environment Using the Adaptive Neural Fuzzy Inference System

Efficient ship guidance, fuel savings, and reduced human control have long been a key focus in developing intelligent controllers. The integration of neural networks and fuzzy logic control offers numerous advantages, creating a robust and adaptive system capable of handling complex dynamics and uncertainties. This intelligent control system learns from its environment and adjusts behavior, making it effective in challenging situations. Additionally, it improves system efficiency, reduces energy consumption, and minimizes human intervention, enhancing safety and reducing errors. This study presents an intelligent control approach, titled “Designing a Ship Autopilot System for Operation in a Disturbed Environment using the Adaptive Neural Fuzzy Inference System”, combining a neural network and fuzzy logic control to steer ships. A 6DOF dynamic model is constructed, simulating ship operations with noise signals. The ANFIS controller comprises six layers, with a distinct composition rule expressing conclusions as linear equations of input variables. Layer 1 has two input signals, layer 2 represents fuzzy rules with six nodes, and layers 3, 4, and 5 contain nine nodes each. Layer 6 combines output signals from layer 5, following the first-order Takagi–Sugeno fuzzy logic control model. Simulation results using MATLAB/Simulink demonstrate the superiority of the ANFIS controller over the PID controller, significantly improving stability and trajectory accuracy.