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The success of machining process automation hinges primarily on the effectiveness of the monitoring and adaptive control systems. A new digital twin–based process monitoring method and system in small batch machining is presented, and the cutting model is integrated into the monitoring method to improve the diagnosis accuracy. Model-based and signal-based monitoring indicators are developed, and indicators of the residual force component for the tool wear monitoring and energy ratio for the chatter detection are introduced to the digital twin–based monitoring. The critical monitoring algorithm is verified in two cases: tool wear monitoring and chatter detection. The results show that the method proposed can accurately evaluate the percentages of the residual useful life of the tool and the chatter. Moreover, a new process monitoring system in small batch machining is developed by integrating the advanced algorithm. This study can provide a critical reference for intelligent machining monitoring and control in industry. [ABSTRACT FROM AUTHOR]

Gas metal arc welding (GMAW) integrated wire arc additive manufacturing (WAAM) depicts non-uniform layers, spatter, thermal distortions, metal overflow, and mechanical anisotropy. The prime cause for these issues is improper thermal energy management due to the current controlled wire feeding mechanism. Here, an autonomous wire feed system (AWFS) has been designed and integrated into the GMAW-WAAM to eliminate such issues. It fine-tunes the wire feed speed (WFS) and maintains a steady flow of arc current. With this developed system, initially, twenty single beads are deposited using ER70S-6 feedstock under different conditions of welding voltage (U), travel speed (TS), and WFS. Later on, an optimum deposit condition has been formulated using response surface methodology-RSM (U ≈ 21.7 V, TS ≈ 8.6%, and WFS ≈ 2.7 m/min). Under this deposit condition, thin-layered parts are manufactured, where inter-layer dwell time (IDT) is only varied (from 2 to 4 min) to illustrate its significance over metallographic and mechanical performances. It is observed that with increasing IDTs, the morphological attributes of the deposit are improved (straight side wall with low surface waviness) with a reduction in grain size that further boosts the hardness and mechanical performances (increased strength and wear resistance). The occurrence of compressive residual stress could additionally support the enhancements in mechanical strength. In addition to the improvements in mechanical performances, the anisotropy in mechanical strength was also reduced (< 5%). Moreover, bulk texture analysis ensures similar fiber texture evolutions along different deposit sections with a least variation in the texture intensity, which point towards isotropicity in the as-fabricated part. [ABSTRACT FROM AUTHOR]

Continuum robots have great application value and broad prospects in various fields due to their dexterity and compliance. To fully exploit their advantages, it is crucial to develop an effective, accurate and robust control system for them. However, research on continuum robot control is still in its infancy and there are many problems remaining unsolved in this field. In particular, this paper focuses on the task-space behavior and the generic control of heterogeneous continuum robots. First, a controller is proposed to achieve the kinematic motion control and visual servoing of continuum robots with predefined task-space behavior. The predefined behavior is twofold: prescribed task-space error and predefined convergence time. Then, the proposed controller is integrated with a velocity-level kinematic mapping estimator to obtain a model-free control system, which is applicable to heterogeneous continuum robots. Furthermore, a re-adjustable performance function is proposed to ensure the effectiveness and robustness of the proposed control system in the presence of external disturbance. Finally, extensive simulations and experiments are performed based on heterogeneous continuum robots, including the cable-driven continuum robot, the parallel continuum robot, the concentric-tube robot, the flexible endoscope, and the pneumatic continuum robot. Our results demonstrate that the task-space error of heterogeneous continuum robots complies with the prescribed boundaries and converges to steady state in predefined time, which reveals the efficacy of the proposed control method. [ABSTRACT FROM AUTHOR]

This study proposes an adaptive neural command-filtered backstepping control system for precise and fast attitude control of a satellite considering different uncertain dynamics. Mission success crucially depends on robust attitude control resilient to model uncertainties, unmodeled dynamics, external disturbances, and actuator faults. The proposed approach synergistically combines a neural network for handling model uncertainties, unmodeled dynamics, and actuator faults, with a disturbance observer for compensating external disturbances and neural network estimation errors. The command-filtered backstepping technique avoids the explosion of complexity inherent to traditional backstepping, while integrating integral action into the design results in the elimination of the steady-state tracking error. Besides, a composite learning method optimizes the update laws for the neural network and disturbance observer weights, enhancing control performance. Despite the presence of uncertainties, the closed-loop system stability is guaranteed by the Lyapunov stability theorem. Simulation results demonstrate the proposed controller's ability to handle severe actuator faults, unmodeled dynamics, and measurement noise without requiring explicit fault detection and isolation schemes. [ABSTRACT FROM AUTHOR]

Research on initial errors and constraint restrictions is one of the main research directions in the field of control of uncertain robotic systems. An adaptive iterative learning control (AILC) method based on radial basis function (RBF) neural network is proposed to address the trajectory tracking problem of the long-stroke hybrid robot system with random initial errors and full state constraints. The RBF neural network is used to approximate the unknown nonlinear terms, and the network weights are updated using an iterative learning law that incorporates a projection mechanism. Additionally, a robust learning strategy is used to compensate for both the approximation error of the neural network and the external disturbances that vary with each iteration. To relax the requirement of traditional iterative learning control (ILC) for identical initial condition, an equivalent error function is constructed based on the time-varying boundary layer. The tangent-type barrier Lyapunov function (BLF) is designed to ensure that the joint position and speed of the robot system are bounded within a predetermined range. Through stability analysis based on barrier composite energy function (BCEF), it can be proved that the boundedness of all signals in the closed-loop system and the tracking error of the robot system will converge to an adjustable residual set asymptotically. Finally, through simulation experiments conducted on the MATLAB platform, the results demonstrate that the method overcomes the random initial errors of the system effectively, ensures that the system satisfies the full-state constraints, and realizes high-precision trajectory tracking. [ABSTRACT FROM AUTHOR]

Global, 24/7, and all-weather Synthetic Aperture Radars (SARs) are optimal platforms for Ground Moving Target Indication (GMTI) missions, and the tether constraint provides a stable mechanic connection for such configurations. To fulfill the requirements of such missions, the Space Tether System (STS) must be deployed to horizontal positions to form the necessary along-track interference baseline, which is unstable relative to traditional vertical positions and has not received adequate focus. To deal with this problem, this study focuses on the deployment control of the STS to the unstable horizontal positions. Firstly, the properties of the STS at the horizontal position are analyzed, and a synthetic criterion of measurement error is defined based on the observation principle of the GMTI mission. Secondly, two deployment control strategies are proposed, and corresponding desired trajectories are generated by considering two occasions respectively. In the end, considering the instability of horizontal positions, an adaptive closed-loop controller is designed utilizing the backstepping method to address gravitational moment and other disturbances. Simulations demonstrate that the system can successfully attain the desired horizontal positions under both deployment strategies, and the designed controller can quickly track trajectories under initial state errors and external disturbances. • Two along-track deployment strategies are proposed for unstable horizontal positions. • A synthetic criterion of measurement error is defined to evaluate deployment accuracy. • A back-stepping adaptive controller is designed to suppress unstable tether oscillations. [ABSTRACT FROM AUTHOR]

Measurement error frequently distorts outcomes in real-world applications, affecting process results. This paper explores how measurement error affects the detection capabilities of a new adaptive control chart with variable sample size in identifying out-ofcontrol conditions. The analysis is based on a model incorporating linear covariates, focusing specifically on the variable sample size with the EWMA chart's ability to detect shifts in the mean. Our findings demonstrate that measurement error considerably influences the chart's performance concerning mean detection. Additionally, we show that the proposed variable sample size strategy outperforms traditional methods under these conditions. [ABSTRACT FROM AUTHOR]

Background and Objectives: The two-axis inertially stabilized platforms (ISPs) face various challenges such as system nonlinearity, parameter fluctuations, and disturbances which makes the design process more complex. To address these challenges effectively, the main objective of this paper is to realize the stabilization of ISPs by presenting a new robust model-free control scheme. Methods: In this study, a robust adaptive fuzzy control approach is proposed for two-axis ISPs. The proposed approach leverages the backstepping method as its foundational design mechanism, employing fuzzy systems to approximate unknown terms within the control framework. Furthermore, the control architecture incorporates a model-free disturbance observer, enhancing the system's robustness and performance. Additionally, novel adaptive rules are devised, and the uniform ultimate boundedness stability of the closed-loop system is rigorously validated using the Lyapunov theorem. Results: Using MATLAB/Simulink software, simulation results are obtained for the proposed control system and its performance is assessed in comparison with related research works across two scenarios. In the first scenario, where both the desired and initial attitude angles are set to zero, the proposed method demonstrates a substantial mean squared error (MSE) reduction: 96.2% for pitch and 86.7% for yaw compared to the backstepping method, and reductions of 75% for pitch and 33.3% for yaw compared to the backstepping sliding mode control. In the second scenario, which involves a 10-degree step input, similar improvements are observed alongside superior performance in terms of reduced overshoot and settling time. Specifically, the proposed method achieves a settling time for the pitch gimbal 56.6% faster than the backstepping method and 58% faster for the yaw gimbal. Moreover, the overshoot for the pitch angle is reduced by 53.5% compared to backstepping and 35.5% compared to backstepping sliding mode control, while for the yaw angle, reductions of 43.6% and 37.6% are achieved, respectively. Conclusion: Through comprehensive simulation studies, the efficacy of the proposed algorithm is demonstrated, showcasing its superior performance compared to conventional control methods. Specifically, the proposed method exhibits notable improvements in reducing maximum deviation from desired angles, mean squared errors, settling time, and overshoot, outperforming both backstepping and backstepping sliding mode control methods. [ABSTRACT FROM AUTHOR]

With intelligent manufacturing development, applying adaptive control technology in the machining process is an effective way to increase productivity and quality. However, adaptive control alone cannot control cutting forces effectively when cutting conditions have excessive change. In this study, a digital twin of the milling process is introduced to cutting force adaptive control for system robustness and efficiency. The cutting force is indirectly measured based on the feed drive current using a Kalman filter, and unknown parameters in the estimation model are identified. A virtual machining system model is established based on online data communication and geometric operation. In addition, the machining state is predicted and introduced into the adaptive control algorithm based on the integrated digital twin for cutting force constraint control. Finally, rough milling of an S-shape specimen is carried out as the cutting experiment to verify the credibility and efficiency of the digital twin-driven cutting force adaptive control. [ABSTRACT FROM AUTHOR]

Introduction. This study presents a robust control method for the path following problem of the PUMA560 robot. The technique is based on the Adaptive Fuzzy Type-2 Fast Terminal Sliding Mode Control (AFT2FTSMC) algorithm and is designed to handle actuator faults, uncertainties (such as payload change), and external disturbances. The aim of this study is to utilize the Fast Terminal Sliding Mode Control (FTSMC) approach in order to ensure effective compensation for faults and uncertainties, minimize tracking error, reduce the occurrence of chattering phenomena, and achieve rapid transient response. A novel adaptive fault tolerant Sliding Mode Control (SMC) approach is developed to address the challenges provided by uncertainties and actuator defects in real robotics tasks. Originality. The present work combined the AFT2FTSMC algorithm in order to give robust controllers for trajectory tracking of manipulator’s robot in presence parameters uncertainties, external disturbance, and faults. We use an adaptive fuzzy logic system to estimate the robot’s timevarying, nonlinear, and unfamiliar dynamics. A strong adaptive term is created to counteract actuator defects and approximation errors while also guaranteeing the convergence and stability of the entire robot control system. Novelty. The implemented controller effectively mitigates the chattering problem while maintaining the tracking precision and robustness of the system. The stability analysis has been conducted using the Lyapunov approach. Results. Numerical simulation and capability comparison with other control strategies show the effectiveness of the developed control algorithm. [ABSTRACT FROM AUTHOR]

Control charts are commonly used for process monitoring under the assumption that the variable of interest follows a normal distribution. However, this assumption is frequently violated in real-world applications. In this study, we develop an adaptive control chart based on the exponentially weighted moving average (EWMA) statistic to monitor irregular variations in the mean of the Truncated Transmuted Burr-II (TTB-II) distribution, employing Hastings approximation for normalization. We propose a continuous function for the adaptation of the smoothing constant. The performance of the proposed TTB-II distribution is compared with multiple existing distributions, including TB-II, TB-III, TB-XII, B-II, B-III, and B-XII, to demonstrate its competitive advantages. The run-length profiles, including the average run-length (ARL) and the standard deviation of run-length (SDRL), are computed under various parameter settings. The effectiveness of the proposed chart is evaluated using Monte Carlo (MC) simulations in terms of run-length profiles. The practical implementation of the proposed chart is demonstrated with a real dataset, illustrating the design and application procedures. [ABSTRACT FROM AUTHOR]

Conventional control charts track changes in the process by using predefined process parameters. Conversely, during online monitoring, adaptive control charts modify the process parameters. To improve the process dispersion monitoring in various operational environments, this study presents an adaptive exponentially weighted moving average (AEWMA) control chart based on support vector regression (SVR). This study investigates the efficacy of different kernels such as linear, polynomial, and radial basis functions (RBF) within the SVR framework. By adapting the smoothing constant to the shift's size in process dispersion, the suggested SVR-based AEWMA control chart makes better use of the strengths of the RBF kernel to identify shifts in the process dispersion. To demonstrate the method's effectiveness, real-life data is used in a practical application, highlighting the adaptability and reliability of the SVR-based AEWMA control chart for monitoring process dispersion. The code and supplementary data set file may be found at (https://github.com/muhammadwaqaskazmi/ARL-SDRL-Codes). [ABSTRACT FROM AUTHOR]

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This study proposes a nonlinear adaptive optimal control method, the adaptive H2 control method, applied to the trajectory tracking problem of the wheeled mobile robot (WMR) with four-wheel mecanum wheels. From the perspective of solving mathematical problems, finding an analytical adaptive control solution that satisfies the adaptive H2 performance criterion for the trajectory tracking problem of the WMR with four-wheel mecanum wheels is an extremely challenging task due to the high complexity of the dynamic system. To analytically derive the control law and adaptive control law for this trajectory tracking problem, a proportional-derivative (PD) type transformation is employed to formalize the trajectory tracking error dynamics between the WMR and the desired trajectory (DT). Based on an in-depth analysis of the trajectory tracking error dynamics, a closed-form adaptive control law is analytically derived from the highly complex nonlinear dynamic system equations. This control law provides a solution to the trajectory tracking problem of the WMR while satisfying the adaptive H2 performance criterion. The proposed adaptive nonlinear control method offers a simple control structure and advantages such as improved energy efficiency. Finally, simulations and experimental implementations were conducted to verify the performance of the proposed adaptive H2 control method and the H2 control method in tracking the DT. The results demonstrate that, compared to the H2 control method, the adaptive H2 control method exhibits superior trajectory tracking performance, particularly in the presence of significant model uncertainties. [ABSTRACT FROM AUTHOR]

As renewable energy sources become more integrated into the power grid, traditional virtual synchronous generator (VSG) control strategies have become inadequate for the current low-damping, low-inertia power systems. Therefore, this paper proposes a VSG inertia and damping adaptive control method based on multi-agent deep deterministic policy gradient (MADDPG). The paper first introduces the working principles of virtual synchronous generators and establishes a corresponding VSG model. Based on this model, the influence of variations in virtual inertia (J) and damping (D) coefficients on fluctuations in active power output is examined, defining the action space for J and D. The proposed method is mainly divided into two phases: "centralized training and decentralized execution". In the centralized training phase, each agent's critic network shares global observation and action information to guide the actor network in policy optimization. In the decentralized execution phase, agents observe frequency deviations and the rate at which angular frequency changes, using reinforcement learning algorithms to adjust the virtual inertia J and damping coefficient D in real time. Finally, the effectiveness of the proposed MADDPG control strategy is validated through comparison with adaptive control and DDPG control methods. [ABSTRACT FROM AUTHOR]

The system inertia insufficiency brought on by a high percentage of wind power access to a power grid can be effectively resolved by wind-storage collaborative participation in primary frequency regulation (PFR). However, the impact of energy storage participation in system-frequency regulation is significantly influenced by its state of charge (SOC). In this paper, considering the SOC of energy storage (ES) and the stochastic characteristics of wind turbine (WT) output, the control strategy of wind-storage collaborative participation in the PFR of a system is proposed. Firstly, a WT adaptive inertia control and a model of storage droop control were constructed. Additionally, to prevent the problem of secondary frequency drop brought on by a separate rotational kinetic energy control, a wind-storage collaborative frequency-regulation control scheme was constructed. Secondly, considering changes in wind speed and the SOC of ES, an improved dynamic droop-control strategy for ES is proposed. This strategy was combined with the adaptive inertia control of the WT to establish the PFR of the WT collaborative participation system. Lastly, a simulation example of a two-region, four-machine system was used to validate the efficacy of the frequency-control strategy presented in this paper. The results show that a significant percentage of WTs connected to a power grid can effectively have their frequency-response ability improved by wind-storage collaborative control. [ABSTRACT FROM AUTHOR]

To address the issue of uneven pressure distribution in the abrasive flow field within the inner hole of components with a large depth/diameter ratio, an adaptive control strategy for regulating flow field pressure difference is proposed in this paper. The strategy was based on the effects of pressure fluctuations at the abrasive flow inlet and outlet on pressure distribution patterns and pressure changes within the inner hole flow field, as derived from numerical simulations. An adaptive control fixture was also designed, enabling dynamic adjustments to the fixture gap, which significantly reduced the pressure difference in the flow field. Experimental results demonstrated that the surface texture uniformity of the inner hole was greatly improved after adaptive control. The non-uniformity in surface roughness after control was 59.1% lower compared to pre-control conditions, indicating improved machining consistency. Furthermore, the maximum reduction in surface roughness increased from 1.146 μm to 1.844 μm, and processing efficiency was notably enhanced. [ABSTRACT FROM AUTHOR]

In industrial applications, hydraulic presses maintain workloads by controlling the hydraulic cylinder to extend and retract, ensuring optimum tracking performance in terms of position and force. Dealing with nonlinear and multinode systems, such as hydraulic systems, often requires an advanced approach that frequently includes machine learning and artificial intelligence methods. Introducing an adaptive control system to significantly improve the response of hydraulic presses is a challenge. Therefore, a polynomial regression model predictive control (PR-MPC) mechanism is proposed in this paper to compensate for external disturbances such as the forming processes and friction dynamics. Using polynomial regression modeling and least squares optimization, the approach produces highly accurate data-driven models with an R2 value of 0.948 to 0.999. The simplicity of polynomial regression facilitates the integration of smart algorithms into an expert system with additional decision-making rules. Remote adaptive control integrated within a 5G network is based on I 4.0 distributed system guidelines that provide insights into the behavior of the hydraulic press. The results of real-time experiments have shown that the PR-MPC mechanism integrated into the expert system reduces the absolute response error of the hydraulic press by up to 98.7% compared to the initial control system with a PID regulation. [ABSTRACT FROM AUTHOR]

This paper presents the discrete‐time modelling and control of modular input‐parallel–output‐parallel (IPOP) dual‐active‐bridge (DAB) converters for electric vehicle (EV) charging. The proposed adaptive control system ensures adequate current‐sharing among parallel modules while minimizing DAB current stress by adopting dual phase‐shift modulation. Driven by the growing need for fast EV charging options, the paper highlights the importance of achieving top‐notch control performance, especially with varying load conditions. Specifically, it introduces a discrete‐time model for adjusting controller parameters adaptively, which simplifies the typically cumbersome manual tuning process associated with these systems. The proposed PI formulae are derived to satisfy specifications on the frequency domain as phase margin and the gain crossover frequency of the open loop gain transfer function, ensuring stability and robustness in operation. Moreover, the implementation of these formulae in discrete microcontrollers facilitates seamless PI autotuning for precise current, voltage, or power control. Notably, the proposed control strategy effectively mitigates current overshot issues commonly encountered during module engagement and shedding operations in modular EV chargers. To validate its efficacy, the proposed controller is evaluated through extensive testing and comparisons within the PLECS environment, particularly focusing on a two‐module IPOP‐DAB converter scenario, and including comparisons with classical offline model‐based pole placement methodology. Furthermore, real‐time hardware‐in‐the‐loop experiments are conducted to confirm the feasibility and performance of the proposed controller under realistic EV charging profiles. [ABSTRACT FROM AUTHOR]

Introduction: With the development of technology, the hydraulic control system for hydro-mechanical continuously variable transmission has also made rapid progress. However, the traditional control system for hydro-mechanical continuously variable transmission has drawbacks such as efficiency loss and slow response speed. The study fused the adaptive and pulse width modulation algorithms to solve these problems effectively, and they were applied to the hydraulic mechanical continuously variable drive control system. Method: First, the study analyzed the continuously variable transmission control of hydraulic machinery and designed the control scheme on the basis of the analysis. Then, a new scheme fusing adaptive and pulse-width modulation algorithms was proposed on the basis of the control scheme. Finally, simulation experiments and practical tests were carried out to verify the performance of the control scheme. Results: The results showed that the time to reach the maximum pressure was 2.15 s when the volume of the pilot valve cavity was 5 × 109/L. When the oil flow rate was 70 L/min, the corresponding maximum and minimum values of the main oil circuit pressure were 1.63 MPa and 0.37 MPa, respectively. The time to reach the maximum value of the pressure was 2.05 s, respectively. These values were in line with the design requirements and were better than the comparison values. Discussion: These results confirm that the system has important theoretical and practical significance, which has a positive promoting effect on the development of the hydraulic machinery industry. This article aims to provide a new approach for research and application in related fields. [ABSTRACT FROM AUTHOR]

The development of Ibu Kota Nusantara, the new capital city of Indonesia, is one of the largest infrastructure projects in Southeast Asia. Targeted surveillance and adaptive control measures are needed to mitigate the risks of malaria and other infectious diseases. Indonesia is developing a new capital city, Ibu Koata Nusantara, which is located in a malaria and biodiversity hotspot. In this Comment, the authors outline some of the potential infectious disease-related risks of the new project – primarily malaria and other vector-borne diseases. [ABSTRACT FROM AUTHOR]

Effective communication is crucial for the performance and collaboration within cooperative networked multi-agent systems. However, existing literature lacks comprehensive solutions for dynamically monitoring and adjusting communication topologies to balance connectivity and energy efficiency. This study addresses this gap by proposing a distributed approach for estimating and controlling system connectivity over time. We introduce a modified consensus protocol where agents exchange local assessments of communication link quality, enabling the estimation of a global weighted adjacency matrix without requiring centralized information. The system's connectivity is measured using the second smallest eigenvalue of the communication graph Laplacian, commonly referred to as algebraic connectivity. Additionally, we enhance the consensus protocol with an adaptive mechanism to expedite convergence, irrespective of system size or structure. Furthermore, we present an analytical method for connectivity control based on the Fiedler vector approximation, facilitating the addition or removal of communication links. This method adjusts control parameters to accommodate minor variations in link quality while reconfiguring the network in response to significant changes. Notably, it identifies and eliminates energy-consuming yet non-contributory links, improving long-term connectivity efficiency. Simulation experiments across diverse scenarios and the number of agents validate the efficacy of our proposed algebraic connectivity estimation and tracking strategy. Results demonstrate robust connectivity maintenance against external disturbances and agent failures, underscoring the practical utility of our approach for real-world multi-agent systems. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have transformed Natural Language Processing (NLP) by enabling robust text generation and understanding. However, their deployment in sensitive domains like healthcare, finance, and legal services raises critical concerns about privacy and data security. This paper proposes a comprehensive framework for embedding trust mechanisms into LLMs to dynamically control the disclosure of sensitive information. The framework integrates three core components: User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control. By leveraging techniques such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Named Entity Recognition (NER), contextual analysis, and privacy-preserving methods like differential privacy, the system ensures that sensitive information is disclosed appropriately based on the user's trust level. By focusing on balancing data utility and privacy, the proposed solution offers a novel approach to securely deploying LLMs in high-risk environments. Future work will focus on testing this framework across various domains to evaluate its effectiveness in managing sensitive data while maintaining system efficiency. [ABSTRACT FROM AUTHOR]

This study demonstrates that the Grünwald–Letnikov fractional proportional–integral–derivative (GPID) controller outperforms traditional PID controllers in adaptive cruise control systems, while conventional PID controllers struggle with nonlinearities, dynamic uncertainties, and stability, the GPID enhances robustness and provides more precise control across various driving conditions. Simulation results show that the GPID improves the accuracy, reducing errors better than the PID controller. Additionally, the GPID maintains a more consistent speed and reaches the target speed faster, demonstrating superior speed control. The GPID's performance across different fractional orders highlights its adaptability to changing road conditions, which is crucial for ensuring safety and comfort. By leveraging fractional calculus, the GPID also improves acceleration and deceleration profiles. These findings emphasize the GPID's potential to revolutionize adaptive cruise control, significantly enhancing driving performance and comfort. Numerical results obtained in α = 0.99 from the GPID controller have shown better accuracy and speed consistency, adapting to road conditions for improved safety and comfort. The GPID also demonstrated faster stabilization of speed at 60 km/h with smaller errors and reduced the error to 0.59 km/h at 50 s compared to 0.78 km/h for the PID. [ABSTRACT FROM AUTHOR]

This paper investigates finite-time resource allocation problems (RAPs) for uncertain nonlinear fractional-order multi-agent systems (FOMASs), considering global equality and local inequality constraints. Each agent is described by high-order dynamics with multiple-input multiple-output and only knows its local objective function. Due to the characteristics of dynamic systems, the outputs of agents are inconsistent with their inputs, making it challenging to satisfy the inequality constraints when solving RAPs. To address this complex optimization control problem, a novel hierarchical algorithm is proposed, consisting of a distributed estimator and a local controller. Specifically, the distributed estimator is established by adopting the ϵ -exact penalty function and the gradient descent method. This estimator enables the system states to reach the optimal solution of RAPs within a finite time. Furthermore, the local controller is presented based on the fractional-order tracking differentiator and adaptive neural control approach. Under this controller, the system states are slaved to track the optimal signals generated by the estimator within a finite time. In both the estimator and controller algorithms, the finite-time stability is uniformly guaranteed with the help of Lyapunov functions. Finally, the effectiveness of our algorithm is demonstrated through three simulation examples. [ABSTRACT FROM AUTHOR]

Switched adaptive laws for parameter estimation have been proposed in recent years to improve the balance between control energy and system performance in adaptive schemes, which is often a big issue when using traditional integer-order or fractional-order adaptive laws in adaptive identification and control. These switched adaptive laws are represented as fractional-order differential equations whose order can switch between a number within the range (0 , 1) and 1. However, a general analytical framework that allows proving the boundedness of the solutions and convergence of the estimation/tracking error is not yet available, with only particular analyses for specific schemes being accessible. This paper address this issue, presenting the analysis of four error models that can appear in the field of adaptive systems when these adaptive laws are chosen. The boundedness of the solutions is proved for all cases, together with the convergence to zero of the estimation/tracking error. Additionally, sufficient conditions for parameter convergence are presented, showing that the excitation condition required for parameter convergence in the vector case is also sufficient for parameter estimation in the matrix case. A numerical example is included to show the possible advantages of using switched adaptive laws in a Model Reference Adaptive Control application. Results show that controller parameters can be found for the switched controller, enabling us to obtain an overall improvement of 7.75% with respect to the non-switched integer-order controller and 34.6% with respect to the non-switched fractional-order controller. [ABSTRACT FROM AUTHOR]

Wireless multimedia sensor networks (WMSNs) face challenges such as high energy consumption, latency, and ensuring secure data transmission, especially for real-time multimedia applications. To address these issues, this study proposes the enhanced adaptive transmission control algorithm (EATCA), which integrates a bidirectional long short-term memory (LSTM) model and chaos-based lightweight cryptography. EATCA optimizes energy consumption, reduces latency, and improves data security through the use of adaptive clustering and predictive transmission techniques. The algorithm uses meta-heuristic optimization for cluster head selection and fuzzy logic for route prioritization to ensure efficient data transmission. A chaos-based encryption scheme ensures data confidentiality with minimal computational overhead. The simulation results show that EATCA achieves 43.53 % lower energy consumption compared to traditional low energy adaptive cluster hierarchy (LEACH) protocols, a 96 % packet delivery ratio (PDR), and a 42 ms latency, making it suitable for time-sensitive multimedia applications. The system also achieves an 85 % compression ratio while maintaining security overhead as low as 7 %. These results make EATCA a reliable and energy-efficient solution for optimizing WMSNs and pave the way for scalable and secure network implementations. [ABSTRACT FROM AUTHOR]

A single-inductor multi-source energy harvesting system that can harvest three types of ambient energy was presented. The system can self-supply using an integrated self-starting circuit that does not require an external battery. It performs hybrid modulation of pulse-frequency modulation (PFM) and pulse-width modulation (PWM) and realizes adaptive asynchronous control for input and output, achieving higher system stability and better energy aggregation. Adaptive maximum power point tracking (MPPT) was implemented to generate inductor charging control signals with different frequencies and duty cycles according to different input sources, enabling the inductor to operate in discontinuous mode (DCM) under different input conditions. The energy harvesting system was designed using a 0.18- μ m CMOS process. The simulation results show that the system can realize low-voltage self-startup as low as 200 mV and can provide a stable output of 1.4 V to the load with a 1 V harvested voltage to self-supply. The highest conversion efficiency of the system is 76.43%@ R load = 1 0 k Ω , and the average power consumption in sleep mode is 2.38 μ W. [ABSTRACT FROM AUTHOR]

This article addresses the challenge of adaptive neural‐based asynchronous control for nonhomogeneous Markov jumping systems with input dead zones. Time‐varying transition probabilities are precisely characterized using a two‐layer nonhomogeneous Markov process. A hidden Markov model is employed to detect system modes and resolve the asynchronous issues of controllers. Based on the detected modes and a neural network strategy, an adaptive asynchronous control strategy is proposed. The Lyapunov stability theory is used to prove that the system remains probabilistically bounded under this control law. Finally, the effectiveness of the control strategy is demonstrated through a simulation example. [ABSTRACT FROM AUTHOR]

This survey provides an overview of combining federated learning (FL) and control to enhance adaptability, scalability, generalization, and privacy in (nonlinear) control applications. Traditional control methods rely on controller design models, but real‐world scenarios often require online model retuning or learning. FL offers a distributed approach to model training, enabling collaborative learning across distributed devices while preserving data privacy. By keeping data localized, FL mitigates concerns regarding privacy and security while reducing network bandwidth requirements for communication. This survey summarizes the state‐of‐the‐art concepts and ideas of combining FL and control. The methodical benefits are further discussed, culminating in a detailed overview of expected applications, from dynamical system modelling over controller design, focusing on adaptive control, to knowledge transfer in multi‐agent decision‐making systems. [ABSTRACT FROM AUTHOR]

This article proposes an online universal self‐learning control (USLC) algorithm based on a physical performance policy‐optimization neural network, which aims to solve the problem of universal self‐learning optimal control laws for nonlinear systems with various uncertain dynamics. As a key system characterization, this algorithm predicts the discrepancy between the optimal and current control laws by evaluating overall performance in each iterative learning cycle, leveraging an offline‐trained universal policy network. This approach is universal, as it does not rely on an exact system model and can adaptively control performance preferences across various tasks by customizing the physical performance cost weights. Using the established control law‐performance surface and contraction Lyapunov function, the necessary assumptions and proofs for the stable convergence of the system within a three‐dimensional manifold space are provided. To demonstrate the universality of USLC, simulation experiments are conducted on two different systems: a low‐order circuit system and a high‐order variable‐span aircraft attitude control system. The stable control achieved under varying initial values and boundary conditions in each system illustrates the effectiveness of the proposed method. Finally, the limitations of this study are discussed. [ABSTRACT FROM AUTHOR]

In this article, the problem of the prescribed‐time formation control for the heterogeneous multi‐agent systems (MASs) under actuator faults and external disturbances with sampling‐data settings is discussed. A sufficient condition for the MASs to globally converge to a bounded neighborhood within a prescribed time is given, which can ensure the formation control performance of the MASs with actuator faults. Further, an event‐triggered communication mechanism based on the sampled data is designed to reduce the communication burden. Such a triggering mechanism allows for adjusting the triggering interval to a certain extent while ensuring that the Zeno phenomenon is excluded for each agent. To mitigate the impact of the actuator faults and external disturbances on the system formation control, an actuator fault estimator is designed along with a prescribed‐time state observer to estimate the state of each agent. Then an adaptive control strategy is developed so that the MASs can achieve the desired formation within a prescribed time under the actuator faults and external disturbances. Simulation results are performed to validate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This study addresses the adaptive dynamic output feedback (DOF) control for unmanned aerial vehicle systems subjected to hybrid attacks. These attacks are delineated by two Bernoulli variables, signifying denial‐of‐service (DoS) and deception attacks, respectively. In an effort to enhance energy efficiency and reduce communication costs in the presence of network delays, the study introduces an innovative probability event‐triggering mechanism (PETM). Subsequently, it crafts a DOF controller that operates based on the available measurement outputs. By harnessing Lyapunov stability theory, the study identifies sufficient conditions that guarantee the stability of the system's augmented dynamics. Finally, the efficacy and applicability of the proposed method are underscored through a case study on an unmanned aerial vehicle. [ABSTRACT FROM AUTHOR]

In view of the single-link manipulator system with quantized input, a predefined performance adaptive neural network control method based on Funnel control is proposed in this study. Different from the traditional Funnel control scheme, this method can ensure that the system reaches the predefined performance index within the predetermined time by constructing a new performance function, and the neural network and dynamic surface control technology are used to solve the unknown nonlinear items in the system and the differential explosion problem in the traditional backstepping control method. Theoretical analysis shows that the proposed control scheme not only eliminates the negative effects caused by quantized inputs, but also ensures the stability of the system. By adjusting the design parameters in the MATLAB simulation experiment, the predetermined time convergence of the tracking error is realized, which verifies the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This article concerns the issue of adaptive fuzzy command-filtered fixed-time control in the context of a category of nonlinear systems characterized by mismatched disturbances and unknown nonlinear functions. The backstepping-based disturbance observers are created to alleviate the effects of mismatched disturbances and Fuzzy logic systems are brought into play to model those terms that are unknown. To address the complexity explosion issue in traditional backstepping control, this paper utilizes fixed-time command filters (FTCFs) to design a novel control approach. Moreover, filtering error compensation mechanisms are developed to eliminate the errors introduced by the FTCFs. This paper derives a novel adaptive fixed-time control protocol that successfully conquers the difficulties posed by unknown nonlinear functions and mismatched disturbances. The protocol, implemented within a backstepping framework, guarantees the boundedness of all signals and tracking errors within fixed time. The efficacy of the derived control protocol is illustrated through simulation examples. [ABSTRACT FROM AUTHOR]

This paper investigated the tracking problem of mixed H ∞ and L 2 − L ∞ adaptive fault-tolerant control (FTC) for continuous-time interval type-2 fuzzy systems (IT2FSs). For the membership function mismatch and uncertainty between the modules of the nonlinear system, the IT2 fuzzy model is applied to linearly approximate it. The observer can sensitively estimate the system state, and the adaptive fault estimation functions can estimate adaptively the fault signals, which enables the designed adaptive FTC scheme to ensure the asymptotic stability of the closed-loop control system and achieve the desired mixed H ∞ and L 2 − L ∞ tracking performance. The designed adaptive control law can achieve the purpose of dynamic compensation for faults and disturbances, and the introduced lemmas further reduce the design conservatism by adjusting the slack parameters and matrices. Finally, a mass-spring-damping system is used to illustrate the effectiveness of the designed method. [ABSTRACT FROM AUTHOR]

Active control in adaptive structures allows for reducing the structural weight of a building and thus to drastically decrease the construction sector's global impact. For an active control, it is necessary to estimate the state. Especially for large structures, decentralized approaches are particularly advantageous. However, the choice of the individual decentralized models, the substructuring, is important. This work considers decentralized state estimation for different substructure layouts obtained by the Relative Gain Array (RGA) for an adaptive high-rise structure. The estimators are realized using Information Filters (IF) based on reduced models derived from the full model via SEREP-Guyan and modal reduction. Different degrees of interconnection (none, sparse, full) are investigated w. r. t. estimation accuracy and robustness towards communication failure. For more substructures the estimation error increases. Depending on the layout, communicating is beneficial, but a full interconnection is not necessary for a sufficient estimation. In case of a failed information exchange, communicating estimators should adapt to the fault. [ABSTRACT FROM AUTHOR]

Aiming at the situation of unstable control and high error during recoil compensation control of gunship borne automatic weapon, the recoil compensation control algorithm of gunship borne automatic weapon is proposed. According to the specific firing conditions of airborne automatic weapons, combined with the internal and external factors that affect the recoil of weapons, the force analysis is carried out. Meanwhile, the attitude parameters of gunship, including coordinate parameters and attitude Angle parameters, are calculated. On this basis, the control model of gunship is established, corresponding controller is designed, nonlinear interference observer is introduced, and adaptive control law is designed. The recoil compensation control of airborne automatic weapon is realized. The experimental results show that the proposed recoil compensation control algorithm has small control delay, can respond to compensation control changes in time, and has small control error. The overall fault tolerance of the control algorithm has been improved. [ABSTRACT FROM AUTHOR]

This article discusses the use of adaptive control in the sonar scanning sector within an obstacle detection system, to improve the effectiveness of collision avoidance for autonomous underwater vehicles (AUVs). An adaptive network-based fuzzy inference system (ANFIS) was used for dynamic calculations of the sonar scanning sector. Based on 100 simulation scenarios containing various trajectories created by the mission planner, with various shapes, dimensions and arrangements of static obstacles, and various arrangements and displacement vectors of dynamic obstacles, the effectiveness of the proposed system was tested in comparison with other classical approaches such as a single echosounder and sonar with a fixed scanning sector width. The above sensor configurations were evaluated in terms of the percentage of collision-free trials, the average percentage of trajectory completion, and the average number of activations of the collision avoidance system. Simulations conducted based on the mathematical model of the AUV confirmed that the proposed approach increased the effectiveness of collision avoidance systems for AUVs compared to classical echosounder and sonar-based systems. [ABSTRACT FROM AUTHOR]

This work presents the development of a multilevel electromagnetic actuation system that controls the shape of a flexible rotatory robotic structure. An array of electromagnets is used as the set of actuators that regulate the position of permanent magnets within the flexible device. The primary outcome of this study is the design and experimental validation of the multilevel rotating device. In addition, the theoretical description of the system motion under electromagnetic actuation is formulated using Euler–Lagrange and electromagnetic theories. Given the developed model, a theoretical study leads to designing an adaptive control that considers motion restrictions in the flexible device. The controller aims to modify the current applied to the electromagnets, which changes the interaction forces between the electromagnet and the permanent magnets in the robotic flexible structure. A set of numerical simulations confirms the proposed controller's effectiveness compared to the traditional state feedback approach that does not consider the state restrictions, which is implemented in devices that also operate under an electromagnetic approach. Furthermore, an experimental version of the flexible device allows for testing of the developed controller. The experimental results show the suitability of the proposed control to generate non-oscillatory controlled motion during the regulation of the flexible mechanic device shape. • The adaptive control of a rotating electromagnetic system is developed considering state restrictions. • State restrictions and electromagnetic interference are considered in the control design. • A barrier Lyapunov function is used to derive the adaptive gains for the control action. • The proposed controller is evaluated in an experimental platform. [ABSTRACT FROM AUTHOR]

The hybrid reluctance actuator (HRA) has achieved widespread application in scanning motion tasks. However, the nonlinear perturbations arising from position-dependent stiffness fluctuations, hysteresis, eddy, and flux leakage can significantly affect the control performance. To enhance the control performance of HRA-based systems in scanning motion, this paper introduces an adaptive feedforward method, known as the Chua operator-based Kalman feedforward compensator (COKFC), which aims to mitigate these nonlinear perturbations, with a PID controller serving as the central control element. In the COKFC approach, a Chua operator is employed to effectively capture the inverse hysteresis behavior. A Chua-based time-varying feedforward compensation model is then formulated to represent the inversion of the nonlinear perturbations inherent in the HRA. An improved Kalman filter is utilized for the real-time adaptation of the time-varying parameters within the feedforward compensation model. The design procedure for this control strategy is presented. Experimental evaluations are conducted on an HRA-based stage (HRA-BS), and comparisons are made between the proposed method and several advanced control methods. The experimental results demonstrate that the proposed COKFC method exhibits superior control performance for the scanning motion of the HRA-BS, highlighting its effectiveness in practical applications. [Display omitted] • A simplified Chua operator was created to capture inverse hysteresis. • A Chua operator-based feedforward compensation model was constructed for HRA. • A COKFC method was proposed for the adaptive feedforward control of HRA-BS. • Comparative experiments on HRA-BS are conducted to validate the method. [ABSTRACT FROM AUTHOR]

The task-space distributed adaptive neural network (NN) fixed-time tracking problem is studied for networked heterogeneous robotic systems (NHRSs). In order to address this complex problem, we propose a NN-based fixed-time hierarchical control approach that transforms the problem into two sub-problems: a distributed fixed-time estimation problem and a local fixed-time tracking problem, respectively. Specifically, distributed estimators are constructed so that each follower can acquire the dynamic leader's state in a fixed time. Then, the neural networks (NNs) are employed to approximate the compounded uncertainty consisting of the unknown dynamics of robotic systems and the boundary of the compounded disturbance. More importantly, to guarantee that the tracking errors can converge into a small neighborhood of equilibrium in a fixed time independent of the initial state, the adaptive neural fixed-time local tracking controller is proposed. Another merit of the proposed controller is that the approximation errors are addressed in a novel way, eliminating the need for prior precise knowledge of uncertainties and improving the robustness and convergence speed of unknown robotic systems. Finally, the experimental results demonstrate the effectiveness and advantages of the proposed control method. • Neural networks-based adaptive fixed-time control algorithm has been proposed to solve the task space tracking problem. • The adaptive learning algorithm is used to deal with the compounded disturbance. • Non-redundant and redundant robots are taken into consideration in task space. • Both the fixed-time convergence and uniform ultimate boundedness of tracking errors was guaranteed. [ABSTRACT FROM AUTHOR]

To reduce the cross-regulation and improve the dynamic response performance of a single-inductor double-output Boost converter, an adaptive backstepping sliding mode control (ABSMC) strategy is proposed in this paper. The nonlinear mathematical model of the converter is established, an output function satisfying the exact feedback linearization (EFL) is constructed based on the inverse system theory, and the linearization and decoupling of the model are implemented. Meanwhile, the problem of EFL heavily relying on an exact model is solved by combining backstepping control with sliding mode control. Furthermore, an adaptive reaching law is proposed to adjust the gain of the switching function, and the chattering phenomenon of sliding mode control is reduced. The stability of the system is proven according to the Lyapunov stability theorem. Finally, compared with the existing control methods, both the simulation results and experimental results verify the effectiveness and superiority of the proposed ABSMC strategy. • An adaptive reaching law is proposed, which improves the response speed and reduces the chattering phenomenon. • The advantages of EFL technology, backstepping sliding mode control method, and adaptive control approach are combined, which suppresses the cross-regulation between the output branches and enhances robustness. • The proposed ABSMC strategy is easy to implement and can be applied to other SIMO DC-DC converters. [ABSTRACT FROM AUTHOR]

This article investigates an adaptive dynamic programming-based online compensation hierarchical sliding-mode control problem for a class of partially unknown switched nonlinear systems with actuator failures and uncertain perturbations under an identifier-critic neural networks architecture. Firstly, by introducing a cost function related to hierarchical sliding-mode surfaces for the nominal system, the original control problem is equivalently converted into an optimal control problem. To obtain this optimal control policy, the Hamilton–Jacobi–Bellman equation is solved through an adaptive dynamic programming method. Compared with conventional adaptive dynamic programming methods, the identifier-critic network architecture not only overcomes the limitation on the unknown internal dynamic but also eliminates the approximation error arising from the actor network. The weights in the critic network are tuned via the gradient descent approach and the experience replay technology, such that the persistence of excitation condition can be relaxed. Then, a compensation term containing hierarchical sliding-mode surfaces is used to offset uncertain actuator failures without the fault detection and isolation unit. Based on the Lyapunov stability theory, all states of the closed-loop nonlinear system are stable in the sense of uniformly ultimately boundedness. Finally, numerical and practical examples are given to demonstrate the effectiveness of our presented online compensation control strategy. • An HSS-based online compensation control method is firstly presented for partially unknown switched nonlinear systems with actuator failures and uncertain perturbations. • The designed method considerably reduces the conservativeness arising from the unknown internal dynamics of switched nonlinear systems. • By utilizing the HSS technique, our proposed method has a faster response speed and stronger robustness, and is insensitive to unknown actuator failures. [ABSTRACT FROM AUTHOR]

This paper provides an adaptive impedance control strategy about docking robot, a locking mechanism scheme based on the Stewart platform developing for the problem of excessive collision contact force caused by external environmental interference during autonomous docking tasks of ground unmanned vehicles. First, the docking robot system was introduced, and an inverse kinematics model of the docking robot was constructed. Next, to solve the problem of excessive collision contact force during docking, we have designed an adaptive impedance control algorithm, which includes a steady-state error model of contact force, an adaptive compensation controller design, and system stability analysis, thus achieving active compliance control. Finally, some simulations and experiments were conducted on the docking robot. Compared with traditional impedance control, adaptive impedance control reduces docking collision contact force and achieves compliant control. In the future, the experimental results provide a new docking approach for autonomous docking of unmanned vehicles, and also serve as a reference for the development of intelligent vehicles. • A docking robot scheme based on the Stewart platform is developed for dynamic docking of UVG. • An adaptive impedance control strategy is proposed to improve adaptability to the environment. • The effectiveness of the control strategy has been verified through simulation and experimentation. [ABSTRACT FROM AUTHOR]

Fixed control parameters and various optimisation objectives significantly limit the robot control performance. To address such issues, a parameter adaptive controller based on deep reinforcement learning is introduced firstly to adjust control parameters according to the real‐time system state. Further, multiple evaluation mechanisms are constructed to take account of optimisation objectives so that the controller can adapt to different control performance indexes by different evaluation mechanisms. Finally, the target pedestrian tracking control with mobile robots is selected as the validation case study, and the Proportional‐Derivative Controller is chosen as the foundation controller. Several simulation and experimental examples are designed, and the results demonstrate that the proposed method shows satisfactory performance while taking account of multiple optimisation objectives. [ABSTRACT FROM AUTHOR]

The advanced quality control of the raw mix remains a priority for the cement industry, particularly in recent years, where large quantities of alternative fuels and raw materials are used in clinker production, aiming to reduce the CO2 footprint. This study presents an adaptive control system with a variable sampling period for regulating raw mix quality in the raw mill (RM) output in a process with four independent inputs and four outputs: the lime saturation factor (LSF), silica modulus (SM), alumina modulus (AM), and SO3. The three pillars of the system are (1) mill dynamics calculation using exclusively industrial data, (2) the design of the controllers to meet robustness criteria, and (3) performance enhancement through simulators. Our technique periodically adjusts the gains of the controllers based on the mill's dynamic parameters, which are computed from raw mix laboratory analyses. The presented results correspond to more than 14,000 h of mill operation. The standard deviation of the LSF at the mill outlet ranged from 1.5 to 3, which is equivalent to 1 to 2 standard deviations of LSF reproducibility. The standard deviation of the other moduli was close to the corresponding reproducibility of each. The presented adaptive gain-scheduling controller for LSF can be applicable to a broad range of raw meal grinding systems. [ABSTRACT FROM AUTHOR]

This research introduces a new adaptive control approach tailored for networked control systems, aimed at addressing the challenges of Denial of Service (DoS) attacks and round-trip time delays. Initially, a compensation mechanism based on estimated values is introduced to counter DoS attacks; subsequently, for round-trip time delay issues, a predictive delay compensation control strategy is designed. The proposed control strategy does not rely on the specific structure or a particular model of the system, but instead solely updates the controller using the system's input and output values. Moreover, we successfully demonstrate the bounded stability of the system using the direct Lyapunov method. Ultimately, the effectiveness and practicality of this method are confirmed through simulation results. [ABSTRACT FROM AUTHOR]

The $ PID $ PID controller is present in at least 90% of industrial applications due to its simplicity and robustness to control linear and non-linear systems. Its main drawbacks include difficulty in achieving proper gains tuning for complex systems and lack of adaptability attributed to its fixed gains when system dynamics change. Despite the emergence of promising controllers like Neural Networks, Fuzzy Logic, Sliding Modes, or Genetic Algorithms, diverse research efforts focus on using these to improve the $ PID $ PID rather than replacing it, aiming for either ideal fixed gains or adaptive gains. Nevertheless, while some require system models, training data, rules establishment, extensive iterations, and demanding computational resources, the $ PID $ PID and the novel $ APID $ APID controller do not. The new seesaw algorithms propose alternative behaviours based on the current error and its derivative, which are dynamic parameters that change over time. These behaviours can be inserted into the $ PID $ PID to imbue it with adaptive characteristics, achieving adaptive $ PID $ PID ( $ Seesaw APID $ SeesawAPID) controllers with faster signal rise and stabilization times, reduction of the maximum peak, and less error accumulation compared to conventional $ PID $ PID. [ABSTRACT FROM AUTHOR]

Model-Free Adaptive Control (MFAC) is a control strategy that eliminates the need for prior knowledge of the system model by leveraging online data to learn the system dynamics and design controllers. This paper offers a comprehensive exploration of the significance of control theory in unmanned surface vessels (USVs), with a particular focus on data-driven approaches. It provides a comprehensive overview of various MFAC algorithms proposed for USVs in diverse scenarios, including neural network-based MFAC, reinforcement learning-based MFAC, and fuzzy logic-based MFAC. The objective of this review is to provide a profound understanding of the latest advancements in MFAC technologies and serve as a guiding resource for further developments in the field. [ABSTRACT FROM AUTHOR]