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Monitoring production processes to provide potential customers with quality products while utilising fewer resources and reducing the cost of inspection is a challenge to quality and industrial engineers. Aiming at developing an improved design using reduced sample sizes, this study proposes a triple sampling (TS) $ \bar{X} $ X ¯ chart to monitor shifts in the process mean when the quality characteristic of interest follows the gamma distribution. Optimal parameters are determined by employing three optimisation procedures. The detection ability of the TS $ \bar{X} $ X ¯ chart is evaluated and contrasted with competing charts via the average number of observations to signal (ANOS), average run length (ARL) and expected ANOS (EANOS) criteria. When the TS $ \bar{X} $ X ¯ chart is designed for detecting small and large shifts, it is noticed that the sample sizes at the first, second and third sampling stages are larger for detecting small shifts. Compared to its competitors, the TS $ \bar{X} $ X ¯ chart requires fewer observations on the average to signal a shift; hence, it uses resources more efficiently and incurs lower inspection costs. To aid in the implementation of the TS $ \bar{X} $ X ¯ chart, an illustrative example of monitoring the wafer process is provided. Overwhelming evidence based on reported results supports the TS $ \bar{X} $ X ¯ chart. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive memetic algorithm based on a new neighbourhood structure (AMA) for solving the assembly job shop scheduling problem, with the aim of minimising the maximum completion time (makespan). To utilise the knowledge of problem, a theoretical analysis is conducted to explore the criteria for feasible and effective movement of operations under assembly constraints, and a multi-operation precise joint movement neighbourhood structure is proposed accordingly. In the AMA, to ensure the feasibility of solutions during the evolution process, a feasible encoding mechanism based on the constraint degree of operations is designed, a greedy active decoding method as well as feasible crossover operation based on independent operation chains are designed specifically for this encoding method. To avoid premature convergence of the population, a population update operator with diversity adaptive control is proposed. Finally, by comparing the results with five state-of-the-art algorithms, the superiority of AMA in terms of solution quality and stability is verified, particularly with the update of known optimal solutions for 11 instances. [ABSTRACT FROM AUTHOR]

Traditional machining is transforming to digital and intelligent machining, in which adaptive machining cyber-physical system (CPS) provides a useful approach to control the machining quality of large thin-walled parts. And the running of adaptive machining CPS is a complex multi-processes execution flow, which can be regarded as a continuous–discrete hybrid system. To realise adaptive controlling of machining quality and adaptive managing of process flow, a unified model for continuous–discrete hybrid adaptive machining CPS is constructed. Firstly, an architecture of adaptive machining CPS is proposed. Next, the cutting process in adaptive machining CPS is modelled as a continuous-variable system (CVS), while the process flow in adaptive machining CPS is modelled as a discrete-events system (DES). Then, the finite state machine is adopted to integrate the CVS and DES to form the unified model of adaptive machining CPS. Finally, an adaptive machining CPS is developed based on the unified model, and the machining results demonstrate that machining quality is efficiently controlled, as well as the process flow is orderly managed. The built unified model has four features, respectively universality, integrability, scalability, and reconfigurability, which can be reconstructed to form a new instancing model according to the different machining requirements. [ABSTRACT FROM AUTHOR]

In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios. [ABSTRACT FROM AUTHOR]

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The low-load efficiency performance of alkaline water electrolyzers (AWEs) is poor so its operation range is limited. It is unfeasible for AWEs to follow the fluctuant renewable energy sources (RESs). Besides, the power fluctuation will lead to the electrolyzer temperature variation. The efficiency performance is negatively influenced. Focusing on these problems, an efficiency enhancement control strategy for AWEs operating at varying temperature is introduced in this paper. First, the efficiency model of AWEs is established. Then, a multi-mode adaptive control strategy is proposed to improve the hydrogen production efficiency of AWEs. Based on the U–I curves of AWEs, parameter identification method is used to obtain the key parameters of proposed efficiency model. Finally, the feasibility of the control strategy is experimentally validated on a 10 kW AWE. The experimental outcome shows that by applying the proposed method, the low-load hydrogen production efficiency of AWEs can be improved at different temperatures. At 15% rated load, the electrolyzer efficiency increases from 21.54% to 42.73% at 80 °C. The operation range of the electrolyzer can be extended from 30% to 100%–21%∼100% of rated load at 80 °C. © 2024 The Authors. Published by Elsevier Ltd. • The efficiency model of alkaline electrolyzers is established. • The efficiency of alkaline electrolyzers under varying temperatures is enhanced. • The proposed method is experimentally verified on a 10 kW alkaline electrolyzer. [ABSTRACT FROM AUTHOR]

The modular multilevel matrix converter can provide frequency control support for remote AC power grids and has good application prospects in high-voltage low-frequency AC transmission systems. However, the dynamic performance of the traditional outer-loop PI controller is susceptible to changes in its own parameters and external conditions, which in turn influences the control effect. Therefore, a variable universe fuzzy proportional-integral (PI) adaptive outer-loop control strategy is proposed in this paper. Using the automatic sensing and self-adaptive capability of variable universe fuzzy control, the self-adaptation and self-adjustment of the size of the input error of the outer loop is achieved. Meanwhile, a function-based scaling factor approach is utilized to develop scaling factors for the input and output domains to enhance the control accuracy. Simulation and experimental findings demonstrate that this control strategy advances system stability and reinforces the self-adaptive adjustment capability of outer-loop control when compared to preexisting approaches. [ABSTRACT FROM AUTHOR]

• The dynamic model for VCRS based on the Switched Moving-Boundary method is built. • The impact of environmental disturbances on the refrigeration performance is studied. • A hybrid paradigm combining online identification and adaptive control is proposed. • The overshoot performance is optimized at 10–4 magnitude in uncertain environments. • Adaptive control's IAE by 2–3 orders of magnitude compared to traditional control. Vapor compression refrigeration systems (VCRS) occupy a crucial position in modern society and in the field of thermal sciences. However, the operation of VCRS is subjected to both external disturbances (dynamic-changing environment) and inherent system characteristics (coupling or nonlinear features), leading to issues like reduced refrigeration efficiency and significant fluctuations in cooling capacity. To address these challenges and enhance the adaptability of VCRS in dynamically changing environments, this study establishes a dynamic simulation model for VCRS based on the Switched Moving-Boundary method. The impact of external environmental disturbances on refrigeration performance is investigated, and continuous online identification methods are employed to elucidate its internal coupling characteristics and nonlinear features. The adaptive temperature control method is introduced, benefiting from the developed recursive least squares method with a forgetting factor for online identification, achieving precise model identification which facilities the real-time parameters tuning of adaptive controller. The results indicate that the hybrid paradigm of online identification and adaptive control algorithm not only effectively handles various disturbances but also reduces overshoot and IAE by 2–3 orders of magnitude compared to traditional PID controllers. Adaptive PID control maintains overshoot in the 10–4 order of magnitude and IAE in the 10–5 order of magnitude. [Display omitted] [ABSTRACT FROM AUTHOR]

In automated fiber placement (AFP), the contact pressure between the roller and the mold is an important process parameter. Too small or too large contact pressure will result in unacceptable laying defects. This paper focuses on the control method of contact pressure in AFP. Firstly, the dynamic model of the pressing foot mechanism is established, and a mathematical relationship of input air pressure and output force is developed. Then, a self-tuning PI control algorithm is used to implement servo control of the roller output force based on the Slowly Time-Varying Recursive Identification Algorithm. Also, using Abaqus/CAE, the mapping relationship between the contact area and measurements from displacement sensors is established to predict the contact area in real time. Finally, the contact pressure control method is established by combining the servo control method of contact force and the prediction method of contact area. Experimental results show that the constant pressure control method can effectively stabilize the contact pressure, with a pressure fluctuation of less than 4% under the experimental conditions set in this paper. [ABSTRACT FROM AUTHOR]

This article develops a predefined‐time sliding mode control approach for systems with external disturbances and uncertainties through a nonlinear disturbance observer (DO). For addressing predefined‐time stabilization problem of robotic manipulator system, a predefined‐time sliding mode surface is proposed, ensuring system states converge to origin within a predefined‐time once sliding mode surface is attained. Compared to conventional fixed‐time and finite‐time control strategies, a distinctive advantage of this scheme is that system settling time can be explicitly chosen in advance and independent of system states. To achieve predefined‐time performance, a disturbance observer is introduced to generate the disturbance estimate, which can be incorporated into controller to counteract disturbance. To address the systems uncertainty, an adaptive law is employed to estimate the unknown upper boundary of system uncertainties. Finally, the effectiveness and performance of the proposed scheme are illustrated by simulation and experiment. [ABSTRACT FROM AUTHOR]

Reinforcement learning is commonly associated with training of reward‐maximizing (or cost‐minimizing) agents, in other words, controllers. It can be applied in model‐free or model‐based fashion, using a priori or online collected system data to train involved parametric architectures. In general, online reinforcement learning does not guarantee closed loop stability unless special measures are taken, for instance, through learning constraints or tailored training rules. Particularly promising are hybrids of reinforcement learning with classical control approaches. In this work, we suggest a method to guarantee practical stability of the system‐controller closed loop in a purely online learning setting, in other words, without offline training. Moreover, we assume only partial knowledge of the system model. To achieve the claimed results, we employ techniques of classical adaptive control. The implementation of the overall control scheme is provided explicitly in a digital, sampled setting. That is, the controller receives the state of the system and computes the control action at discrete, specifically, equidistant moments in time. The method is tested in adaptive traction control and cruise control where it proved to significantly reduce the cost. [ABSTRACT FROM AUTHOR]

As for a class of strict‐feedback nonlinear systems with simultaneous sensor and actuator faults, the article focuses on the problem of fixed‐time fault‐tolerant control. Both fixed‐time command filters and a compensation mechanism are utilized to cope with these issues of differential explosion and filter errors. Meanwhile, through the fusion of fixed‐time control, fuzzy logic systems, adaptive control and the backstepping technology, a n$$ n $$‐step design scheme is presented. Our proposed controller ensures the fixed‐time convergence of all the signals in the system even with sensor/actuator faults, and allows the designers to acquire the upper limit value of the fixed convergence time only by tuning the design parameters. And this scheme takes into account rapidity, adaptability and fault tolerance of the system simultaneously. Based on the numerical simulation analysis, it is shown that the better tracking performance can be achieved. So it is concluded that our strategy is effective. [ABSTRACT FROM AUTHOR]

A neural network adaptive integral terminal sliding mode control method with input saturation is proposed for the horizontal vibration problem of high‐speed elevator car systems caused by uncertainties such as guideway excitation and shaft piston wind change. First, considering the input saturation problem existing in the elevator control actuator, a class of smooth functions is introduced to approximate the nonlinearity of switching saturation, and an eight‐degree‐of‐freedom asymmetric anti‐saturation nonlinear system model of the high‐speed elevator car is established; second, in order to solve the singularity problem existing in the terminal sliding‐mode control, a nonlinear term is introduced into the sliding‐mode design, and a neural network is utilized for the fitting of the complex unknown function, and the design of the an adaptive integral terminal sliding mode controller (AITSMC), which enables the state variables of the system to achieve finite time convergence and proves the stability of the system by using Lyapunov theory; finally, under the action of two typical guide excitations, the proposed controller is compared with the passive control and adaptive control (AC), and the results show that, after adopting the proposed control method, the vibration acceleration eigenvalue is reduced by more than 60%, which effectively suppresses the horizontal vibration of the car system and verifies the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

The primary focus of this research paper is to explore the realm of dynamic learning in sampled‐data strict‐feedback nonlinear systems (SFNSs) by leveraging the capabilities of radial basis function (RBF) neural networks (NNs) under the framework of adaptive control. First, the exact discrete‐time model of the continuous‐time system is expressed as an Euler strict‐feedback model with a sampling approximation error. We provide the consistency condition that establishes the relationship between the exact model and the Euler model with meticulous detail. Meanwhile, a novel lemma is derived to show the stability condition of a digital first‐order filter. To address the non‐causality issues of SFNSs with sampling approximation error and the input data dimension explosion of NNs, the auxiliary digital first‐order filter and backstepping technology are combined to propose an adaptive neural dynamic surface control (ANDSC) scheme. Such a scheme avoids the n$$ n $$‐step time delays associated with the existing NN updating laws derived by the common n$$ n $$‐step predictor technology. A rigorous recursion method is employed to provide a comprehensive verification of the stability, guaranteeing its overall performance and dependability. Following that, the NN weight error systems are systematically decomposed into a sequence of linear time‐varying subsystems, allowing for a more detailed analysis and understanding. In order to ensure the recurrent nature of the input variables, a recursive design is employed, thereby satisfying the partial persistent excitation condition specifically designed for the RBF NNs. Meanwhile, it can verify that the NN estimated weights converge to their ideal values. Compared with the common n$$ n $$‐step predictor technology, there is no need to redesign the learning rules due to the designed NN weight updating laws without time delays. Subsequently, after capturing and storing the convergence weights, a novel neural learning dynamic surface control (NLDSC) scheme is specifically formulated by leveraging the acquired knowledge. The introduced methodology reduces computational complexity and facilitates practical implementation. Finally, empirical evidence obtained from simulation experiments validates the efficacy and viability of the proposed methodology. [ABSTRACT FROM AUTHOR]

In this article, we present an innovative approach for controlling nonlinear switched systems (NSSs) with strict feedback utilizing adaptive neural networks (ANNs). Our methodology encompasses several facets, addressing key challenges inherent to these systems. To commence, we tackle the constrained nature of NSSs with strict feedback by designing a barrier Lyapunov function. This function ensures that all states within the switched systems remain within prescribed constraints. Additionally, we harness neural networks (NNs) to approximate the unknown nonlinear functions inherent to the system. Furthermore, we deploy an ANN state observer to estimate unmeasurable states. Our approach then proceeds to develop a cost function for the subsystem. Building upon this, we apply the Hamiltonian–Jacobi–Bellman (HJB) solution in conjunction with observer and behavior critic architectures, all rooted in backstepping control (BC) principles. This integration yields both a virtual optimal controller and a real optimal controller. Furthermore, we introduce a novel ANN event‐triggered control (ETC) strategy tailored explicitly for strictly feedback systems. This strategy proves highly effective in reducing the utilization of communication resources and eliminating the occurrence of Zeno behavior. Our analysis provides formal proof that all states within the closed‐loop system exhibit half‐leaf consistency and are ultimately bounded, regardless of arbitrary switching conditions. Finally, we substantiate the efficacy and viability of our control scheme through comprehensive numerical simulations. [ABSTRACT FROM AUTHOR]

The operation of multi‐agent systems (MAS) in space necessitates considerations for obstacle avoidance, collision prevention, and connectivity among agents. This coupling conflict, stemming from spatial resource utilization, poses a significant and non‐negligible threat to the safe operation of MAS. This article proposes a consensus control scheme for the time‐varying formation of MAS, incorporating functions for maintaining connectivity, collision avoidance, and obstacle dodging. This scheme effectively constrains the time‐varying formation tracking error within an arbitrarily small range. The considered system model takes a high‐order nonlinear form, with uncertainties and disturbances present in each order. This grants the control scheme with high generality and practicability. By employing barrier Lyapunov function to delineate the safe operational space of MAS, conflicts in spatial resource utilization are avoided. This approach simultaneously fulfills the requirements for connectivity maintenance, collision avoidance, and obstacle dodging in the safe operation of MAS. An additional rotation operator is integrated into the controller to smoothly address the "minima" problem, eliminating the need for external intervention. Gaussian radial basis function is used to estimate the nonlinear terms, uncertainties, and unknown perturbation online in the system. The stability of the MAS under the proposed control scheme is analyzed through Lyapunov function. Finally, numerical simulation results are demonstrated to explain the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

This article presents a discrete‐time robust model reference adaptive controller and adaptive sigmoid super‐twisting sliding mode (RMRAC‐ASSTSM) and its stability analysis using Lyapunov stability theory. This control structure is robust to matched and unmatched dynamics. In addition, the chattering phenomenon tends to be suppressed in the steady state due to adaptive super‐twisting sliding mode action using a sigmoid function. The performance of the proposed control structure is corroborated with simulation results, considering a second‐order non‐minimum phase unstable plant, where it can be seen that RMRAC‐ASSTSM regulation errors converge to a finite residual set when the plant presents unmodeled dynamics. A comparison is also presented with a similar RMRAC‐based control structure with an adaptive super‐twisting sliding mode implemented with a sign function. [ABSTRACT FROM AUTHOR]

Cognitive conflict is typically experienced as negative, which has been argued to drive adaptive behavior following a conflict. We tried to change the negative value of conflict using evaluative conditioning, and measured changes in conflict adaptation in a subsequent Stroop task (N = 416 Prolific participants, English native speakers from different countries). We did not find evidence for decreased conflict adaptation following positive evaluative conditioning of conflict. However, we also did not find evidence for the change of conflict evaluation measured with the affect misattribution procedure in the follow-up experiment (N = 70). Interestingly, the exploratory follow-up analysis showed that people with low goal motivation (as measured through BAS Drive) did show the expected effect. A memory test for the evaluative conditioning pairings and the follow-up experiment suggest that, although the affective value of conflict was difficult to change, people with low goal motivation experienced less difficulty remembering the association between conflict stimuli and positive pictures. Our findings show additional evidence that conflicts are inherently negative, however, there is no clear support for, or against, the affective signaling hypothesis, that is the idea that conflict negativity drives control adaptations. [ABSTRACT FROM AUTHOR]

To facilitate cooperation among connected automated vehicles (CAVs), such as cooperative platooning or cooperative lane changes in mixed traffic comprising CAVs, connected human-driven vehicles (CHVs), and conventional (unconnected) human-driven vehicles (HDVs), an ego CAV needs to discern which surrounding vehicles are connected (CAVs or CHVs) and which are unconnected (HDVs). Therefore, this paper introduces a surrounding vehicles identification system (SVIS) designed to enable CAVs to identify the connectivity status of surrounding vehicles. Unlike a previously developed preceding vehicle identification system that only identifies the connectivity of its immediately preceding vehicle, the proposed SVIS generalizes this identification to nearby surrounding vehicles. Furthermore, the proposed SVIS significantly reduces the error in the connected vehicle identification by integrating distance and the magnitude of velocity for matching instead of distance-only matching used by the previous system. The SVIS is a must to enable CAVs to form platoons via lane changes after a connected vehicle is detected in an adjacent lane. We compared a newly proposed distance-plus-speed-matching approach with a previously developed distance-only-matching approach using Next Generation Simulation (NGSIM) data. Through comparison, the efficacy of the proposed distance-plus-speed-matching SVIS is demonstrated to be considerably superior to that of the distance-only-matching SVIS. Given that a reliable and robust method for identifying surrounding connected vehicles is key to the successful formation of cooperative adaptive cruise control (CACC) platoons and cooperative lane changes, the proposed distance-plus-speed-matching SVIS would help quickly and efficiently form platoons in a mixed traffic environment. [ABSTRACT FROM AUTHOR]

This study investigates the issue of vehicle platoon control for nonlinear heterogeneous vehicle platoon systems containing a leader and several followers subject to unknown uncertainties and disturbances. Platoon control has been shown to effectively achieve the consensus of vehicles, increase road capacity, and enhance traffic safety. Achieving that the vehicle platoon maintains a desired inter-vehicle spacing and reaches the velocity consensus is the main objective of platoon control. To achieve vehicle consensus and avoid collisions, the new distributed cooperative sliding mode control protocols are proposed for the heterogeneous platoon to ensure the convergence of vehicle spacing errors and velocity errors and to guarantee string stability. The spacing policy and information flow topology are introduced within the development of the platoon control strategies. Furthermore, the novel distributed neural adaptive control strategies utilizing the nonsingular updating method are developed to address the problem of the unknown uncertainties and heterogeneities in the platoon vehicles. The proof of the ultimate boundedness of the spacing and velocity errors is carried out by utilizing the Lyapunov theory. The analysis of string stability is conducted to confirm the effectiveness of the vehicle platoon control strategies. Finally, the correctness and efficacy of the control strategies can be verified through a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, we present an adaptive optimal control algorithm for solving the tracking control problem of multiple autonomous surface vehicles under uncertain dynamics and unknown external disturbances. The proposed control algorithm uses an adaptive dynamic programming technique with optimal compensation terms. A disturbance observer is designed to handle the problem of unknown time-varying external disturbances. It is proven that all the signals in the closed-loop system are bounded. Simulation results are provided to illustrate the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

The editorial in the Journal of Intelligent Manufacturing discusses the increasing use of information technologies in manufacturing organizations, focusing on AI and data-driven decisions. The special issue highlights data-driven industrial decision-making techniques and theoretical frameworks for AI applications in manufacturing. The editorial also mentions the ASSISTANT project, funded by the European Commission, which introduced intelligent digital twins to enhance decision-making in manufacturing. The special issue covers four key themes: advanced AI-based production and logistics systems design, AI-based planning and scheduling, AI-based quality control and predictive maintenance, and AI-driven process optimization and intelligent control systems. [Extracted from the article]

The adaptive backstepping method has strong performance in handling control problems with disturbances in previous research. However, it exhibits limitations when applied to time-varying disturbances. This paper proposes an improved adaptive backstepping method based on Discrete Fourier Transform (DFT). By estimating the frequency spectrum of the disturbance and indirectly obtaining its time-domain estimate, the proposed method effectively overcomes the shortcomings of traditional adaptive backstepping. To address the issue of frequency leakage caused by discontinuities in window data, the idea of DFT is improved by using STFT with the addition of the window function and window-shifting operation. Additionally, a projection operator and adaptive reduction of the control objective are employed to mitigate the effects of actuator saturation. Finally, in simulations involving an aircraft subjected to gusts and turbulence, the proposed method is compared with traditional adaptive backstepping and radial basis function (RBF) neural network control methods. Simulation results demonstrate that the proposed method outperforms the others in these experimental scenarios. [ABSTRACT FROM AUTHOR]

In Automated Fiber Placement (AFP), the substantial structure of the placement robot, the variable mold shapes, and the limited free space pose significant challenges for planning collision-free robot transitions. The task involves planning a collision-free path within the robot's high-dimensional configuration space. Informed RRT* is a common approach for such problems but often struggles with efficiency and path quality in environments with large informed sampling spaces influenced by obstacles. To address these issues, this paper proposes an improved Informed RRT* algorithm with a Local Knowledge Acceleration sampling strategy (LKA-Informed RRT*), aimed at enhancing planning efficiency and adaptability in complex obstacle settings. An Adaptive Sampling Control (ASC) rate is introduced, measuring the algorithm's convergence speed, guides the algorithm to switch between informed and local sampling adaptively. The proposed local sampling method uses failure nodes from the exploration process to estimate obstacle distributions, steering sampling toward regions that expedite path convergence. Experimental results show that LKA-Informed RRT* significantly outperforms state-of-the-art algorithms in convergence efficiency and path cost. Compared to the original Informed RRT*, the proposed method reduces planning time by about 60%, substantially boosting efficiency for collision-free transitions in complex environments. [ABSTRACT FROM AUTHOR]

This study is the first time devoted to seek an online optimal tracking solution for unknown nonlinear singularly perturbed systems based on single network adaptive critic (SNAC) design. Firstly, a novel identifier with more efficient parametric multi-time scales differential neural network (PMTSDNN) is developed to obtain the unknown system dynamics. Then, based on the identification results, the online optimal tracking controller consists of an adaptive steady control term and an optimal feedback control term is developed by using SNAC to solve the Hamilton–Jacobi–Bellman (HJB) equation online. New learning law considering filtered parameter identification error is developed for the PMTSDNN identifier and the SNAC, which can realize online synchronous learning and fast convergence. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed loop system consisting of the PMTSDNN identifier, the SNAC and the optimal tracking control policy. Three examples are provided to illustrate the effectiveness of the investigated method. [ABSTRACT FROM AUTHOR]

The study aims to tackle two key issues: the dependency of optimal controllers on available training data and their limited capability in managing multi-component structural responses. Real Time Fuzzy Logic Efficient Control system (RTFLEC) was developed to address these challenges. This system integrates a fuzzy logic inference system with a multi-verse optimization algorithm and utilizes real time training for real-time optimal controller derivation. It incorporates decentralized systems and optimal controller memory concepts and employs a magnetorheological damper (MR) for adjustable vibration control. Two case studies were conducted to evaluate the RTFLEC system's effectiveness. The first study focused on a three-story shear building under seismic loads, while the second analyzed a 76-story building facing multi-directional wind loads. The results showed that RTFLEC system reduced structural drifts by 28.33% and 51.5% during near-field and far-field earthquakes, respectively. It also decreased structural acceleration by 27.66% and 53% during near-field and far-field earthquakes, respectively. For wind-induced structures, it reduced story displacement by 37.5%, 14.5%, and 12% against across-wind, along-wind, and rotational forces, respectively. RTFLEC system demonstrated robust performance against uncertainties in external excitations, providing exceptional structural responses. Its key advantage lies in real-time adaptability, requiring minimal training data and computational processing. These findings highlight its significant potential in enhancing the performance of adaptive vibration control systems under unpredictable real-world loads. [ABSTRACT FROM AUTHOR]

A wheelchair is the primary means of mobility for individuals unable to walk. This study aimed to develop a safety system for electric wheelchairs to help people with tetraplegia avoid obstructions. The main contribution of this study is the implementation of a sensor with a wider reflection angle and the adjustment of the wheelchair's speed based on the distance to the obstruction, eliminating the need for manual speed selection. The safety system utilizes LV-MaxSonarEZ1 ultrasonic sensors, which function as reflectance distance readers placed on the front, rear, right, and left sides of the wheelchair. The output from the sensors is input into an Arduino, which functions as the controller. The safety system employs adaptive speed control based on distance through a fuzzy logic controller. The wheelchair was tested with obstruction distances of 1, 1.8, 3, and 10 m. The wheelchair could stop at a distance of 34.06 cm for forward movement and 45.16 cm for reverse movement. The results of this study successfully demonstrate the creation of a safety system on a wheelchair using ultrasonic sensors to avoid obstructions and detect areas, with more adaptive speed control based on distance through a fuzzy logic controller. [ABSTRACT FROM AUTHOR]

Dynamic impacts such as wind and earthquakes cause loss of life and economic damage. To ensure safety against these effects, various measures have been taken from past to present and solutions have been developed using different technologies. Tall buildings are more susceptible to vibrations such as wind and earthquakes. Therefore, vibration control has become an important issue in civil engineering. This study optimizes tuned mass damper inerter (TMDI) using far-fault ground motion records. This study derives the optimum parameters of TMDI using the Adaptive Harmony Search algorithm. Structure displacement and total acceleration against earthquake load are analyzed to assess the performance of the TMDI system. The effect of the inerter when connected to different floors is observed, and the results are compared to the conventional tuned mass damper (TMD). It is indicated that the case of connecting the inerter force to the 5th floor gives better results. As a result, TMD and TMDI systems reduce the displacement by 21.87% and 25.45%, respectively, and the total acceleration by 25.45% and 19.59%, respectively. These percentage reductions indicated that the structure resilience against dynamic loads can be increased using control systems. [ABSTRACT FROM AUTHOR]

This paper investigates the practical prescribed time tracking for a class of uncertain nonlinear systems based on neural networks and event‐triggered control. Introducing a time‐varying constraint function transforms the original practical prescribed time‐tracking control issue into a tracking error constraint problem. An event‐triggered adaptive control has been proposed, which can effectively reduce the communication burden between the controller and the actuator. Using neural networks to approximate unknown nonlinear functions avoids the differentiation of virtual controllers, thereby reducing the computational burden. In addition, users can independently choose preset time and tracking accuracy without changing the control structure, which remains independent of the initial conditions and any design parameters. Finally, the effectiveness of this method is verified through simulation examples. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the adaptive fuzzy backstepping control problem for uncertain discrete‐time nonlinear systems with mismatched disturbances. A novel adaptive fuzzy command filtered backstepping approach is proposed, which can overcome the noncausal problem by using the command filter. The filter errors generated by filter are removed by constructing error compensation mechanism. The fuzzy logic systems are used to approximate the uncertain nonlinear dynamics, and the unknown mismatched disturbances are actively attenuated by using adaptive control technique. Based on the proposed new weighed Lyapunov functions, all the signals in the close‐loop system are proved to be uniformly ultimately bounded. The viability of proposed method is demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive quantized output feedback event‐triggered (ET) distributed consensus control approach for an uncertain nonstrict nonlinear switched fractional‐order (FO) multi‐agent system (SFOMAS) subject to communication limitation is proposed. By relying on the common Lyapunov function method, the switch signals are arbitrarily, and no information regarding the switching instants is required. The consensus error of each agent has its own asymmetric time‐varying constraints (ATCs). By adopting the common barrier Lyapunov function (BLF), violation of these constraints is prevented. For each agent, different input nonlinearities can be considered in different modes, and the controllers need no information about their types and characteristics. The communication burden between outputs sensors and the controllers is reduced by applying uniform‐logarithmic‐hysteresis quantizers to the outputs signals. By using FO filters, which can be considered as a FO version of the dynamic surface control (DSC) method, the so‐called explosion of complexity is avoided and the discontinuity problem of FO derivatives of virtual controllers due to outputs quantization is resolved. Observers are designed to estimate the unmeasured states. Also, function approximators (FAs) are employed to approximate uncertainties. A novel lemma that correlates observer states to consensus errors and their constraints, FO filters errors, adaptive parameters and backstepping error surfaces, has been proposed to establish the observer stability. To reduce the communication burden between the controllers and actuators, relative threshold ET mechanisms have been applied to control signals. The combination problem of unmeasured states with the mentioned input constraints for SFOMASs is coped with another novel lemma. Utilizing the common Lyapunov functions (CLFs) and the backstepping technique, virtual controllers, adaptive law and control signal for each agent have been designed and boundedness of closed‐loop signals and avoidance of Zeno behavior have been proved. The effectiveness of the proposed control scheme is demonstrated through a simulation study. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a prescribed performance model‐free adaptive platooning sliding mode control (PP‐MFAP‐SMC) problem for the nonlinear vehicular platooning systems (VPSs) under denial‐of‐service (DoS) attacks is studied. Firstly, the partial form dynamic linearization (PFDL) technique is employed to convert the nonlinear VPSs into an equivalent linear data model, in which the nonlinear features of the VPSs are compressed into an unknown time‐varying pseudo gradient (PG) vector. Then, an observer is devised to acquire the estimation value of the unknown time‐varying PG vector. To lower the complication of the design, the constrained tracking error is converted into the unconstrained one. Based on which, the sliding mode control (SMC) strategy is proposed to enhance the robustness of the VPSs. Further, a PP‐MFAP‐SMC algorithm with an attack compensation mechanism is developed to ensure that the vehicular tracking errors of the position and velocity can converge to the predefined regions, respectively. Eventually, the effectiveness of the developed algorithm is demonstrated by an actual VPS with the comparisons. [ABSTRACT FROM AUTHOR]

Summary: The primary objective of this paper is to enhance the efficient utilization of communication resources. To achieve this, the paper delves into the disturbance observer based adaptive heading control strategy for Unmanned Marine Vehicles with event‐triggered and input quantization. Furthermore, in order to mitigate the impact of slow time‐varying external disturbances within the control system, the disturbance observer is employed for estimation. Within the context of a networked control, control input is subjected to quantization via an input quantizer, and the process of input quantization is described by using a linear analytical model. Importantly, no foreknowledge of quantization parameters is necessary for the quantized feedback controllers. Subsequently, the sliding mode control method is combined with event‐triggered mechanismss to design quantization feedback control system. The stability of the closed‐loop system is established in line with the fundamental tenets of Lyapunov stability theory, validating the bounded nature of both observation and heading tracking control errors. The effectiveness of the proposed heading control scheme is further underscored through a series of simulation experiments. [ABSTRACT FROM AUTHOR]

ABSTRACT This article investigates the distributed adaptive multi‐lane fusion control for two‐dimensional (2‐D) plane vehicle platoons with variable distance constraints, input hysteresis, and angle sensor faults. Variable distance constraints occur within a limited time interval (VDCOLT), which limits vehicle spacing within a variable range during preset time intervals while keeping the vehicle spacing unconstrained at other times. Combined with a transformed error function, a new shifting function is introduced to convert the constrained spacing error variables into the unconstrained variables while keeping the unconstrained variable unchanged. Designing the position controller ensures that the distance satisfies VDCOLT and reduces the adverse effects caused by unknown signs of input hysteresis by using a Nussbaum function. Moreover, a new angle controller based on the hyperbolic tangent function is designed to solve the problem of angle sensor faults. Furthermore, the stability of the vehicle platoon is achieved through the backstepping control and sliding‐mode control methods. Finally, a numerical simulation is provided to verify the proposed techniques. [ABSTRACT FROM AUTHOR]

ABSTRACT This study focuses on addressing the challenge of adaptive finite‐time control for nonstrict‐feedback nonlinear systems subject to input delay and saturation. Neural networks (NNs) are utilized to handle unknown nonlinear functions, and Padé approximation is employed to effectively manage input delay. To mitigate the issue of “explosion of complexity,” the command filter method is applied. By leveraging command filter technology and backstepping technique, an adaptive finite‐time control scheme is developed using NN approximation. The proposed control scheme demonstrates that the closed‐loop signals achieve semi‐global practical finite‐time stable (SGPFS), ensuring that the tracking error converges within a finite time to a small region around the origin. The effectiveness of the proposed scheme is validated through two simulation examples. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposes a novel finite‐time adaptive continuous robust control method for buck converters, combining dynamic sliding mode theory with a mismatched disturbance observer. While traditional sliding mode control can effectively manage matched disturbances, it often falls short when faced with mismatched disturbances, potentially compromising system performance. Our developed control strategy introduces a nonlinear sliding mode‐based disturbance observer and an adaptive continuous robust sliding mode control to guide state variables towards desired values. To mitigate the chattering phenomenon, the proposed controller incorporates a super‐twisting algorithm, ensuring continuous control command behavior. We conducted a rigorous finite‐time stability analysis of the control procedure using Lyapunov theory. Furthermore, comprehensive experimental results demonstrate that our proposed control method significantly reduces disturbance effects and enhances the buck converter's performance in terms of robustness and accuracy. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, a hierarchical distributed model‐free adaptive fault‐tolerant vehicular platooning control scheme for nonlinear vehicular platooning systems (VPSs) is studied. First of all, the dynamic linearization technique (DLT) is utilized to acquire an equivalent linear data model for nonlinear VPSs. Secondly, a hierarchical control structure is developed to realize the vehicular platooning tracking control task, which consists of the upper‐layer adaptive distributed observer and the under‐layer decentralized model‐free adaptive vehicular platooning tracking controller. In order to make each follower vehicle get the leader's information, the adaptive distributed observer is employed to get the estimation value of leader's information. In addition, for the purpose of ensuring the safety of driving, the radial basis function neural network (RBFNN) algorithm is utilized to address the problem of sensor failures. Based on this, a novel hierarchical distributed model‐free adaptive fault‐tolerant vehicular platooning control scheme is designed to achieve simultaneous tracking of vehicular position and speed. Lastly, the validity of the theoretical control scheme is demonstrated through a realistic and detailed simulation example of a VPS. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the impact of adaptive cruise control on energy consumption in real-world driving conditions based on a natural experiment: a large-scale observational dataset of driving data from a diverse fleet of vehicles and drivers. The analysis is conducted at two different fidelity levels: (1) a macroscopic trip-level benefit estimate that compares trips with and without cruise control in a counterfactual way using statistical methods, and (2) a situation-based comparison achieved through the segmentation of trips into distinct driving situations such as acceleration, braking, cruising, and other maneuvers. The results of this research show that the effect of cruise control on energy consumption varies across different driving situations and levels of analysis. In a macroscopic trip-level analysis, cruise control engagement is associated with a slight increase in fuel consumption across the fleet. As revealed later by the situation-based analysis, this result can be attributed to the negative impact of cruise control on energy consumption in cruising mode, which is the most common driving situation. However, the situation-based comparison demonstrates that cruise control can provide fuel consumption benefits in situations involving acceleration and braking, particularly when a preceding vehicle is present. The study also emphasizes the importance of controlling for various factors that can influence both fuel consumption and the likelihood of cruise control engagement to properly evaluate its effects. Efficient automated driving technology to improve upon human driving behaviour offer a promising pathway to energy and fuel savings. Here, authors analyse real-world data to determine how adaptive cruise control affects fuel consumption and identify specific driving situations where it can be optimized. [ABSTRACT FROM AUTHOR]

Traditional Proportional-Integral-Derivative (PID) control systems often encounter challenges related to nonlinearity and time-variability. Original dung beetle optimizer (DBO) offers fast convergence and strong local exploitation capabilities. However, they are limited by poor exploration capabilities, imbalance between exploration and exploitation phases, and insufficient precision in global search. This paper proposes a novel adaptive PID control algorithm based on enhanced dung beetle optimizer (EDBO) and back propagation neural network (BPNN). Firstly, the diversity of exploration is increased by incorporating a merit-oriented mechanism into the rolling behavior. Then, a sine learning factor is introduced to balance the global exploration and local exploitation capabilities. Additionally, a dynamic spiral search strategy and adaptive -distribution disturbance are presented to enhance search precision and global search capability. The BPNN is employed to fine-tune both PID and network parameters, leveraging its powerful generalization and learning ability to model nonlinear system dynamics. In the simplified motor experiments, the proposed controller achieved the lowest overshoot (0.5%) and the shortest response time (0.012 s), with a settling time of 0.02 s and a steady-state error of just 0.0010. In another set of experiments, the proposed controller recorded an overshoot and response time of 0.7% and 0.0010 s, across five DC motor tests. These results demonstrate the proposed adaptive PID control algorithm has superior performance in optimizing control system parameters, as well as improving system robustness and stability. [ABSTRACT FROM AUTHOR]

Distributed cooperative control strategies for DC microgrids have been rapidly evolving in recent years. However, introducing a cyber layer to enhance robustness, scalability, and reliability also exposes the system to potential cyber‐attacks. The damage inflicted by such attacks on the system performance can be catastrophic, reaching a point where it may devastate the system normal operation. By using model reference adaptive control (MRAC), this article proposes a resilient approach that does not require an accurate model of the system and despite the uncertainties for detecting false data injections into the reference DC voltage and simultaneously mitigating their adverse effects on the system stability and performance. The proposed technique employs an observer to detect possible false data injections in an online manner. By emulating the behavior of an ideal reference model, the MRAC ensures adaptive adjustments of the control parameters over time to mitigate the negative effects of potential attacks effectively and despite non‐idealities such as measurement noise, parameter variations, and environmental changes in DC microgrids, the MRAC effectively manages false data injection attacks. Simulation studies are conducted using diverse scenarios involving a three‐node DC microgrid to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the complex maritime navigation environment, Unmanned Surface Vessels (USVs) are influenced by unknown nonlinear dynamics arising from external disturbances and internal uncertainties. Achieving effective formation control while maintaining obstacle avoidance performance presents significant challenges. This article proposes a Neural Networks (NNs) adaptive formation Artificial Potential Field (APF) obstacle avoidance control method for multiple USVs. By employing online updates of Radial Basis Function (RBF) NNs technology, the unknown nonlinear dynamics are approximated, thus addressing complex nonlinear dynamics problems. In scenarios involving multiple USVs navigating under high wind and wave conditions, collisions with obstacles frequently occur. To tackle this issue, a leader-follower control strategy is designed that effectively addresses risk assessment and obstacle avoidance under such challenging conditions. Additionally, to account for saturation constraints or potential faults in the controller inputs commonly encountered in engineering applications, it implements an asymmetric auxiliary control system. Furthermore, the Lyapunov stability theorem is utilized to ensure the stability of both the formation control and obstacle avoidance algorithms for multiple USVs. Finally, the effectiveness of the proposed algorithm is validated through simulations. [ABSTRACT FROM AUTHOR]

The simultaneous monitoring of both the process mean and dispersion has gained considerable attention in statistical process control, especially when the process follows the normal distribution. This paper introduces a novel Bayesian adaptive maximum exponentially weighted moving average (Max-EWMA) control chart, designed to jointly monitor the mean and dispersion of a non-normal process. This is achieved through the utilization of the inverse response function, particularly suitable for processes conforming to a Weibull distribution. To assess the effectiveness of the proposed control chart, we employed the average run length (ARL) and the standard deviation of run length (SDRL). Subsequently, we compared the performance of our proposed control chart with that of an existing Max-EWMA control chart. Our findings suggest that the proposed control chart demonstrates a higher level of sensitivity in detecting out-of-control signals. Finally, to illustrate the effectiveness of our Bayesian Max-EWMA control chart under various Loss Functions (LFs) for a Weibull process, we present a practical case study focusing on the hard-bake process in the semiconductor manufacturing industry. This case study highlights the adaptability of the chart to different scenarios. Our results provide compelling evidence of the exceptional performance of the suggested control chart in rapidly detecting out-of-control signals during the hard-bake process, thereby significantly contributing to the improvement of process monitoring and quality control. [ABSTRACT FROM AUTHOR]

ABSTRACT Bi‐directional power exchange between the lithium‐ion battery and the grid can be achieved using vehicle‐to‐grid (V2G) technology. Bi‐directional battery chargers for V2G applications present opportunities and challenges, as the rapid growth of active components can compromise system reliability. This paper proposes an adaptive control mechanism for a three‐phase bi‐directional battery charging system to address this. The system integrates a bi‐directional active front‐end (AFE) converter (BAC) with an interleaved buck‐boost converter to enable adaptive constant current/constant voltage (CC/CV) mode operation and bi‐directional power flow. A feedforward decoupled current controller and pulse width modulation scheme optimize battery charging, while an optimized voltage‐oriented control (OVOC) model‐based controller ensures stable bi‐directional power exchange between the electric vehicle (EV) and the utility. Experimental results validate the effectiveness of the proposed approach, with findings demonstrating low total harmonic distortion (THD), unity power factor (UPF), and minimal DC voltage ripple. A 12.5 kW prototype further confirms the practical viability of the design, offering a promising solution for enhanced reliability and performance in V2G battery chargers. [ABSTRACT FROM AUTHOR]

Nowadays, the nylon ropes have been widely applied in many different areas such as fishing, hiking, hoisting, and pull traction. Presently, there are some commercial machines available for nylon rope winding onto a shipping reel before shipment. However, they still need manual operation relying on rapid movement of the hands, also requiring full coordination with the winding action. This operation procedure makes it very difficult to continuously achieve the winding process fast and accurately due to high dependence on human operation but also some forms of diameter deviation occurred from the chemical process. In reality, even a slight human mistake may result in uneven winding outcome. Therefore, an adaptive reeling schedule method using a simple algebra computation is developed to resolve this problem. For this reason, an automatic rope winding control system to adaptively suit various rope diameters with an error involved is developed using adaptive decision-making (ADM) algorithm based on a simple arithmetic operation. First, the winding turn in each layer of drum is obtained from the quotient of the drum length divided by average rope diameter of each layer. Second, through the accumulated winding turn divided by the winding turn of a layer, the quotient represents the number of winding layers, and the remainder is the current winding turn of the layer. Third, the turning location to instantly swift to forward or backward winding direction is therefore determined when the winding turn reaches the target of each layer. The proposed model is realized on the basis of automation mechanism integrated with programming logical controller (PLC), human machine interface (HMI), optical micrometer, server motor, linear motor, tension sensor and optical motor, etc. The experimental results verify that the proposed system can reach 100% reality performance in the rope diameters of 3.8 mm, 4.0 mm, and 6.5 mm. Compared with the traditional winding method taking 15 min, the operation time is reduced to 11 min. Accordingly, it is confirmed that the proposed system is superior to traditional human-handling machines in term of rapidness, robustness, and accuracy. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, we develop a novel adaptive extremum‐seeking control (AdESC) algorithm with robustness guarantees and without persistence of excitation (PE). Specifically, this builds on a proportional‐integral (PI)‐like parameter estimator. A zeroth‐order optimization framework is used, where the optimizer/agent can only query the numerical value of the cost function at the current coordinate given an unmodeled bounded disturbance. Since parameter estimation plays a decisive role in the stability and convergence properties of AdESC algorithm, it is also well established in the existing literature that to ensure parameter convergence a stringent PE condition is required. Here, we eliminate the need for a stringent PE condition by utilizing a novel set of weighted integral filter dynamics, while ensuring sufficient richness using a milder condition, called initial excitation (IE). Moreover, to validate the robustness guarantees towards unmodeled bounded disturbance, a detailed Lyapunov function based analysis is performed to establish the closed‐loop stability and convergence in the form of uniform ultimate boundedness (UUB). Furthermore, an experimental study using a unicycle wheeled mobile robot (WMR) is carried out as a proof‐of‐concept considering disturbance and disturbance‐free scenarios. [ABSTRACT FROM AUTHOR]

Vehicle platooning improves energy savings via vehicle-to-vehicle (V2V) communication. Ecological cooperative adaptive cruise control (Eco-CACC) is implemented in platoons for merging task by using regrouped platoon models. The merging positions are selected in the middle and tail of an original platoon with a two-vehicle sub-platoon. The distributed nonlinear model predictive controller based on signal temporal logic (DNMPC-STL) approach is developed to model the Eco-CACC merging strategy. The performance of the Eco-CACC merging strategy is modeled by objective control for a predecessor-leader following (PLF) topology. The results demonstrate that merging positions located in the tail exhibit superior performance and can be used to improve stability, tracking performance, energy consumption efficiency and SOC of battery. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, an event‐triggered adaptive neural network controller based on threshold band is designed for a four wheels independently steered and four wheels independently driven (4WS4WD) mobile robot. The 4WS4WD mobile robot is attracting attention for its excellent motion performance such as manipulation versatility and posture flexibility. However, its control difficulty is increased due to its characteristics of being controlled by eight motors for steering and driving respectively. Also, since the robot itself has limited computing and communication resources, real‐time control cannot be guaranteed. To copy with that, the kinematic and dynamics models are first introduced for the 4WS4WD mobile robot. Second, an adaptive neural network controller with low‐frequency learning rate is utilized to control the mobile robot since there are unknown perturbations in the model. It can maintain system stability while handing unidentified model perturbations. The stability of the controller is demonstrated by Lyapunov stability analysis. An event‐triggered based on threshold band is suggested to lessen the amount of computation in the control process. Finally, the simulation outcomes further demonstrate how the suggested approach can greatly lessen the computational and communication cost while maintaining control performance. [ABSTRACT FROM AUTHOR]

The hysteresis characteristics related to the frequency and amplitude of the control signal seriously affect the precision of the displacement tracking control of the macro-fiber composite (MFC) bimorph. The traditional feedforward compensator tends to exhibit low precision in controlling displacement. Although it enhances the control accuracy to a certain extent by incorporating a feedback controller, the existing feedback controller has a weak adaptive ability. Thus, the Q-learning (QL) algorithm is combined with the Bouc-Wen (BW) feedforward compensator in this study. The output voltage from the BW compensator has a more significant impact on the control accuracy and convergence speed of the QL algorithm. Therefore, this paper proposes an improved QL (IQL) algorithm that leverages the control error. Moreover, a BW-IQL adaptive control method is proposed to realize precise adaptive control of the MFC bimorph. Experimental comparisons are performed with the proposed method and the traditional BW, BW-PID, and BW-fuzzy PID controllers. The BW-IQL controller reduces the average relative errors of the three controllers by 86.9%, 59.9%, and 41.8%, respectively. Meanwhile, the Q̄ table plays a major role in error suppression in the IQL controller. These results verify that the BW-IQL control method has higher adaptability and accuracy. [ABSTRACT FROM AUTHOR]

The coupling model between unwinding tension and various factors is crucial for understanding warp beam unwinding characteristics and designing tension control systems. However, currently established models are simple and fail to reflect the complexity of the actual unwinding process. Additionally, the traditional unified PID control method for multiple warp beams is difficult to ensure stable unwinding tension for single warp beam, negatively impacting the quality and efficiency of the sizing process. To address these issues, coupling models between tension, radius, speed, turns and air pressure during the warp beam unwinding process were established and validated using constructed detection devices. Experimental results showed that the four types of measured warp beams fit the three coupling models with R 2 values exceeding 90%, confirming the accuracy of these models. Subsequently, a novel fuzzy adaptive PID control system for single warp beam unwinding tension was proposed based on these models. Unlike traditional methods that measure tension directly, the system utilizes unwinding turns detection device to monitor the real-time changes. Simulation results confirmed that the proposed system maintains constant tension under different reference tensions and unwinding radius. Compared to traditional PID control, it significantly reduces tension fluctuation, with reductions of 64.49% under different speed step disturbances and 23.66% under noise disturbances. The validated models and proposed tension control system in this paper provide a solid theoretical foundation and practical guidance for optimizing the new generation of sizing machines. [ABSTRACT FROM AUTHOR]

Electro-hydraulic actuators offer advantages over traditional hydraulic actuators, particularly for high-precision force or position control, but their nonlinear dynamics complicate modelling and control. This study proposes a predictor-integrated adaptive controller to address these challenges. By combining a proportional-integral-derivative controller with a sliding surface, the system tracks desired trajectories, while a parameter optimization rule minimizes errors. Additionally, a smart grey prediction model with dynamic step size adjusts parameters and generates control signals to reduce noise and disturbances. Simulations show improved accuracy and control performance, with a root mean square error reduction of over 50% compared to traditional control algorithms. This adaptive approach ensures precise control in varying conditions, making it suitable for aerospace, automotive, and robotics applications. [ABSTRACT FROM AUTHOR]