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In this paper, a new dynamic event-triggered control solution is presented for state-constrained strict-feedback nonlinear systems. The current barrier Lyapunov function is required to assume or remove the feasibility conditions for virtual control signals. In this paper, a new scheme is proposed to guarantee the designed virtual control signals satisfy feasibility conditions. By introducing a dynamic variable into the event-triggered schedule, the dynamic event-triggered control scheme is established by using backstepping iterative design procedure. Moreover, the theoretical analysis illustrates that the proposed control solution can ensure the boundedness of all signals in SNS. Finally, simulation example is given to showcase the availability of the developed method. [ABSTRACT FROM AUTHOR]

As autonomous vehicles (AVs) and human-driven vehicles (HVs) are expected to share the road for the foreseeable future, understanding how to improve the stability of mixed-autonomy platoons is crucial. This paper introduces a novel adaptive control strategy tailored for a specific platoon configuration termed as " 1 + n + 1 ", consisting of a leading AV, n intervening vehicles, and a trailing AV. Utilizing vehicle-to-vehicle communication, the trailing AV adapts to real-time traffic states, thereby promoting overall platoon stability. Our analysis, grounded in Lyapunov theory, demonstrates that stabilizing only the trailing vehicle is sufficient to ensure the entire system reaches a stable state. To mitigate the negative effects of sensor and actuator uncertainties, we also introduce a corrective signal framework capable of nullifying adverse inputs. Numerical experiments validate the effectiveness of the proposed strategy in platoon control, which can also be adapted to other platoon configurations. Additionally, the performance of this strategy on macroscopic traffic flow is explored, suggesting substantial potential throughput increases compared with the benchmark strategy of Cooperative Adaptive Cruise Control (CACC). [ABSTRACT FROM AUTHOR]

This paper considers output-based adaptive event-triggered control of saturated system with the dynamic anti-windup compensator. An adaptive event-triggered condition with a time-varying threshold function is proposed, under which the system has better performance in saving communication resources. A dynamic anti-windup compensator is employed to overcome the potential performance degradation caused by input saturation. For the prescribed dynamic anti-windup compensator, an optimization problem is proposed for enlarging the domain of attraction. Moreover, if dynamic anti-windup compensator is not given in advance, co-design of the dynamic anti-windup compensator and adaptive event-triggered condition is developed. In addition, the Zeno behavior is excluded by calculating the minimum triggering time interval. Finally, the theoretical result is verified by numerical simulation. [ABSTRACT FROM AUTHOR]

In this article, an adaptive prescribed performance tracking control scheme is proposed for switched nonlinear systems with output dead zone and unmeasured state variables using an adaptive fuzzy approach. Fuzzy logic systems are utilized to learn the unknown nonlinear functions. The output nonlinearity is resolved via introducing Nussbaum function. The novelty of this article is that a shift function is utilized to break the strict restriction that the initial value of the tracking error must be within the initial value of the finite-time performance function. In addition, a switched observer is adopted to reduce the conservativeness caused by the use of a common observer. Then, by combining the average dwell time scheme and the backstepping technology, a novel observer-based fuzzy adaptive controller is developed, which can assure that all the closed-loop signals of the switched systems are bounded under a type of slowly switching signals and the tracking error converges to a pre-specified range in finite time even if the initial value of the tracking error is greater than the performance function. Finally, the simulation results are shown to verify the feasibility of the presented control scheme. [ABSTRACT FROM AUTHOR]

Cold atom traps are at the heart of many quantum applications in science and technology. The preparation and control of atomic clouds involves complex optimization processes, that could be supported and accelerated by machine learning. In this work, we introduce reinforcement learning to cold atom experiments and demonstrate a flexible and adaptive approach to control a magneto-optical trap. Instead of following a set of predetermined rules to accomplish a specific task, the objectives are defined by a reward function. This approach not only optimizes the cooling of atoms just as an experimentalist would do, but also enables new operational modes such as the preparation of pre-defined numbers of atoms in a cloud. The machine control is trained to be robust against external perturbations and able to react to situations not seen during the training. Finally, we show that the time consuming training can be performed in-silico using a generic simulation and demonstrate successful transfer to the real world experiment. The preparation and control of atomic clouds which are commonly used in scientific and technological applications is a complex process. Here, authors demonstrate reinforcement learning as a flexible and adaptive approach to control of a cold atoms trap, opening an avenue to robust experiments and applications. [ABSTRACT FROM AUTHOR]

Tracking is a crucial problem for nonlinear systems as it ensures stability and enables the system to accurately follow a desired reference signal. Using Takagi–Sugeno (T–S) fuzzy models, this paper addresses the problem of fuzzy observer and control design for a class of nonlinear systems. The Takagi–Sugeno (T–S) fuzzy models can represent nonlinear systems because it is a universal approximation. Firstly, the T–S fuzzy modeling is applied to get the dynamics of an observational system in order to estimate the unmeasurable states of an unknown nonlinear system. There are various kinds of nonlinear systems that can be modeled using T–S fuzzy systems by combining the input state variables linearly. Secondly, the T–S fuzzy systems can handle unknown states as well as parameters known to the indirect adaptive fuzzy observer. A simple feedback method is used to implement the proposed controller. As a result, the feedback linearization method allows for solving the singularity problem without using any additional algorithms. A fuzzy model representation of the observation system comprises parameters and a feedback gain. The Lyapunov function and Lipschitz conditions are used in constructing the adaptive law. This method is then illustrated by an illustrative example to prove its effectiveness with different kinds of nonlinear functions. A well-designed controller is effective and its performance index minimizes network utilization—this factor is particularly significant when applied to wireless communication systems. [ABSTRACT FROM AUTHOR]

Upper limb prostheses are commonly propelled by pneumatic artificial muscles organized in an antagonistic arrangement. Nonetheless, the control of upper limb prostheses under changing/unknown situations is difficult and necessary for a variety of real-world applications. Adaptive control, learning-based control, and robust control have been studied to deal with such challenges. However, their adaptability is insufficient for prostheses used in daily life, which are exposed to variable task levels, user motor characteristics, and prosthetic features. This paper introduces a highly adaptive controller for the first time based on Generative Adversarial Nets and proportional–integral–derivative controller (G-PID controller). G-PID controller comprises a generator for generating compensation actions to enhance PID responsiveness when controlling the unknown/changing system. Moreover, it incorporates a discriminator that receives responses from both a user-preselected reference system and the compensated changing/unknown system, and simultaneously determines the source of these responses. Through continuous updates, the compensator modifies the response of unknown/changing system to align with the reference system, thereby facilitating adaptive control. The G-PID controller's effectiveness is evaluated through 1-degree of freedom (DoF) joint and 2-DoF shoulder prostheses in simulation experiments, and further validated in prototype experiments focusing on online learning for unknown and time-varying payload. The results demonstrate its ability to deal with diverse types of unknowns/changes, marking a significant advancement towards incorporating prostheses seamlessly into daily life. [ABSTRACT FROM AUTHOR]

Laser directed energy deposition (LDED), a class of additive manufacturing (AM) processes, has immense potential to be used for various engineering applications to build components with medium to high complexity. However, dimensional deviations from intended values and inadequate surface quality of the built parts limit its wide deployment. The present work reports the development of an adaptive tool path trajectory platform to correct the dimensional inaccuracies in-situ to build high-quality components using LDED. The study deploys a laser line scanner to scan the part after the deposition of the definite number of layers followed by the detection of concave, convex, and flat surfaces using deep learning. Further, a novel adaptive trajectory planning algorithm is deployed by using three different strategies to control material deposition on concave, convex, and flat surfaces. The material deposition is controlled by using adaptive scanning speed control (ASSC), and a combination of laser on–off and scanning speed control (ASSLC). Subsequently, the built geometries are subjected to geometric, microstructure, and mechanical characterizations. It is observed that the deviation of the part was reduced by 30%, and 27.5% using ASSC and ASSLC, respectively. The structures built using the three strategies show some micropores at isolated locations. The microstructure is mainly cellular under all conditions with a similar average microhardness of ~ 210 HV. The study provides an integrated and comprehensive approach for building high-quality large-scale components using LDED with minimal dimensional deviation from the original CAD model. [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]

Fuel cell vehicles are a reliable solution to address energy shortages. However, when the road conditions are complex, the system distributes power unevenly between fuel cells and lithium batteries, and cannot effectively absorb the energy generated by braking. In response to this issue, an adaptive control strategy is adopted to allocate the required power of the car to two types of batteries in real time. Fuzzy logic is used to continuously optimize the relevant parameters of the controller based on the vehicle state, and a multi-island genetic algorithm is used to optimize the control strategy, enhancing the global search ability of the control strategy and increasing the vehicle's ability to absorb and reuse the energy generated by braking. The experiment findings denote that the optimized control strategy increases the remaining capacity of lithium batteries by an average of 1.67%, increases energy recovery by an average of 135 W, increases the overall energy recovery rate by an average of 2.8%, and reduces vehicle fuel consumption by an average of 0.24 L/100 Km. It can be concluded that the optimized adaptive fuzzy control strategy can reduce the probability of over-charging and discharging of lithium batteries and improve the battery life. Meanwhile, the optimized strategy can improve the energy reuse rate, reduce vehicle fuel consumption, lower usage costs. The optimized strategy provides a reference for subsequent research on energy management of fuel cell vehicles. [ABSTRACT FROM AUTHOR]

To improve the motivation and enthusiasm of subjects during active rehabilitation training, this paper proposes a novel training task planning-based adaptive assist-as-needed (TTP-AAAN) control algorithm for an upper limb exoskeleton. The overall controller contains an outer control loop to determine the required assistive force, and an inner control loop to drive the exoskeleton to track subject motion and to provide desired assistive force obtained from the outer control loop. In the outer control loop, a motion intention and task performance evaluation (MITPE) strategy is established to learn the motor capability of the subject. Based on the obtained evaluation result, the radius and frequency of multi-periodic trajectory tracking task, and the gain of the assistive force are adaptively adjusted by using the adaptive central pattern generator (ACPG) algorithm. Then, in the inner control loop, an asymmetric barrier Lyapunov function-based adaptive output feedback (ABLF-AOF) controller, in combination with a neural network (NN) state observer, is developed. The exoskeleton tracking errors are constrained by the asymmetric barrier Lyapunov function, and the state variables and uncertainty terms of the exoskeleton are simultaneously estimated by the NN state observer. Experiments are carried out with an upper limb exoskeleton to demonstrate the effectiveness of the proposed control strategy. The experimental results show that the developed control scheme can provide assistance and achieve task parameter adaption for the subjects with different motion patterns. In addition, the proposed controller has better training performance than task performance-based adaptive velocity assist-as-needed (AAN) controller and minimal AAN controller. [ABSTRACT FROM AUTHOR]

This paper investigates a finite-time adaptive fuzzy prescribed performance fault-tolerant control (FTC) issue for the steer-by-wire vehicle (SBWV) systems with intermittent actuator faults. Different from the steer-by-wire (SBW) system studied by the previous literatures, the SBWV system involved in this study consists of a vehicle dynamics model and an SBW system, including unmeasurable states and unknown nonlinear dynamics. Fuzzy logic systems (FLSs) are first used to identify the unknown model dynamics, and a fuzzy state observer is constructed to estimate the unmeasured states. Then, to compensate for the influence of intermittent actuator faults, a novel finite-time output-feedback prescribed performance adaptive FTC scheme is developed by using the adaptive backstepping control methodology and co-designing the last virtual controller. The presented control scheme not only guarantees that all signals of the closed-loop system are bounded in the presence of actuator faults, but also ensures that the tracking error converges to a small neighborhood of the zero within the prescribed performance bounded. The computer simulation and comparison results demonstrate the effectiveness of the proposed fuzzy control algorithm. [ABSTRACT FROM AUTHOR]

In order to suppress the influence of uncertain disturbances on the trajectory tracking of hydraulic manipulator, a composite control strategy for the cutting electro-hydraulic driving system (CEHDS) of the tunneling robot is presented, which synthesizes the advantages of neural networks technique, recursive backstepping and adaptive control theory. The Lagrangian model with actuator dynamics is derived based on the practical tunneling robot. The back-stepping method is utilized for the strictly feedback state-space model. To address the matched and unmatched lumped uncertainties, the radial-basis-function neural networks (RBFNNs) are employed to approximate the unmatched term which contains the nonlinear friction torque and external cutting load in the mechanical subsystem. The nonlinear disturbance observer (NDOB) is utilized to estimate the matched lumped uncertainty in the hydraulic subsystem. Simultaneously, the adaptive robust mechanism is proposed to compensate the residual disturbances. Based on the Lyapunov theorem, the stability and the bounded tracking error of the CEHDS are obtained. The simulation and experimental results validate the effectiveness of the proposed method in comparison with the common backstepping and PID-controller approaches. [ABSTRACT FROM AUTHOR]

The study targets uncertain coupling faults in robotic arm actuators and proposes a new fault-tolerant visual servo control strategy. Specifically, it considers both multiplicative and additive actuator faults within the dynamic of the robotic arm, treating the coupling faults and time-varying disturbances as an aggregate of concentrated uncertainties. A radial basis function neural network-based state observer is introduced to online approximate these concentrated uncertainties, which include fault information, eliminating the need for prior knowledge of faults. Furthermore, a fault-tolerant controller based on a non-singular fast terminal sliding mode is proposed, which separately decouples the nominal quantities and concentrated uncertainties and develops individual adaptive control laws for each. This effectively reduces the detrimental impact of coupled faults and disturbances on the system's performance, facilitating image feature trajectory tracking control with minimal jitter, high precision, and strong transient response capabilities. The stability of the state observer and the fault-tolerant controller has been substantiated through Lyapunov's theory. Lastly, numerical simulations validate the efficacy and robustness of the proposed fault-tolerant visual servo control approach. [ABSTRACT FROM AUTHOR]

This paper is devoted to the observer-based adaptive robust control for fractional-order Takagi–Sugeno (T-S) fuzzy systems with input uncertainties and output disturbances. By combining system states and output perturbations as new state variables, an augmented fuzzy system whose state variables are unknown is built. Furthermore, an observer is devised to simultaneously estimate unmeasurable system states together with unknown external disturbances. Two stability theorems are derived to prove the asymptotic stability of the error system based on linear matrix inequalities and Lyapunov stability theory. Finally, simulation results are provided to demonstrate the effectiveness of the designed method. [ABSTRACT FROM AUTHOR]

This work presents a complete workflow for the parameter identification of an MBS model representing a vibratory conveying system. First, the MBS model built within the multibody dynamics tool HOTINT is shown. Essential components, such as contact modeling and adaptive time step control, are explained in detail. The model includes a considerable amount of parameters that need to be identified. Altough, this model is fully deterministic, the complex contact dynamics can cause major effect on results by minor parameter variations. Thus, parameter variations usually show statistical variance, which is characteristic for real conveying processes too. For parameter identification it is important to detect parameters that significantly correlate with results. A method that allows to distinguish significant and non-significant parameters is presented. The application to the multibody system shows that stiffness, restitution and sliding friction of the contact model are the essential parameters. Next, the parameter identification is discussed with particular attention to multimodal distributions, that can occur in conveying systems. Furthermore, the integration of statistics into the deterministic simulation model is discussed. In order to demonstrate the applicability of the proposed methods, a parameter identification is performed based on measurement data of a single part on a real conveying system in the development site of STIWA Automation GmbH. Finally, the validity of the fully parametrized simulation model is shown by investigating geometric adjustments of the conveyor. In order to further verify the identified set of parameters, a conveying process with multiple parts is analyzed and compared in measurement and simulation. [ABSTRACT FROM AUTHOR]

The purpose of this research is to address the problem of synchronization between two identical biological hyperchaotic snap oscillator systems with unknown parameters at a fixed time, as well as to design a new theory for synchronizing two different chaotic systems. Firstly, we designed an adaptive controller using Lyapunov stability and fixed-time stability theory to achieve synchronization between two identical biological hyperchaotic snap oscillator in fixed time under unknown parameters. Among the results obtained, we found that the synchronization time for system states occurred in a very short time compared to results in previous research. Secondly, we extended and generalized synchronization between non-identical chaotic systems by creating a theory for achieving synchronization in which adaptive control and fixed-time stability are combined. Through the new theory, the unknown parameters in chaotic systems were estimated, and the settling time was determined, which turned out to be very short and independent of the initial conditions of the system states. One of what makes the scheme important is that it can be easily applied to most chaotic and even hyperchaotic systems in the shortest possible time. Finally, we gave examples of different systems to test the feasibility and effectiveness of the scheme that was designed. [ABSTRACT FROM AUTHOR]

Metro trains have non-linear load characteristics, which means that the power sent to them gets distorted. Problems are caused by changes in power, swells, harmonics, and other disturbances. In this research, an artificial intelligence-driven control method was used on a unified power quality conditioner (UPQC) to help reduce power quality problems and improve power quality. Three advanced control methods are built and compared using MATLAB Simulink. Some of these methods are the ANN Controller, the NARMA-L2 Controller, and the PI Controller, improved using the Adaptive Lizard Algorithm. The controls' usefulness is judged by how well they lower the total harmonic distortion (THD) in the source current. The results show that all three AI-based controls work much better than the system that was not paid for. The ANN Controller works the best, followed by the NARMA-L2 Controller, and the PI Controller improved with the Adaptive Lizard Algorithm. These AI-driven control methods can enhance power quality and ensure that metro rail systems run smoothly and efficiently, as shown by how well they work. Modern transportation networks need more advanced ways to handle power quality, and this research helps make those solutions come together. [ABSTRACT FROM AUTHOR]

Aerodynamic forces acting on a racing car will impact its handling, stability, and steering characteristics. Oversteering typically occurs in racing cars with a significant front-end downforce. In the process of racing, the car's attitude will change, causing a shift in the distribution of front and rear downforce. This, in turn, will impact the car's handling performance. Therefore, in this study, a set of aerodynamic devices with attitude-adaptive function linked to the suspension is designed to reduce aerodynamic attitude sensitivity. The range of the car's attitude changes, the adjustment ability of the front and rear flaps, and the reasonable matching relationship between different operating conditions and the attack angle of the front and rear flaps are confirmed. In this work, the matching relationship is achieved through the use of multiple groups of linkage mechanisms. The aerodynamic characteristics of the entire car are analyzed and simulated in the lap speed simulation. Results showed that the installation of the device reduces the center of pressure (CoP) movement during braking by 52%, the aerodynamic resistance of the entire racing car during acceleration by 19.5%, and the single lap time by 1.5%, while also inhibiting the generation of aerodynamic torque during roll. [ABSTRACT FROM AUTHOR]

Software-defined perimeters (SDP) revolutionize network security by offering dynamic and adaptive access controls, focusing on user and device authentication to substantially reduce the attack surface. Despite their potential, traditional SDP models grapple with challenges in scalability and component-level security, limiting their effectiveness in complex digital environments. To overcome these limitations, this article introduces SecureSDP, a sophisticated evolution of the SDP framework designed to enhance scalability and bolster security for each network component. SecureSDP stands out for its seamless integration into varied organizational structures, delivering a robust and comprehensive security solution that strengthens the network's defenses across all layers. The key advancement of SecureSDP is evidenced by rigorous experimental evaluations, which demonstrate its superior performance in improving network security and scalability. Specifically, SecureSDP achieved substantial increases in the hardening scores across various tools: 65% in Lynis, 78% in Chef Inspec, and 30% in OpenSCAP for the SDP controller. These enhancements underscore SecureSDP's significant contributions to the field, marking a pivotal step forward in the development of more secure, scalable network infrastructures. [ABSTRACT FROM AUTHOR]

In order to improve the intelligent perception and adaptability of the 6G network, drones joined this challenge. For large-scale long-range Unmanned Aerial Vehicle (UAV), most of the time during normal flight belongs to fixed altitude flight. It is required to sail along the planned optimal path. Whether it can fly along the optimal path is mainly attributed to the tracking problem of horizontal flight trajectory. In order to minimize the UAV horizontal plane tracking error, it is necessary to consider the influence of many factors (such as strong winds, heavy rain, obstacles, etc.). Due to the complexity of High-Altitude environment, these disturbances are uncertain. In addition, there are some dynamic errors in the model of UAV control system, and these errors also have uncertainties. And, due to the change of global planning path coordinates, the control system needs to adjust the set value in real time during AUV horizontal trajectory tracking, and the conventional control algorithm is difficult to meet the requirements. Therefore, firstly, the influence of prediction uncertainty of grey prediction on AUV horizontal track tracking control is used; Then the grey prediction is improved according to the practical application; Ultimately, the control law is designed by combining the grey prediction with the control method. Finally, the grey prediction fuzzy adaptive PID method of UAV flight control is applied to the planned path simulation, and good control results are obtained. [ABSTRACT FROM AUTHOR]

This article presents a novel approach to achieve precise trajectory tracking control for a line-following quadcopter by employing a multilayer type-2 fuzzy Petri nets controller (MT2PNC). The MT2PNC dynamically adapts its parameters based on tracking errors, allowing for real-time adjustments to the quadcopter's tilt angles and flight direction. The effectiveness of the controller is thoroughly evaluated through both simulations and experimental studies. In the experimental study, a camera is integrated into the quadcopter to capture line images, which are then processed using sophisticated image processing algorithms to extract essential line information. This extracted data is subsequently fed into the MT2PNC, enabling the quadcopter to precisely follow the reference line. The simulation and experimental results conclusively demonstrate the superior control efficacy of the MT2PNC, showcasing its remarkable ability to accurately track the quadcopter's trajectory. The proposed control method exhibits great promise for line-following and trajectory-tracking applications, and its practical implementation holds substantial potential. [ABSTRACT FROM AUTHOR]

In this work, the practical implementation of magnetic end effector is presented using magnetic levitation (Maglev) system as a working prototype, due to their similar functionality. Maglev refers to a method by which an object can be suspended with the aid of a magnetic field. The motive of this research is to propose a novel fractional-order enhanced model reference adaptive controller (FOEMRAC)-based approach for controlling the stability of the levitating magnetic objects. FOEMRAC uses modified MIT rule as the adaptation mechanism in this system. The stability analysis of Maglev system has been conducted using Matignon theorem. The simulation is carried out using Quanser Maglev system, and a comparative study is conducted with other existing state-of-the-art approaches. The integral error criterion including integral absolute error, integral square error, and integral time absolute error, and other performance metrics such as rise time, settling time, overshoot, and undershoot have been used to compare the robustness of the controllers under nominal, load disturbance, and parametric uncertain environments. Further, the results are verified on the hardware in real time which reveal that FOEMRAC shows efficient results than other controllers and thus can be used to enhance the performance of the magnetic end effector in real-time environment. [ABSTRACT FROM AUTHOR]

In this study, dynamic formation tracking and interception are performed by controlling multi-agent using a Euler-like Lagrangian model. The purpose is to use the distance-based rigid graph method to control multi-agent, and ultimately achieve dynamic formation tracking and target interception of multi-agent. Initially, distance-based graph stiffness and back-stepping techniques were considered to address the formation control challenge. This method helps achieve the initial expected formation and effectively complete the formation mission. Leaders continue to chase the moving target, while followers stick to the expected arrangement. The leader then precisely tracks the moving target and surrounds it within the formation. By using Lyapunov stability theory with adaptive control, it is ensured that the total distance meets finite and consistent error limits. Finally, the numerical simulation of the interception plan was carried out by 6 multi-agents and 1 target at different times to verify the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

In order to solve the problem of optimal prescribed performance control for unknown dynamic nonlinear systems, an adaptive dynamic programming method based on dynamic event-triggered control strategy is designed. By using Lyapunov stability theory, it is proved that all signals in nonlinear systems are uniformly and ultimately bounded. First, the system under consideration is transformed into an unconstrained system with the prescribed performance by the variable transformation method. Then, the integral reinforcement learning method is used to solve the optimal control problem when the system drift dynamic is unknown. In addition, a dynamic event-triggered control strategy is constructed, which can update the weight estimation and control strategy irregularly, so as to alleviate the problem of excessive data transmission burden when the designed critic neural network approximates the value function. At the same time, Zeno's behavior in the communication process is avoided. Finally, a numerical example is given to verify the validity of the proposed theory. [ABSTRACT FROM AUTHOR]

Based on Takagi-Sugeno(T-S) fuzzy models, this paper investigates practical finite-time(PFET) synchronization of complex networks with a linear coupling and two different kinds of nonlinear couplings, including nonlinear relative state coupling and nonlinear absolute state coupling. A new stability lemma is established based on different time intervals. Two kinds of controllers are designed including semi-intermittent state feedback control and semi-intermittent adaptive control. As a result, with the help of new stability lemma and control schemes, the goal of PFET synchronization is realized via Lyapunov functionals. Eventually, simulation experiments are presented to verify our new results. [ABSTRACT FROM AUTHOR]

This paper aims to investigate an adaptive event-triggered control scheme for unreliable networked control systems with data loss, transmission delay, and time-varying delay described by Takagi–Sugeno (T–S) fuzzy model. In order to achieve higher computational efficiency, better robustness, and higher control accuracy, an adaptive event-triggered controller based a sampling-state error mechanism is proposed. The proposal of this scheme extends the adaptive range of the event-triggered controller. On the basis of linear matrix inequality (LMIs), a new stability criterion is attained under the Lyapunov–Krasovskii functional (LKF) theory. Compared with the previous results, the scheme is less conservative. By using a numerical system and a continuous stirred tank reactor (CSTR) system, the quantitative experimental results indicate that the number of sampling in the controller is reduced, thus verifying the effectiveness and superiority of this control method. [ABSTRACT FROM AUTHOR]

Water conservation has become a global problem as the population increases. In many densely populated cities in China, leaks from century-old pipe works have been widespread. However, entirely eradicating the issues involves replacing all water networks, which is costly and time-consuming. This paper proposed an AI-enabled water-saving control system with three control modes: time division control, flow regulation, and critical point control according to actual flow. Firstly, based on the current leaking situation of water supply networks in China and the capability level of China's water management, a water-saving technology integrating PID control and a series of deep learning algorithms was proposed. Secondly, a multi-jet control valve was designed to control pressure and reduce water distribution network cavitation. This technology has been successfully applied in industrial settings in China and has achieved gratifying water-saving results. [ABSTRACT FROM AUTHOR]

The concept of "smart rotor" is an evolving advancement in wind turbine which enables an intelligent active flow control in rotor. The deformable trailing edge flap (DTEF) is a part of smart rotor concept which implements a customized active load control. The trailing edge flap actuator effectively replaces the tedious blade pitch actuation and conserves the actuation energy required for pitching the entire blade. The DTEFs require a fast computing, anticipatory controller for optimally tuning the flap angle with minimal power compromise. This work analyzes the performance of advanced control strategies like model predictive control (MPC), adaptive MRAC control, and DQ controllers. The MRAC controller is found to reduce the fatigue stress by 40% and the MPC controller damps up to 70% more efficiently than the typical feedback controller. The control strategies are aided by the LiDAR-based preview wind data for the active manipulation of trailing edge flap angle (θ flap) control. The validation of proposed controller is done using power analysis curve and the component fatigue lifetime analysis using MLIFE software. The above analyses are done in NREL Onshore 5-MW FAST wind turbine model which could be interfaced with MATLAB with modified AeroDyn code for active flap deflection. [ABSTRACT FROM AUTHOR]

Brain tumor is an anomalous growth of glial and neural cells and is considered as one of the primary causes of death worldwide. Therefore, it is essential to identify the tumor as soon as possible for reducing the mortality rate throughout the world. However, the classification of brain tumor is a challenging task due to presence of irrelevant features that cause misclassification during detection. In this research, the adaptive manta ray foraging optimization (AMRFO) is proposed for performing an effective feature selection to avoid the problem of overfitting while performing the classification. The adaptive control parameter strategy is incorporated in the AMRFO for enhancing the search process while selecting the feature subset. The linear intensity distribution information and regularization parameter-based intuitionistic fuzzy C-means algorithm namely LRIFCM is used to perform the segmentation of tumor regions. Next, LeeNET, gray-level co-occurrence matrix, local ternary pattern, histogram of gradients, and shape features are used to extract essential features from the segmented regions. Further, the attention-based long short-term memory (ALSTM) is used to classify the brain tumor types according to the features selected by AMRFO. The datasets utilized in this research study for the evaluation of AMRFO-ALSTM method are BRATS 2017, BRATS 2018, and Figshare brain datasets. Segmentation and classification are the two different evaluations examined for the AMRFO-ALSTM. The structural similarity index measure, Jaccard, dice, accuracy, and sensitivity are utilized during segmentation evaluation, while accuracy, specificity, sensitivity, precision, and F1-score are used during classification evaluation. The existing researches namely, transformer-enhanced convolutional neural network, Chan Vese (CV)-support vector machine, CV-K-nearest neighbor, deep convolutional neural network (DCNN), and salp water optimization with deep belief network are used to compare with the AMRFO-ALSTM. The accuracy of AMRFO-ALSTM for Figshare brain dataset is 99.80 which is a greater achievement when compared to the DCNN. [ABSTRACT FROM AUTHOR]

Modern automotive systems with adaptive control features require rigorous analysis to guarantee correct operation. We report our experience in modeling the automotive case study from the ABZ2020 conference using the ASMETA toolset, based on the Abstract State Machine formal method. We adopted a seamless system engineering method: from an incremental formal specification of high-level requirements to increasingly refined ASMETA models, to the C++ code generation from the model. Along this process, different validation and verification activities were performed. We explored modeling styles and idioms to face the modeling complexity and ensure that the ASMETA models can best capture and reflect specific behavioral patterns. Through this realistic automotive case study, we evaluated the applicability and usability of our formal modeling approach. [ABSTRACT FROM AUTHOR]

This paper presents a new optimization approach for solving the under-frequency load shedding (UFLS) problem in power systems. UFLS is a very important function in maintaining the power system within its safe operating limits.It is also the last resort in the event of frequency instability. This paper investigates the use of an adaptive neuro-fuzzy inference system (ANFIS)-based controller. The proposed ANFIS provides optimal response compared to conventional load shedding and other methods. The proposed approach is based on the simulation of the multi-zone load frequency control problem. Rate of change of frequency, voltage deviation and tie-line power transit are used as inputs to the ANFIS. The outputs in the first level are the optimal frequency threshold (Fth) values, and in the second level, they are the quantity of load shedding. Numerical results based on the IEEE 39 power system and the Algerian grid are used to demonstrate the effectiveness of the proposed approach in finding the optimal UFLS control compared with the conventional method as well as the results of the other intelligent method. The comparison showed that the proposed scheme gave the best response in all scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a new direct adaptive control (DAC) technique using Caputo's definition of the fractional-order derivative. This is the first time a fractional-order adaptive law is introduced to work together with an integer-order stable manifold for approximating the uncertainty of a class of nonlinear systems. The DAC approach uses universal function approximators such as multi-layer perceptrons with one hidden layer or fuzzy systems to approximate the controller. This paper presents a new lemma, which elucidates and clarifies the link between the Caputo and the Riemann–Liouville definitions. The introduced lemma is useful in developing a Lyapunov candidate to prove the stability of using the proposed fractional-order adaptive law. This is further explained by a numerical example, which is provided to elucidate the practicality of using the fractional-order derivative for updating the approximator parameters. The main novelty of the results in this paper is a rigorous stability proof of the fractional DAC approach for a class of nonlinear systems that is subjected to unstructured uncertainty and deals with the adaptation mechanism using a traditional integer-order stable manifold. This makes the control scheme easier to implement in practice. The fractional-order adaptation law provides greater degrees of freedom and a potentially larger functional control structure than the conventional adaptive control. Finally, the paper demonstrates that traditional integer-order DAC is a special case of the more general fractional-order DAC scheme introduced here. [ABSTRACT FROM AUTHOR]

The conversion mode control scheme for tilt-rotor aircraft is provided with a switched Takagi-Sugeno (T-S) fuzzy modeling method and an adaptive anti-disturbance reference model tracking technique. In order to describe the dynamic features of the tilting process, a switched T-S fuzzy model is constructed with respect to the inclination angle. Compared to the linearized model, the initial nonlinear characteristics of the mathematical description are retained by the fuzzy modeling process, including the nonlinear disturbance input. Hence, an adaptive controller is designed to eliminate the adverse impact of composite disturbances caused by the rotor effect and the natural wind, which guarantees an L 2 - L ∞ performance of the corresponding error system. Furthermore, a simulation result with respect to the XV-15 tilt-rotor aircraft is given to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper investigates an event-triggered adaptive fuzzy finite-time control issue for the steer-by-wire vehicle (SBWV) systems. Different from the steer-by-wire (SBW) system studied by the previous literatures, the SBWV systems addressed by this study is composed of a vehicle dynamics model and a SBW system and contains the unknown nonlinear dynamics. Fuzzy logic systems (FLSs) are employed to identify the unknown model dynamics, and a relative threshold event-triggered mechanism is established to reduce unnecessary controller updates. Then, by using the finite-time stability concepts and the backstepping with dynamic surface control (DSC) design methodology, a new fuzzy finite-time adaptive event-triggered control scheme is proposed. It is proved that the proposed control scheme ensures the closed-loop system to be stable and the tracking error converges to a small neighborhood of zero within a finite-time interval. The computer simulation results are given to demonstrate the effectiveness of the proposed fuzzy control algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the reference trajectory tracking issue of cooperative robots system with bounded uncertainty is investigated from a novel standpoint of servo constraint-following. The bound of uncertainty is not known but can be characterized by the fuzzy set theory. Then, establishes the dynamical model of cooperative robots system with fuzzy uncertainty. The process of the proposed controller design contains two steps. First, an adaptive robust control is investigated which can render the desired servo constraint to be followed even if there exists uncertainty in the system, and through stability analysis by selecting appropriate Lyapunov function which proves uniform boundedness and ultimate uniform boundedness of tracking error. The second step is to find a parameter selection guideline for the optimal control parameter. Based on the fuzzy description of system uncertainty, a performance index consisting of system performance and control cost can be developed. Then, the selection of optimal control parameters translates into finding the parameter that makes the performance index minimized. Finally, numerical simulations are performed on a two-planar two-link manipulator system for evaluating the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel incipient fault estimation and fault-tolerant control approach for wireless power transfer (WPT) systems with disturbances and incipient sensor faults. Firstly, the dynamic models of the WPT system and incipient faults are established. Then, a k-step incipient fault estimation observer method is analyzed for estimating the states and incipient faults of the system. Based on it, a nonlinear dynamic output feedback fault-tolerant controller is devised to ensure the stability of the WPT system when considering incipient faults and disturbances. Further, the designed controller can monitor the system in real time without the knowledge of the system states. Besides, the stability analysis approach is rigorous, and the proof method can also apply to other similar systems. At last, the simulation example shows that the proposed fault diagnosis method is correct and effective. [ABSTRACT FROM AUTHOR]

Prior research has shown that the sensorimotor cortical oscillations are uncharacteristic in persons with cerebral palsy (CP); however, it is unknown if these altered cortical oscillations have an impact on adaptive sensorimotor control. This investigation evaluated the cortical dynamics when the motor action needs to be changed "on-the-fly". Adults with CP and neurotypical controls completed a sensorimotor task that required either proactive or reactive control while undergoing magnetoencephalography (MEG). When compared with the controls, the adults with CP had a weaker beta (18–24 Hz) event-related desynchronization (ERD), post-movement beta rebound (PMBR, 16–20 Hz) and theta (4–6 Hz) event-related synchronization (ERS) in the sensorimotor cortices. In agreement with normative work, the controls exhibited differences in the strength of the sensorimotor gamma (66–84 Hz) ERS during proactive compared to reactive trials, but similar condition-wise changes were not seen in adults with CP. Lastly, the adults with CP who had a stronger theta ERS tended to have better hand dexterity, as indicated by the Box and Blocks Test and Purdue Pegboard Test. These results may suggest that alterations in the theta and gamma cortical oscillations play a role in the altered hand dexterity and uncharacteristic adaptive sensorimotor control noted in adults with CP. [ABSTRACT FROM AUTHOR]

The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components. Damaging energy bursts in a tokamak are a major obstacle to achieving stable high-fusion performance. Here, the authors demonstrate the use of adaptive and machine-learning control to optimize the 3D magnetic field to prevent edge bursts and maximize fusion performance in two different fusion devices, DIII-D and KSTAR. [ABSTRACT FROM AUTHOR]

An adaptive state-feedback control system is proposed for a class of linear time-varying systems represented in the controller canonical form. The adaptation problem is reduced to the one of Taylor series-based first approximations of the ideal controller parameters. The exponential convergence of identification and tracking errors of such an approximation to an arbitrarily small and adjustable neighbourhood of the equilibrium point is ensured if the condition of the regressor persistent excitation with a sufficiently small time period is satisfied. The obtained theoretical results are validated via numerical experiments. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach to achieve finite-time synchronization (FTS) in a certain class of fractional-order complex-valued neural networks (CVNNs) containing reaction-diffusion terms. The proposed method uses intermittent control and provides a theoretical analysis to establish criteria for achieving FTS. This is achieved through new Lyapunov functions based on the proposed system, deriving inequalities in the complex domain. To realize FTS, the study designs complex-valued intermittent controllers for the targeted CVNNs relying solely on the information obtained from the controlled nodes. Moreover, an adaptive controller is introduced to effectively regulate the control gain, and the FTS of CVNNs is analyzed. The effectiveness of the proposed control strategies and derived results is demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, an online event-triggered self-learning scheme based on adaptive dynamic programming (ADP) is developed to address tracking control design for nonlinear systems with constrained input and uncertain disturbance. Firstly, the value function with non-quadratic function is defined for the augmented nominal system, and the constrained robust tracking problem is equivalent to the optimal control for solving the tracking event-triggered Hamilton–Jacobi–Bellman (ETHJB) equation. Then, a single-critic network is developed to obtain the value function and control law related to the solution of the tracking ETHJB equation, greatly reducing approximation errors and computational costs. To alleviate the requirement for the entire state sampling, we propose a triggering rule that ensures system stability while limiting control updates. Theoretical proof demonstrates that the tracking state of the closed-loop system and the weight approximation error of the neural network are uniformly ultimately bounded (UUB). Finally, two examples are provided to validate the availability of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, the optimal formation control issue with collision avoidance and connectivity assurance is investigated for a class of second-order uncertain nonlinear multi-agent systems. First, the neural networks are employed in order to deal with the unknown nonlinear dynamics of the system. Then, an optimal formation control scheme is developed in the framework of the identifier–actor–critic. By constructing a new performance metric function containing collision avoidance and connectivity constraints, it is demonstrated that asymptotic convergence of the tracking error can be achieved under the proposed control scheme. Finally, the effectiveness of the proposed control method is validated by the numerical simulation example. [ABSTRACT FROM AUTHOR]

The stator field-oriented control (SFOC) strategy, utilizing classical proportional-integral (PI) regulators for the doubly fed induction generator (DFIG) within a dual rotor wind turbine (DRWT) system, encounters several significant challenges. These challenges encompass undesirable fluctuations in stator active and reactive powers, the occurrence of a coupling effect in specific scenarios, and a lack of robustness. Moreover, the conventional SFOC demonstrates suboptimal performance in the presence of grid voltage drop scenarios. To address these issues, this study proposes the application of a backstepping super-twisting control (BSTC) strategy. The design of the controller involves integrating a super-twisting algorithm (STA) term into the control law of the classical backstepping control (BC) approach. The MATLAB simulation tests conducted on a 1500 KW DFIG-based DRWT system illustrate the clear superiority of the proposed BSTC over conventional SFOC, BC, and some previously published control methods. In the reference tracking test, the results indicate a significant reduction in the total harmonic distortion (THD) value of the stator current by 57.14% compared to SFOC and by 33.33% compared to BC. Additionally, the BSTC technique, in the same test, also reduces the steady-state error (SSE) for active power by 60% and 28.57% compared to SFOC and BC, respectively. Concerning reactive power, the proposed BSTC strategy decreases SSE by percentages estimated at 56.25% and 12.5%, respectively, compared to SFOC and BC. The computed percentages illustrate the substantial superiority of the suggested controller in enhancing power system characteristics and elevating the quality of energy. [ABSTRACT FROM AUTHOR]

Memristors have several chaotic dynamic models and have been used successfully in various fields, including secure communication systems, information storage, and artificial neural networks. The memristor-based four-dimensional chaotic (FDMC) systems generate unpredictable and intricate time domain signals. Parameter fluctuations in the FDMC system may give birth to chaos, making it difficult to suppress. Stabilizing chaos in the FDMC system improves the circuit's performance. This paper synthesizes a novel time-efficient chatter-free nonlinear robust adaptive control (NLRAC) technique that stabilizes chaos in the FDMC system affected by time-varying unknown bounded exogenous disturbances and model uncertainties. The proposed NLRAC strategy decimates the time-varying unknown bounded exogenous disturbances and model uncertainties effects; it establishes a faster, smoother state-variable trajectories convergence to the zero vicinity. The theoretical analysis and mathematical proofs are based on the Lyapunov stability technique. Computer simulation results show that the proposed NLRAC technique effectively brings the FDMC system's state-variable trajectories to zero with reduced fluctuations for control input signals and state-variable trajectories. This feedback controller's attribute enhances closed-loop stability performance, improves precision, and reduces risk overshoot. The paper includes comparative computer simulation results to endorse the proposed controller performance. [ABSTRACT FROM AUTHOR]

This paper proposes a novel, robust flight control system using adaptive nonlinear sliding mode control for a quadrotor UAV in the presence of parametric uncertainties and aerodynamical disturbances. The proposed control system is based on a proportional derivative sliding surface combined with a non-singular fast terminal sliding mode control to enhance the tracking accuracy and reduce the chattering influence. Moreover, an adaptive mechanism is proposed to approximate the unknown upper limit of external disturbances/uncertainties. The Lyapunov criteria is used to prove the closed-loop stability and calculate the adaptive mechanism. The proposed adaptive PD-NFTSMC (APD-NFTSMC) can cope with the negative influence of modeling uncertainties, external disturbances, and measurement noise, allows null error in the steady state, and solves the chattering problem. Moreover, intensive simulation experiments under various external conditions are carried out to highlight the sovereignty of the developed control approach. Finally, comparisons with some well-known control techniques are performed to show the usefulness, smoothness, and robustness of the proposed APD-NFTSMC strategy. [ABSTRACT FROM AUTHOR]

In topology optimization of the microchannel heat sink, the minimum width of flow channels are constrained by the mesh size. To reduce the constraint of finite element mesh on the optimized structure, this paper proposes a mesh adaptive strategy which consists of finer ground mesh and coarser adaptive mesh for the heat sink design. The nodes of adaptive mesh are selected from ground mesh based on the structure of current iteration. The physical fields are solved on adaptive mesh, and design variables are updated on ground mesh. Besides, hyperparameters are introduced to control the adaptive degree, and their influence on optimized results is analysed in detail. Numerical experiments with simplified two-layer thermal-fluid model and parametric level set method show that the developed method outperforms traditional uniform mesh and is able to save large amounts of computing resources. [ABSTRACT FROM AUTHOR]

Reinforcement learning is a compelling area of research within machine learning because it enables the improvement of control strategies for future manipulation of dynamic systems, leveraging previous data even without a precise model of the system. It usually makes complex, model-free predictions from data alone, which is actually consistent with the purpose of control in that they both aim to design systems using richly structured perceptions to execute planning and control strategies that adequately adapt to changing environments. The robust trajectory tracking control of intricate mechanical systems presents a challenging problem that necessitates effective control methods. In this paper, we propose a novel nonlinear control strategy based on deep reinforcement learning to solve the trajectory tracking problem of a 3-degree-of-freedom (3-DOF) control moment gyroscope (CMG). First, dynamic modeling of the 3-DOF CMG is used as a policy solver for the reinforcement learning training environment, and transfer learning is employed to bridge the reality gap. Then, the hyperparameters and reward functions of the neural network are optimized using the asynchronous successive halving algorithm. Ultimately, the twin delay depth determination policy gradient algorithm is trained in simulation to yield an agent capable of tracking user-defined trajectory routes as a nonlinear controller for the system. Both simulation and experimental results show that the proposed method works well for both high-frequency and low-frequency varying trajectory tracking control, and that the proposed method has better response speed and robustness than classic linear parameter-varying control methods and the state-of-the-art nonlinear parameter-varying method and the neural network-based feedback linearization adaptive control method. [ABSTRACT FROM AUTHOR]

Variable parameters (VP) schemes are the most effective adaptive schemes in increasing control charts' sensitivity to detect small to moderate shift sizes. In this paper, we develop four VP adaptive memory-type control charts to monitor multivariate multiple linear regression profiles. All the proposed control charts are single-chart (single-statistic) control charts, two use a Max operator and two use an SS (squared sum) operator to create the final statistic. Moreover, two of the charts monitor the regression parameters, and the other two monitor the residuals. After developing the VP control charts, we developed a computer algorithm with which the charts' time-to-signal and run-length-based performances can be measured. Then, we perform extensive numerical analysis and simulation studies to evaluate the charts' performance and the result shows significant improvements by using the VP schemes. Finally, we use real data from the national quality register for stroke care in Sweden, Riksstroke, to illustrate how the proposed control charts can be implemented in practice. [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]