Adaptive Scheme Research Articles

Model-Free Adaptive Sliding Mode Control Scheme Based on DESO and Its Automation Application

Model-Free Adaptive Sliding Mode Control Scheme Based on DESO and Its Automation Application

Adaptive Sampling for Signals Associated with the Special Affine Fourier Transform

Adaptive sampling is inspired by the working principle of biological neurons, which converts the amplitude information of the received signal into a time sequence through a time encoding machine (TEM). The article mainly studies uniform sampling and adaptive sampling of non-bandlimited signals in two kinds of function spaces associated with the special affine Fourier transform (SAFT). Firstly, based on the stability characterization of the basis functions, we prove the existence of orthogonal projection operators in function spaces associated with the canonical convolution and the SAFT-convolution, respectively. Secondly, uniform sampling theorems are established for two kinds of signals living in the proposed function spaces. Finally, adaptive sampling schemes based on the crossing TEM and the integrate-and-fire TEM are studied. Moreover, the corresponding iterative reconstruction algorithms with exponential convergence are provided.

Fixed-Time Fault-Tolerant Adaptive Neural Network Control for a Twin-Rotor UAV System with Sensor Faults and Disturbances

This paper presents a fixed-time fault-tolerant adaptive neural network control scheme for the Twin-Rotor Multi-Input Multi-Output System (TRMS), which is challenging due to its complex, unstable dynamics and helicopter-like behavior with two degrees of freedom (DOFs). The control objective is to stabilize the TRMS in trajectory tracking in the presence of unknown nonlinear dynamics, external disturbances, and sensor faults. The proposed approach employs the backstepping technique combined with adaptive neural network estimators to achieve fixed-time convergence. The unknown nonlinear functions and disturbances of the system are processed via an adaptive radial basis function neural network (RBFNN), while the sensor faults are actively estimated using robust terms. The developed controller is applied to the TRMS using a decentralized structure where each DOF is controlled independently to simplify the control scheme. Moreover, the parameters of the proposed controller are optimized by the gray-wolf optimization algorithm to ensure high flight performance. The system’s stability analysis is proven using a Lyapunov approach, and simulation results demonstrate the effectiveness of the proposed controller.

Adaptive weight determination schemes in angle and sub-band compounding for ultrasonic beamforming

Abstract We have proposed the Adaptive Sub-Band Compound (A-SBC) method, which uses subbands of different frequencies to improve the resolution by adaptive compounding. A-SBC effectively suppresses the sidelobe level by mainly utilizing the principle that the phases of subbands of different frequencies are inconsistent off-axis. There are many specific methods that can realize A-SBC. In this study, we propose three additional methods based on a previous application of A-SBC to multi-angle plane-wave beamforming. We evaluate these four methods through simulation and human carotid artery experiments.

Adaptive Neural Cooperative Control of Multirobot Systems With Input Quantization.

This article develops the adaptive neural cooperative control scheme for a group of mobile robots with a limited sensing range in presence of input quantization by a dynamic surface control technique. First, to make the controller design feasible, the original robotic system is transformed into a new fully actuated system using a transverse function. Then, taking into consideration the effects of a hysteresis quantizer, an adaptive neural cooperative controller is developed based on the universal approximation property of the radial basis function neural networks and the connectivity preservation strategy. Furthermore, the proposed control scheme can guarantee that all closed-loop signals are semi-globally uniformly ultimately bounded. Meanwhile, desired constraints are not breached and tracking errors are within the predefined domains. Finally, several simulation results are carried out to testify the feasibility and efficiency of the theoretical findings revealed in this article.

A SOC balancing adaptive droop control scheme for multiple energy storage modules in DC microgrids

A SOC balancing adaptive droop control scheme for multiple energy storage modules in DC microgrids

An Adaptive Stabilization Scheme for Autonomous System Oscillations

An Adaptive Stabilization Scheme for Autonomous System Oscillations

Stochastic ranking improved teaching-learning and adaptive Grasshopper optimization algorithm-based clustering scheme for augmenting network lifetime in WSNs

Stochastic ranking improved teaching-learning and adaptive Grasshopper optimization algorithm-based clustering scheme for augmenting network lifetime in WSNs

Intelligent joint communication and computation scheme of UAV-assisted Offloading in high speed rail scenarios

With the continuous development of communication and computation technologies, large bandwidth services place higher demands on computing capacity and throughput in High-Speed Rail (HSR) scenarios. On the one hand, Millimetre-Wave enables high data transmission rates, but leads to high Doppler frequency deviation as well as large path loss. On the other hand, the development of Mobile Edge Computing greatly alleviates user computing congestion. In this paper, we propose the Adaptive Joint Communication and Computation Resource Allocation scheme to solve the energy optimization problem of a dual-band UAV and Mobile Relay relay-assisted HSR offloading system. This scheme works well to optimize the performance of the system through resource allocation. In addition, since the optimization problem is modeled as a Mixed-Integer Nonlinear Program, we propose the Pre- and Post-state connected Parameterized Deep Q-Network algorithm, which is based on Deep Reinforcement Learning approach, for offloading decision and bandwidth resource allocation. Simulation results show that the proposed algorithms result in lower system energy consumption, while ensuring a high task completion rate as well.

Adaptive critic design for safety-optimal FTC of unknown nonlinear systems with asymmetric constrained-input

Safe fault tolerant control is one of the key technologies to improve the reliability of dynamic complex nonlinear systems with limited inputs, which is hard to solve and definitely a great challenge to tackle. Thus the paper presents a novel safety-optimal FTC (Fault Tolerant Control) approach for a category of completely unknown nonlinear systems incorporating actuator fault and asymmetric constrained-input, which can guarantee the system’s operation within a safe range while showcasing optimal performance. Firstly, a CBF (Control Barrier Function) is incorporated into the cost function to penalize unsafe behaviors, and then we translate the intractable safety-optimal FTC problem into a differential ZSG (Zero-Sum Game) problem by defining the control input and the actuator fault as two opposing sides. Secondly, a neural-network-based identifier is employed to reconstruct system dynamics using system data, and the resolution of handling asymmetric constrained-input with the introduced non-quadratic cost function is achieved through the design of an adaptive critic scheme, aiming to reduce computational expenses accordingly. Finally, through the theoretical stability analysis, it is demonstrated that all signals in the closed-loop system are consistently UUB (Uniformly Ultimately Bounded). Furthermore, the proposed method’s effectiveness is also verified in the simulation experiments conducted on a model of a single-link robotic arm system with actuator failure. The result shows that the algorithm can fulfill the safety-optimal demand of fault tolerant control in fault system with asymmetric constrained-input.

Synergistic frameworks for sensor fault isolation and accommodation in grid-side converters

The stable and reliable operation of grid-integrated renewable energy systems requires advanced control and coordination of grid-side converters (GSCs), utilizing the feedback measurements of voltage and current sensors from both the direct current (DC) and alternating current (AC) sides of the converter. However, the effective operation of the converter is susceptible to sensor failures or divergence from their proper operation. Although sensor fault detection algorithms are usually effective under abrupt faults, the fault propagation effect caused by the physical interconnection between the DC and AC sides of the converter may limit the performance of the sensor fault isolation process in revealing the exact location of a potential faulty sensor. Therefore, this work proposes a robust, model-based fault isolation and accommodation scheme. Specifically, a synergistic sensor fault isolation framework based on adaptive estimation schemes is proposed for both single and multiple faults in the DC voltage and AC current sensors, considering modeling uncertainty and measurement noise. The performance analysis in terms of stability, learning capability, and fault isolability is rigorously examined. An accommodation scheme based on a virtual sensor utilizing dynamic sensor fault estimation with real-time learning capabilities is applied to a GSC. Finally, the performance of the proposed fault isolation and accommodation scheme is evaluated through simulation analysis under several scenarios involving single and multiple sensor faults.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance: A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows, comprising human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), face challenges from uncertain disturbances, such as sensor faults, inaccurate driver operations, and mismatched model errors. Furthermore, misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’ perception and platoon safety. In this paper, we develop a two-dimensional robust control method for a mixed platoon, including a single leading CAV and multiple following HDVs that incorporate robust information sensing and platoon control. To effectively detect and locate unknown obstacles ahead of the leading CAV, we propose a cooperative vehicle–infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV. This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner. Additionally, we propose a distributed car-following control scheme with robustness to guarantee the following HDVs, considering uncertain disturbances. We also provide theoretical proof of the string stability under this control framework. Finally, extensive simulations are conducted to validate our approach. The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers, significantly reduce the mean-square deviation in obstacle sensing, and approach the theoretical error lower bound. Moreover, the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

Fall-related gait pattern recognition based on surface electromyography using a hybrid neural network with transfer learning

Gait analysis across different individuals presents significant challenges due to individual variability. In this study, we developed a gait analysis system to enable continuous decoding of gait patterns based on surface electromyography (EMG). Our objective was to address the long-term challenges associated with neural networks, particularly their limited generalization capability and the heavy training burden on new subjects in gait pattern recognition. Fifteen healthy subjects were recruited to imitate two fall-related gait patterns and collect myoelectric signals from eight lower limb muscles. We proposed a transfer learning-based framework and investigated eight adaptation schemes using varying quantities of target training samples. In the cross-subject evaluation, our framework demonstrated robust generalization capability and reduced training burden. Even with a limited number of target training samples, the framework outperformed state-of-the-art machine learning models and discriminative neural networks that have previously shown excellent recognition performance in subject-specific evaluation. This study highlights the promising potential of using transfer learning in conjunction with neural networks for gait pattern recognition across different individuals. It also provides valuable insights into the diagnosis, monitoring, and treatment of various medical conditions using surface EMG-based gait analysis systems.

Fault-tolerant control of underactuated unmanned surface vessels in presence of deception and injection attacks

This paper studies the trajectory tracking control problem of underactuated unmanned surface vessels (USVs) under the influence of internal and external uncertainties, actuator faults, and injection and deception attacks. In the control design, we separately designed virtual adaptive laws to compensate for the time-varying gains suffered by the kinematics channel and combined the event-triggered control (ETC) mechanism to establish an online approximator to compensate for the time-varying gains of the kinematics channels and the loss-of-effectiveness (LOE) and bias faults suffered by the system. Combining robust neural damping technology and finite-time disturbance observer (FTDO), the dynamic uncertainties and external disturbances are suppressed. Finally, a novel robust adaptive trajectory tracking control scheme is proposed. The scheme achieves active compensation for heterogeneous uncertainties including internal and external uncertainties, actuator faults, and attack signals. The Lyapunov stability theory analysis shows that all signals of the tracking system are bounded. The simulation results prove the effectiveness of this control scheme.

Dynamic performance-guaranteed adaptive event-triggered trajectory tracking control for underactuated surface vehicles

This paper investigates the trajectory tracking control problem of underactuated surface vehicles in the presence of model uncertainties and external disturbances. A dynamic performance-guaranteed adaptive event-triggered control scheme is proposed to transform the original constrained control problem into an equivalent unconstrained problem and ensure that the trajectory tracking is achieved with prescribed performance. By developing a dynamic event-triggered mechanism with adjustable thresholds that can be updated in an adaptive way, the communication burden and actuator wear can be effectively alleviated. To avoid differential explosions that may increase system complexities, an improved transformation-adapted dynamic surface control (DSC) is designed to eliminate adverse effects induced by performance functions and make the DSC applicable to performance-transformed systems. By utilizing the parameter compression algorithm, model uncertainties and external disturbances can be effectively addressed, and the adaptive laws can be integrated into a concise form, with only two online updating parameters required. Comprehensive analysis is provided to verify that all error signals are semi-globally uniformly ultimately bounded (SGUUB). Compared to existing approaches, the proposed control scheme exhibits lower update frequency.

The Role of Attitude to Uncertainty When Choosing Adaptive Strategies and Implementing Them: A Comparative Analysis on a Sample Of Students And Teachers

Introduction. This article explores the relationship between the attitude towards uncertainty and the nature of personality adaptability in the context modern life situation’s risks. The relevance of this topic lies in the possibility of choosing not only constructive, but also destructive adaptation schemes in situations of uncertainty, which are increasingly arising in the lives of young people today. The analysis of studies existing in this subject field made it possible to identify a number of subjective predictors of a successful adaptation. The novelty of this research is identifying and describing the influence of the attitude towards uncertainty on the adaptation of an individual in the context of modern risks. The contents of the article include analysis of various adaptation profiles and their link to the attitude towards uncertainty.Purpose. Studying the relationship between the attitude to tolerance and adaptation of an individual in the context of modern risks.Materials and Methods. This study features the following methodological tools: the McLean Uncertainty Tolerance Scale; the S. Maddy Resilience Test (adapted by D. A. Leontiev); the methodology for diagnosing socio-psychological adaptation (K. Rogers and R. Diamond); the “Attitude to Uncertainty” questionnaire (T.N. Shcherbakova). Statistical data processing was carried out using the Spearman rank correlation coefficient.Results. A structured analysis of the factors that determine the adaptability of students and teachers in different regions in the context of modern risks is presented in this section. The peculiarities of the severity of indicators of tolerance to uncertainty and resilience of an individual, as well as their relationship with adaptability – maladaptivity were found.Discussion. The results of the empirical study reflect the importance of the attitude and tolerance of an individual to uncertainty for their successful adaptation. For the first time, the interrelation of the components of resilience and tolerance to uncertainty with adaptability – maladaptiveness of students from different regions personalities in the context of information, social and educational risks are shown. Thus, it can be said that there is a connection between the adaptability of students’ personalities and their attitude to uncertainty, indicators of tolerance to rapidly changing conditions, and the severity of the parameters of resilience. The level of reflection of the features of the projections of uncertainty allows to develop resilience and increase the level of adaptability to the relevant life conditions on a personal level. A model of a program of psychological support for increasing the level of adaptation of students in the context of modern risks of uncertainty is proposed.

Impact of Instantaneous Parameter Sensitivity on Ensemble‐Based Parameter Estimation: Simulation With an Intermediate Coupled Model

AbstractOn ensemble‐based coupled data assimilation, cross‐component parameter estimation (CPE), has not been as extensively developed and applied as weakly coupled state and parameter estimation along with cross‐component state estimation. This discrepancy is partially attributed to the lack of emphasis on the instantaneous response of coupled model states with respect to parameters across different components. We define so‐called response as the instantaneous parameter sensitivity (IPS). Under the framework of sequential assimilation, the prior information heavily relies on the IPS of coupled states with different time scales. Based on the IPS analysis for an intermediate coupled model, a series of twin experiments of state and parameter estimation are conducted, in which an IPS‐inspired adaptive inflation scheme for parameter ensemble is introduced. Results show that the success of a parameter estimation strategy is closely tied to the significant IPS of the observed state to the parameter targeted for optimization, as it maintains a high signal‐to‐noise ratio in the error covariance between parameter and prior state, thereby enhancing parameter estimation. An interesting finding in the context of IPS‐based CPE is: an atmospheric parameter can be successfully estimated by assimilating observations from slow‐varying oceanic component, but not vice versa. In comparison with cross‐component state estimation, successful CPE significantly enhances the estimation accuracy of coupled states by mitigating model bias.

Elite-guided Resampling and Multi-mutation based Differential Evolution with exponential crossover for numerical optimization

In the existing Differential Evolution (DE) research, there are two main crossover schemes: exponential crossover and binomial crossover. Most researchers believe that binomial crossover is skilled in handling numerical optimization problems while exponential crossover has the advantage in handling optimization problems with certain correlation between adjacent variables. However, we discover that DE variants with exponential crossover can achieve performance comparable to advanced ones utilizing binomial crossover in optimization applications, provided that an appropriate crossover rate CR and associated parameter control are established. In this paper, an Elite-guided Resampling and Multi-mutation based DE with exponential crossover (ERM-DE) is raised to fill the blank in this field, and its main highlights are as follows: First, a novel structure with two stages is raised in our ERM-DE algorithm. The first stage is elite-guided global opposition learning based resampling, and the other is novel exponential crossover DE evolution stage. Second, a Novel Parameter Control (NPC) technique involving scale factor F and crossover rate CR is raised. Different from applying the fitness-based parameter control, the adaptation schemes of the parameters F and CR in our ERM-DE algorithm are fitness-independent. In addition, this NPC technique will also be selectively applied to the trial vector generation strategy DE/target-to-pbest/1/exp with or without optional archive. Third, a novel population Diversity Enhancement Mechanism (DEM) is proposed, which can enhance population diversity by recalculating individuals in a stagnant state. Finally, a time stamp mechanism is posed to deal with outdated inferior and historical solutions in the external archive. A Large test suite with 88 benchmark functions from CEC2013, CEC2014, and CEC2017 test suites is used for algorithm validation and the experimental results indicate that our ERM-DE demonstrates competitiveness with several state-of-the-art DE variants.

In the Wild Video Violence Detection: An Unsupervised Domain Adaptation Approach

This work addresses the challenge of video violence detection in data-scarce scenarios, focusing on bridging the domain gap that often hinders the performance of deep learning models when applied to unseen domains. We present a novel unsupervised domain adaptation (UDA) scheme designed to effectively mitigate this gap by combining supervised learning in the train (source) domain with unlabeled test (target) data. We employ single-image classification and multiple instance learning (MIL) to select frames with the highest classification scores, and, upon this, we exploit UDA techniques to adapt the model to unlabeled target domains. We perform an extensive experimental evaluation, using general-context data as the source domain and target domain datasets collected in specific environments, such as violent/non-violent actions in hockey matches and public transport. The results demonstrate that our UDA pipeline substantially enhances model performances, improving their generalization capabilities in novel scenarios without requiring additional labeled data.

ATENA: Adaptive TEchniques for Network Area Coverage and Routing in IoT-Based Edge Computing

The Internet of Things (IoT) and Edge Computing (EC) are now pervasive. IoT networks are made up of several objects, deployed in an area of interest (AoI), that can communicate with each other and with a remote computing centre for decision-making. EC reduces latency and data congestion by bringing data processing closer to the source. In this paper, we address the problems of network coverage and data collection in IoT-based EC networks. Several solutions exist designed to solve these problems unfortunately, they are either not energy-efficient or do not consider connectivity and they do not cover AoI. The proposed routing mechanisms are often not suited for AoI coverage schemes and lead to poor data routing delay or high packet losses. To address these shortcomings, we propose ATENA, a periodic, lightweight and energy-efficient protocol that aims to improve network coverage based on the two new schemes used to define a few number of objects to be kept awake at each period it also uses an adaptive routing scheme to send the collected data to the computing centre. This protocol is designed to take into account the limited resources of objects and ensures a longer network lifetime. A comparison of ATENA’s simulation results with recent existing protocols shows that it significantly improves network coverage, network lifetime and end-to-end delay to the computing centre.