Adaptive Feedback Research Articles

Event-Sampled Adaptive Neural Course Keeping Control for USVs Using Intermittent Course Data

This paper addresses the issue of course keeping control (CKC) for unmanned surface vehicles (USVs) under network environments, where various challenges, such as network resource constraints and discontinuities of course and yaw caused by data transmission, are taken into account. To tackle the issue of network resource constraints, an event-sampled scheme is developed to obtain the course data, and a novel event-sampled adaptive neural-network-based state observer (NN–SO) is developed to achieve the state reconstruction of discontinuous yaw. Using a backstepping design method, an event-sampled mechanism, and an adaptive NN–SO, an adaptive neural output feedback (ANOF) control law is designed, where the dynamic surface control technique is introduced to solve the design issue caused by the intermission course data. Moreover, an event-triggered mechanism (ETM) is established in a controller–actuator (C–A) channel and a dual-channel event-triggered adaptive neural output feedback control (ETANOFC) solution is proposed. The theoretical results show that all signals in the closed-loop control system (CLCS) are bounded. The effectiveness is verified through numerical simulations.

Using Adaptive Feedback in an ESP Course for Developing University Students’ EFL Listening and Reading Comprehension Skills

Using Adaptive Feedback in an ESP Course for Developing University Students’ EFL Listening and Reading Comprehension Skills

Desynchronizing oscillators coupled in multi-cluster networks through adaptively controlling partial networks.

This article introduces an adaptive control scheme with a feedback delay, specifically designed for controlling partial networks, to achieve desynchronization in a coupled network with two or multiple clusters. The proposed scheme's effectiveness is validated through several representative examples of coupled neuronal networks with two interconnected clusters. The efficacy of this scheme is attributed to the rigorous and numerical analyses on the corresponding transcendental characteristic equation, which includes time delay and other network parameters. In addition to investigating the impact of time delay and inter-connectivity on the stability of an incoherent state, we also rigorously find that controlling only one cluster cannot realize the desynchronization in the coupled oscillators within three or more clusters. All these, we believe, can deepen the understanding of the deep brain stimulation techniques presently used in the clinical treatment of neurodegenerative diseases and suggest future avenues for enhancing these clinical techniques through adaptive feedback settings.

Periodic Intermittent Adaptive Control with Saturation for Pinning Quasi-Consensus of Heterogeneous Multi-Agent Systems with External Disturbances.

A periodic intermittent adaptive control method with saturation is proposed to pin the quasi-consensus of nonlinear heterogeneous multi-agent systems with external disturbances in this paper. A new periodic intermittent adaptive control protocol with saturation is designed to control the internal coupling between the follower agents and the feedback gain between the leader and the follower. In particular, we use the saturation adaptive law: when the quasi-consensus error converges to a certain range, the adaptive coupling edge weight and the adaptive feedback gain will not be updated. Furthermore, we propose three saturated adaptive pinning control protocols. The quasi-consensus is achieved through its own pinning as long as the agents remain connected to each other. Using the Lyapunov function method and inequality technique, the convergence range of the quasi-consensus error of a heterogeneous multi-agent system is obtained. Finally, the rationality of the proposed control protocol is verified through numerical simulation. Theoretical derivation and simulation results show that the novel proposed periodic intermittent adaptive control method with saturation can successfully be used to achieve the pinning of quasi-consensus of nonlinear heterogeneous multi-agent systems.

Deep reinforcement learning for cooperative robots based on adaptive sentiment feedback

Human–robot cooperative tasks have gained importance with the emergence of robotics and artificial intelligence technology. In interactive reinforcement learning techniques, robots learn target tasks by receiving feedback from an experienced human trainer. However, most interactive reinforcement learning studies require a separate process to integrate the trainer’s feedback into the training dataset, making it challenging for robots to learn new tasks from humans in real-time. Furthermore, the types of feedback sentences that trainers can use are limited in previous research. To address these limitations, this paper proposes a robot teaching strategy that uses deep RL via human–robot interaction to learn table balancing tasks interactively. The proposed system employs Deep Q-Network with real-time sentiment feedback delivered through the trainer’s speech to learn cooperative tasks. We designed a novel reward function that incorporates sentiment feedback from human speech in real-time during the learning process. The paper presents an improved reward shaping technique based on subdivided feedback levels and shrinking feedback. This function serves as a guide for the robot to engage in natural interactions with humans and enables it to learn the tasks effectively. Experimental results demonstrate that the proposed interactive deep reinforcement learning model achieved a high success rate of up to 99.06%, outperforming the model without sentiment feedback.

Adaptive Global Synchronization for a Class of Quaternion-Valued Cohen-Grossberg Neural Networks with Known or Unknown Parameters

In this paper, the adaptive synchronization problem of quaternion-valued Cohen–Grossberg neural networks (QVCGNNs), with and without known parameters, is investigated. On the basis of constructing an appropriate Lyapunov function, and utilizing parameter identification theory and decomposition methods, two effective adaptive feedback schemes are proposed, to guarantee the realization of global synchronization of CGQVNNs. The control gain of the above schemes can be obtained using the Matlab LMI toolbox. The theoretical results presented in this work enrich the literature exploring the adaptive synchronization problem of quaternion-valued neural networks (QVNNs). Finally, the reliability of the theoretical schemes derived in this work is shown in two interesting numerical examples.

Global stability and optimal control of epidemics in heterogeneously structured populations exhibiting adaptive behavior

We present a novel SIRS model on scale-free networks that takes into account behavioral memory and time delay to depict an adaptive behavioral feedback mechanism, which can better characterize the actual spread of epidemics. We conduct rigorous analysis on the dynamics of the model, including the basic reproduction number R0, uniform persistence and the global asymptotic stability of equilibria. The model has the sharp threshold property, namely, if R0 is less than 1 then the disease-free equilibrium is globally asymptotically stable while if R0 is larger than 1 then the endemic equilibrium is globally asymptotically stable. We further perform an optimal control study for the model to seek effective vaccination and treatment strategies. The existence and uniqueness of these optimal control strategies are demonstrated. Finally, we perform some stochastic network simulations that yield quantitative agreement with the deterministic mean-field approach. Our findings indicate that time delay does not affect R0, but behavioral memory does.

Event-triggered fuzzy adaptive output feedback containment fault-tolerant control for nonlinear multi-agent systems against actuator faults

In this paper, the observer-based fully distributed event-triggered adaptive fuzzy containment fault-tolerant control (FTC) problem is studied for nonlinear multi-agent systems (MASs) subject to unknown actuator faults. Fuzzy logic systems (FLSs) are utilized to model unknown agents, and a new state observer is constructed to estimate unmeasurable states. Since the global communication information of topology is not available, the local reference signals are also not available, a distributed estimator is introduced to obtain the information of the local reference signals. To deal with the unknown actuator faults, a novel actuator fault compensation mechanism with a projection operator is adopted. Then based on backstepping technique and by designing a two-channel event-triggered mechanism, a fully distributed event-triggered containment FTC approach is developed. It is testified that all parameters and variables of the controlled system are bounded, and the followers can converge to the dynamic convex hull spanned by the leaders. Finally, we apply the developed control algorithm to the multiple unmanned surface vehicle (USV) systems, and the corresponding simulation results further confirm the feasibility of the developed control algorithm.

Improving Li-ion battery health: Predicting remaining useful life using IWBOA-ELM algorithm

The prediction of Li-ion battery remaining useful life (RUL) is primarily used to prevent battery health damage caused by overcharging and discharging Li-ion batteries, which is critical for secondary battery life applications. To accurately predict the RUL of Li-ion batteries, a prediction method based on the improved black widow algorithm and extreme learning machine (IWBOA-ELM) is proposed in this paper. Because of the single method of black widow spider seeking, this paper employs the Archimedean spiral (AR) method to improve the search method of the BWOA algorithm and the global search capability of the BWOA algorithm; this is done to prevent the BWOA algorithm from being prone to failure. To avoid the problems of local optimal solutions and premature convergence that plague the BWOA algorithm, this paper employs the Golden-Sine strategy (Golden-SA) to update the positions of individual black widow spiders. Furthermore, for the cyclic trend of Li-ion batteries, this paper proposes an adaptive feedback factor to increase the convergence coefficient and control the early convergence rate while adapting to different battery capacity data. By comparing the outcomes of some CEC2017 test functions, the upgraded IBWOA performs well in determining the optimal. Finally, the IWBOA-ELM model is validated using NASA data, and the RUL prediction is performed under three scenarios: constant current discharge at different temperatures, constant current discharge at room temperature, and random current discharge at room temperature, with the results compared to the conventional method. The results reveal that the IWBOA-ELM method's RMSE error of RUL prediction is lower than that of other algorithms, indicating that IWBOA-ELM has strong generalization and robustness.

FAT-based adaptive and velocity feedback control of cooperative manipulators handling a flexible object

The previous regressor-based control method to control two cooperative manipulators in handling a deformable object leads to complex calculations and complicated programming in experimental hardware tests. There is an existing lack of studies about the development of the controller based on a partial differential equation (PDE)-based model and considering the model’s uncertainties. Previous studies have shown fewer experimental validations regarding two cooperative manipulators that handle deformable objects under uncertain model parameters. This study proposes a composite controller comprising a function approximation technique (FAT)-based adaptive control (FATAC) for a slow subsystem and a velocity feedback control (VFC) for a fast subsystem. The proposed FATAC is used for trajectory tracking, and VFC is used to suppress the vibration of the deformable object. Lyapunov stability analysis has been carried out to design controllers that stabilize a non-linear system of two cooperative manipulators handling the flexible object. Simulation and hardware experimental tests have been carried out to validate the performance of proposed controllers. The results verified that the proposed composite controller comprising the FATAC has successfully driven the cooperative manipulators to handle the deformable object so that it follows the desired trajectories. The VFC has successfully suppressed the transverse vibration of the deformable object.

Adaptive cooperation control of wind power generation systems based on Hamilton system under limited input

SummaryNumerous wind turbines form large‐scale wind farms, which are complex nonlinear systems with uncertain parameters. The issue of maximum wind energy capture and coordinated control has always been a research hotspot. In this article, under the condition of limited input and uncertain parameters, the preset controller and the adaptive cooperation control are designed to realize the maximum wind energy capture for every wind turbine and the adaptive cooperation control of multiple wind turbines. The research gap lies in that the Hamilton model of wind power generation system is established with uncertain parameters, and the preset controller (method) is designed to capture the maximum wind energy. Under the hypothesis that the uncertain part can be expressed as a linear form about unknown parameter, and using the saturation function processing method in the diagonal matrix, an adaptive feedback controller with limited input is designed to realize the adaptive cooperation control of multiple wind turbines. The simulation results show that under the conditions such as variable wind speed, limited input and uncertain parameters, the wind turbine remain normal operation at the desired angular velocity. It can be concluded that not only the maximum wind energy capture is realized under the condition of variable wind speed, in which the wind turbine can operate on the optimal power curve to improve the utilization of wind energy, but also the adaptive cooperation control of multiple wind turbines can be achieved with limited input and parameter perturbation.

Adaptive consensus reaching process with dynamic weights and minimum adjustments for group interactive portfolio optimization

The mean–variance model (MV) can help companies in an economic downturn identify portfolios that increase revenue while reducing risk. However, the MV optimization model derives a set of optimal portfolios. The limited rationality and experience of the company's manager, as well as the conflict of interest with the shareholders, require several experts to help the manager make investment decisions. Moreover, experts' unique preferences require negotiation through interaction to reach a consensus, which is not considered in existing group interactive optimization algorithms. The social network group decision-making (SNGDM) method is an effective approach for reaching consensus. Notably, in addition to the trust relationships, experts' intrinsic risk preferences and rational levels affect investment decisions and change with the decision-making process. Another important problem is reaching consensus with finite interaction costs. Although dynamic opinion is considered by current minimum cost models, the impact of dynamic trust on consensus is neglected. This research aims to develop a group interactive portfolio optimization method based on SNGDM (SNGDM-PO) to address the mentioned limitations. First, we introduce consensus into group interactive optimization algorithms and develop an adaptive learning model that simulates group preferences. Then, we combine dynamic intrinsic influence and extrinsic trust to generate experts' dynamic weights. To reduce interaction consensus costs, we propose an adaptive consensus feedback mechanism that minimally adjusts for dynamic opinion and trust with a time constraint. Finally, empirical study and comparison findings demonstrate that the SNGDM-PO can efficiently obtain consensus with minimum adjustments and low interaction costs and produce high group satisfaction.

Passivity Analysis and Complete Synchronization of Fractional Order for Both Delayed and Non-Delayed Complex Dynamical Networks with Couplings in the Derivative

This manuscript explores the analysis of passivity and synchronization criteria for a complex fractional-order dynamical network model with derivative couplings and time-varying delays. The passivity problem of the proposed network model is deduced using various inequality methods and presented as a linear matrix inequality. To ensure complete synchronization for a fractional-order complex dynamical network with derivative couplings (CDNMDC), we derive suitable criteria using an adaptive feedback control method. Additionally, we investigate the synchronization criterion of these complex networks while accounting for parameter uncertainties. Finally, we provide an example to demonstrate the effectiveness of the proposed solutions.

Curvelet based robust improved sine adaptive filter for feedback cancellation in hearing aids

Acoustic feedback is a common and persistent issue in hearing aids, which can lead to limitations in the achievable amplification and significant degradation in sound quality, including howling artefacts. Recently, the maximum versoria criteria (MVC) algorithm has been developed to reduce the acoustic feedback in hearing aids in the presence of impulsive noise. However, the robust technique MVC has not addressed the feedback signal’s sparseness characteristic while developing an adaptive feedback canceller. This paper proposes a robust and sparsity-aware technique called curvelet-improved sine-based adaptive filtering (CISAF) algorithm for adaptive feedback cancellation (AFC) in hearing aids using the prediction error method (PEM) in the case of non-gaussian interference. The step factor is enhanced based on the logarithmic cosh function to speed up the algorithm’s convergence while guaranteeing a low steady-state error. This variable step size (VSS) is included with CISAF as CISAF-VSS technique. Computer simulations show that the suggested method achieves a good convergence rate, maintains high speech quality, keeps a high segmental SNR (segSNR) and short-time objective intelligibility (STOI) at steady-state.

Discontinuous control for exponential synchronization of complex-valued stochastic multi-layer networks

This article is concerned with the exponential synchronization issue of complex-valued stochastic multi-layer networks (CVSMLNs) via two discontinuous control approaches. At first, a new network model of complex-valued multi-layer network is built by integrating stochastic disturbances, intra-layer and inter-layer couplings. Different from previous aperiodic intermittent control (APIC) approaches, under the framework of APIC, novel impulsive feedback control (IFC) (IFC is executed at the activation time of APIC) and universal adaptive feedback control (adaptive control gain is different in different layers) are both developed to realize the exponential synchronization of CVSMLNs. Subsequently, some simple sufficient criteria are derived by virtue of Lyapunov method, graph theory and complex generalized Itô’s formula. Ultimately, the theoretical results are used in a practical complex-valued neural network and the availability of the obtained results is verified by giving some numerical examples.

Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR

The integrated Resonant Magnetic Perturbation (RMP)-based Edge-Localized Mode (ELM)-crash-control process aims to enhance the plasma performance during the RMP-driven ELM crash suppression, where the RMP induces an unwanted confinement degradation. In this study, the normalized beta () is introduced as a metric for plasma performance. The integrated process incorporates the latest achievements in the RMP technique to enhance efficiently. The integrated process triggers the n = 1 Edge-localized RMP (ERMP) at the L–H transition timing using the real-time Machine Learning (ML) classifier. The pre-emptive RMP onset can reduce the required external heating power for achieving the same by over 10% compared to the conventional onset. During the RMP phase, the adaptive feedback RMP ELM controller, demonstrating its performance in previous experiments, plays a crucial role in maximizing during the suppression phase and sustaining the -enhanced suppression state by optimizing the RMP strength. The integrated process achieves up to ∼2.65 during the suppression phase, which is ∼10% higher than the previous KSTAR record but ∼6% lower than the target of the K-DEMO first phase ( = 2.8), and maintains the suppression phase above the lower limit of target (= 2.4) for ∼4 s (∼60). In addition to enhancement, the integrated process demonstrates quicker restoration of the suppression phase and recovery of compared to the adaptive control with the n = 1 Conventional RMP (CRMP). The post-analysis of the experiment shows the localized effect of the ERMP spectrum in radial and the close relationship between the evolution of and the electron temperature.

Switched Combination of Simplified Kalman Filter and Affine Projection Algorithm for Acoustic Feedback Cancellation

Acoustic coupling between the microphone and the loudspeaker is a major issue in open-fit digital hearing aids. When compared to a close-fit hearing aid, an open-fit dramatically reduces signal quality and limits the potential maximum stable gain. Adaptive feedback cancellation (AFC) is a practical method for reducing the influence of acoustic coupling. However, because to the high correlation between the loudspeaker signal and the incoming signal, it might induce bias in calculating the feedback path if not carefully considered, especially when the incoming signal is spectrally coloured, as in speech and music. For decreasing this bias, the prediction error method (PEM) is well recognised. In this paper we proposed a switched PEM based hybrid combination of simpliedkalman filter and affine projection algorithms (H-SKF-APA), with soft-clipping that allows for further increase in convergence/tracking rates, resulting in a better ability to recover from an unstable or howling state. Simulation results showed that the proposed algorithm performed better.

Adaptive Output Feedback Control for Nonholonomic Chained Systems with Integral Input State Stability Inverse Dynamics.

This paper investigates a class of nonholonomic chained systems with integral input-to-state stable (iISS) inverse dynamics subject to unknown virtual control directions and parameter uncertainty included in drift terms. First, the system is divided into two interconnected subsystems according to the system's structure. Second, one controller is designed using a switch strategy for state finite escape. Then, another controller and adaptive law are designed by combining a reduced-order state observer and backstepping method after input-state scaling. Finally, simulation results validate the feasibility of the proposed control algorithm.

Trajectory tracking controller of a robotized arm with joint constraints, a direct adaptive gain with state limitations approach

Motion restrictions in robotic devices may introduce complex requirements for any closed-loop control design, mainly when the robot joints must track reference trajectories that force the end-effector to perform planned motions. This study summarizes the comprehensive technical design of an adaptive state feedback controller for multi-link robotic manipulators that consider the effect of position and velocity restrictions on the tracking trajectory control approach. The proposed design is less conservative than other methods because of the explicit inclusion of state restrictions in the control gain dynamics. A logarithm barrier Lyapunov function class supports the design of the adaptive gain for the manipulator. Sufficient conditions based on a Riccati equation simplify the implementation of the adaptive controller with gains depending on the distance between the current state and the restriction sets. Numerical simulations show the advantages of the proposed controller with adaptive gains concerning a similar adaptive controller that does not consider the restrictions and a proportional–integral–derivative form. An implementation for the motion control of a robotic arm is presented to demonstrate the development by implementing the proposed gain, which confirms the suggested improvements enforced by the proposed controller. The performed comparison shows the advantages of the suggested adaptive gain control form, inducing better tracking of reference trajectories and smaller control energy applications.

Adaptive Sliding Mode Feedback Control Algorithm for a Nonlinear Knee Extension Model

Functional electrical stimulation (FES) has been widely used to treat spinal cord injury (SCI) patients. Many research studies employ a closed-loop FES system to monitor the stimulated muscle response and provide a precise amount of charge to the muscle. However, most closed-loop FES devices perform poorly and sometimes fail when muscle nonlinearity effects such as fatigue, time delay response, stiffness, spasticity, and subject change happen. The poor performance of the closed-loop FES device is mainly due to discrepancies in the feedback control algorithms. Most of the existing feedback control algorithms were not designed to adapt to new changes in patients with different nonlinearity effects, resulting in early muscle fatigue. Therefore, this research proposes an adaptive sliding mode (SM) feedback control algorithm that could adapt and fine-tune internal control settings in real-time according to the current subject’s nonlinear and time-varying muscle response during the rehabilitation (knee extension) exercise. The adaptive SM feedback controller consists mainly of system identification, direct torque control, and tunable feedback control settings. Employing the system identification unit in the feedback control algorithm enables real-time self-tuning and adjusting of the SM internal control settings according to the current patient’s condition. Initially, the patient’s knee trajectory response was identified by extracting meaningful information, which included time delay, rise time, overshoot, and steady-state error. The extracted information was used to fine-tune and update the internal SM control settings. Finally, the performance of the proposed adaptive SM feedback control algorithm in terms of system response time, stability, and rehabilitation time was analysed using a nonlinear knee model. The findings from the simulation results indicate that the adaptive SM feedback controller demonstrated the best control performance in accurately tracking the desired knee angle movement by having faster response times, smaller overshoots, and lower steady-state errors when compared with the conventional SM across four reference angle settings (20°, 30°, 40°, and 76°). The adaptive feedback SM controller was also observed to compensate for muscle nonlinearities, including fatigue, stiffness, spasticity, time delay, and other disturbances.