International Journal of Adaptive Control and Signal Processing最新文献

This article focuses on the input-to-state stability (ISS) issue of quaternion-valued memristive networks. Employing the quaternion norm tool and the Lyapunov method, two improved conclusions are developed for the continuous networks. After that, via the semidiscretization technique, a new discrete model is designed, and its ISS performance is discussed and subsequently recur to a nonlinear scalarization approach. Less conservative results are obtained since the nonlinear scalarization approach makes the quaternion interval meaningful. Simulations are presented to verify the validity of the outcomes.

This paper presents a novel design framework termed differential Extreme Learning Machine (DELM) for addressing nonlinear process dynamics in time series modelling. DELM is constructed via a single-layer feed-forward ELM network featuring a skip net topology. This innovative network is engineered to accurately assess nonlinear time series patterns utilizing an nth order Legendre polynomial activation and imposing constraints at the output layer. The DELM persistently monitors trends in streaming process data and adjusts dynamic model predictive control (DMPC) settings inside the feedback loop. The Adaptive Distributed Model Predictive Control (ADMPC) is engineered to provide optimal control responses that meet both local and global stability requirements. The efficacy of DELM-driven DMPC is evaluated for reference tracking and disturbance rejection goals and compared with RELM-based DMPC and model-based adaptive MPC (AMPC). The DELM-DMPC surpasses alternative methods by providing superior generalization, stability, and computational efficiency. Average performance accuracy of 95% is attained across the operational range, exhibiting superior computing speed relative to its controller counterparts.

This work investigates the adaptive constrained control design for a parallel-flow heat exchanger represented by a system of two coupled linear first-order hyperbolic partial differential equations (PDEs). This system incorporates structured uncertainty involving unknown in-domain parameters that characterize neglected dynamics in the heat exchanger model. These parameters may encompass unmodeled heat transfer phenomena, variations in fluid properties, and modeling simplifications. The objective is to regulate the internal fluid outlet temperature to track a desired reference trajectory by adjusting the external fluid velocity. Due to inherent physical constraints, this manipulated variable is upper and lower-bounded. Accordingly, the control problem is bounded and bilinear. Using the set-invariance principle and an energy-like framework, we first develop a bounded state-feedback controller. Then, since the measurements are considered only at the boundaries, we propose an adaptive boundary observer using a swapping scheme and a recursive least squares identifier. The proposed adaptive observer provides online estimates of the distributed state and the unknown parameters. Next, the state-feedback controller is associated with the boundary observer and parameter identifier, and the exponential stability of the closed-loop system is guaranteed using Lyapunov's stability theory. Finally, we provide numerical simulations to demonstrate the efficiency of the proposed control scheme.

This article focuses on adaptive control of a class of non-strict feedback nonlinear fractional order systems with input delay and unmodeled dynamics under prescribed performance constraints. Command filtering backstepping design method is employed to avoid explosion of computation. Additionally, an error compensation mechanism is established to mitigate any errors introduced by the command filter. Radial basis function neural network is utilized to approximate the nonlinear function. Auxiliary signal processing variables are introduced to handle unmodeled dynamics. To address the input delay problem, a Pade approximation technique is employed. The stability analysis of the controller is conducted using Lyapunov stability theory, ensuring that the tracking error converges within a narrow predefined performance range. Finally, simulation results are presented to demonstrate the effectiveness of the proposed controller.

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

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

The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent Kalman filtering solutions. This paper proposes a novel method of H∞ filtering-based on adaptive random weighted estimation to address this issue. It combines the H∞ filter with random weighted concept to estimate system noise statistics. Random weighting theories are established based on the state estimate and state error covariance of the H∞ filter to estimate both process noise statistics and measurement noise statistics. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. Simulation and experimental results show that the proposed method can effectively estimate system noise statistics, leading to improved accuracy for system state estimation.

Automated categorization of electrocardiogram (ECG) waveforms using deep learning (DL) methods has garnered considerable attention in recent research. However, prevalent DL networks encounter challenges including overfitting, class imbalance, limitations in deeper network training, and high computational demands. To address these issues, this study proposes an Automated ECG Arrhythmia Classification framework employing the Reinforced Visual Geometry Group-27 (REF-VGG-27). Initially, the framework encompasses preprocessing steps such as denoising, R-peak identification, data balancing, and cross-validation. For automatic feature extraction and classification, two DL architectures are suggested: a novel hybrid model combining 2D convolutional neural network (2DCNN) with VGG-16, featuring a deep architecture for extracting morphological characteristics, frequency features related to heart rate variability (HRV), and statistical attributes crucial for identifying atrial fibrillation (AF). Subsequently, to classify arrhythmia patterns, the VGG-16 Model is employed. Utilizing publicly available ECG image datasets, the proposed model achieved remarkable accuracy benchmarks: 99.61% accuracy, precision of 99.61%, and recall of 99.48%. Comparative analysis with existing approaches substantiates the efficiency and robustness of our model.

The main thrust of this study is to scrutinize the issues of fault estimation and asynchronous sampled-data fault-tolerant control for semi-Markov jump cyber-physical systems with external disturbances, faults and deception attacks. To do so, initially, an intermediate variable is framed and then using that variable as a foundation, a mode-dependent intermediate estimator is constructed, which estimates the fault signals and system's state simultaneously. Due to the unavailability of mode information in the Markov chain for the observer/controller, a hidden Markov model is employed to represent the asynchronous scenario between the mode of the original system and that of the designed observer/controller. Subsequently, benefited by the estimated terms and sampled-data approach, an asynchronous sampled fault-tolerant control protocol is offered up that facilitates compensating for the faults occurring in the system. In the meantime, the extended passive performance is used to lessen the negative impact of external disturbances exerting on the system. Besides this, the deception attacks occurring in the system are presumed to have a stochastic nature that adheres to the Bernoulli distribution. Moreover, by constructing mode-dependent Lyapunov–Krasovskii functional and blending it with integral inequalities, the sufficient condition confirming the intended outcomes is procured in the framework of linear matrix inequalities. Thereafter, on the platform of deduced adequate criteria, an explicit formulation for the requisite gain values can be obtained. Ultimately, simulation results are offered to verify the reliability of presented outcomes.

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