International Journal of Adaptive Control and Signal Processing最新文献

In the era of digitalization boom, cyber-physical system (CPS) has been widely used in several fields. However, malicious data tampering in communication networks may lead to degradation of the state estimation performance, which may affect the control decision and cause significant losses. In this paper, for the identification of finite impluse response (FIR) systems with binary-valued observations under data tampering attack, an optimal attack strategy based on the average entropy is designed from the perspective of the attacker. In the case of unknown parameters, the regression matrix is used to give the estimation method of the system parameters, the algorithmic flow of the data tampering attack for the implementation of the on-line attack is designed. Finally, the effectiveness of the algorithm and the reliability of the conclusions is verified through the examples.

In this article, an asynchronous adaptive tracking control approach is presented for a type of hidden Markov jump nonlinear systems with external disturbances. In this joint jump process model, hidden Markov model signifies the dynamics of the actual system, whereas the signal emits from the detector symbolizes the transmitted information. This leads to the phenomenon of asynchronization between the modes of the system and that of the controller. Accordingly, an asynchronous observer is developed by using the mode information from the detector to develop an asynchronous control approach. The observer contains a disturbance estimation part, to compensate the unknown external inputs. Utilizing the backstepping scheme, a strict-feedback asynchronous tracking controller is formulated, guaranteeing that all signals within the closed-loop system are semi-globally uniformly ultimately bounded in probability. Finally, the validity of the presented methodology is illustrated by means of a simulation example.

This paper proposes a command filter-based decentralized adaptive backstepping practical prescribed-time (PPT) tracking control scheme for a class of non-strict feedback interconnected systems with time varying parameters, unknown control coefficients, unmodeled dynamics, input deadzone and saturation. By the aid of the characteristics of Gaussian functions, the obstacles arising from the non-strict feedback terms are successfully solved. By constructing a novel time-varying scaling function and utilizing nonlinear mapping, the PPT tracking control is developed. The estimations of dynamical uncertainties resulting from unmodeled dynamics are accomplished by employing auxiliary signals, while the unknown continuous terms are characterized by the aid of radial basis function neural networks (RBFNNs). A superposition of two hyperbolic tangent functions is utilized to approximate input nonlinearity. Utilizing the compact set defined in the command filtered backstepping technique, the problem of unknown control direction is solved without using the Nussbaum gain technique. All the signals involved are proved to be semi-global uniform ultimate bounded, and the tracking error can enter the pre-specified convergence region within a pre-specified time. Simulation results are used to demonstrate the effectiveness of the proposed control approach.

Breast cancer is the most prevalent kind of tumor to occur in females and the primary cause of death for women. Early detection is perhaps the most successful strategy to minimize breast cancer mortality. Early diagnosis necessitates a consistent and efficient diagnostics method that allows doctors to differentiate benign from malignant breast cancers without a surgical sample. The goal of this endeavor is to develop a sophisticated breast cancer diagnosis method. The primary goal of the paper is to reduce the death rate among women by promoting early detection of breast cancer. First, pre-processing techniques such as median filtering and contrast limiting adaptive histogram equalization are used to the obtained raw images. By doing this, the machine-learning model's computational complexity is decreased and the image quality is enhanced. K-means clustering is used to segregate the pre-processed image. Additionally, features including the enhanced local vector pattern, grey-level co-occurrence matrix and local vector patterns are produced in the course of the feature extraction stage. Finally, an optimized deep belief network (DBN) is carrying out the classification process. To boosts the classification accuracy, activation function of DBN (tanh, softmax, ReLu) as well as its weight function is optimized by the proposed grey wolf updated whale optimization algorithm The accuracy of the greywolf updated whale optimization algorithm+DBN is above 93% for datasets 1 and 2 when compared to extant models. Finally, calculation of the performance validates the proposed model's performance.

In industrial process control systems, parameter estimation is crucial for controller design and model analysis. This article examines the issue of identifying parameters in continuous-time models. This article presents a stochastic gradient estimation algorithm and a recursive least squares estimation algorithm for identifying the parameters of continuous systems. It derives the parameter identification model of linear continuous-time systems based on the Laplace transforms of the input and output of the systems. To prove that the techniques given here work, we have included a simulated example.

This article delves into the adaptive heading tracking control of unmanned surface vehicle (USV) by incorporating an event-triggered mechanism and signal quantization. The primary objective is to save communication resources while alleviating the burden of signal transmission. To address time-varying external perturbations inherent in the control system, a disturbance observer is employed for precise estimation. Additionally, a linear model is introduced to delineate the procedure of quantization. By furnishing the controller with purpose-designed quantized control input, the adaptive tracking control system can effectively track desired input without requiring any prior knowledge of the quantized parameters. The article substantiates its claims by demonstrating the system's stability in the absence of quantization considerations and the bounded nature of quantization errors through a series of presented lemmas. Further, the stability of the USV heading control system, integrated with an event-triggered mechanism and signal quantization, is proofed in accordance with Lyapunov stability theory. Finally, the proposed strategy's efficacy and practical applicability are validated through experimental simulations.

In this paper, a modified consensus protocol algorithm is proposed for controlling a group of identical unmanned aerial vehicles (UAVs), which have been subjected to interfering signals during coordinate information exchange, using an unknown parameter in the consensus protocol. The maximum levels of interfering signals in the proposed protocol were adjusted by incorporating a hysteresis function with a dead zone consistent with the initial coordinates and the interfering signal levels. An adaptation algorithm is proposed to address a priori uncertainty regarding the consensus parameters, involving the correction of an unknown parameter by eliminating control signals exhibiting false sign changes. This correction relies on the coordinates in the phase plane, indicating that the delay in maneuver execution occurs at the beginning of the maneuver. Furthermore, by modeling synchronized motion, UAV group consensus is demonstrated for an ideal case devoid of a priori uncertainty regarding control protocol parameters, interfering signals, or consequences. The convergence of the adaptation algorithm was assessed by defining a vector function to track parameter changes during tuning. The monotonically decreasing nature of the resulting curve, along with the finite duration of the tuning process, provides confirmation of the convergence of the adaptation algorithm.