International Journal of Adaptive Control and Signal Processing最新文献

Aiming at the problem that distributed secondary control is vulnerable to FDI attacks in DC microgrid, which affects the stability of the system, and the problems of communication redundancy and energy loss in traditional distributed secondary control, an event triggered fixed time distributed secondary control scheme based on high-order differentiator is proposed. The high-order differentiator is used to estimate the FDI attack signal of the system. Then, using the estimated attack signal, a fixed time distributed secondary controller based on event triggering is designed to eliminate the impact of the attack signal, and realize the adjustment of bus voltage and accurate power distribution. The strict stability and the absence of Zeno behavior are proved. The simulation results show that the proposed control strategy can significantly reduce the impact of attacks on the system, and the event triggered fixed time control reduces the energy consumption of the system and the update numbers of the controller, making the system have better dynamic performance.

As the nonlinearities of many real physical controlled systems become stronger and stronger, they are difficult to be described by accurate mathematical models. For a class of nonlinear single-input single-output systems with all-state constraints and actuator faults, an event-triggered adaptive fuzzy ABLF function fixed-time tracking control method is proposed. The asymmetric barrier Lyapunov function (ABLF) combined with backward step technique is used to ensure the boundedness of the closed-loop system output. In order to solve the problem of limited bandwidth resources, this paper adopts the event trigger mechanism and fuzzy control technology to approximate the unknown function to ensure the convergence of the system in a fixed time. After theoretical analysis, it is proved that the tracking error of the system converges in the small neighborhood of the origin, and it still has good tracking performance when the actuator faults. Finally, by comparing the proposed method with the asymmetric barrier Lyapunov function, which verifies the effectiveness of the proposed method.

The integration of multi-energy within distribution networks has escalated the need for efficient operation and control of integrated energy systems (IES). Addressing the complexities of real-time scheduling and low-carbon optimization, we propose a novel artificial intelligence driven multi-agent system (MAS) approach for modeling the interactions and operations within the multi-agent integrated energy systems (MA-IES) framework. In this framework, distinct components such as electric, gas, and heat networks are conceptualized as autonomous agents, each responsible for managing its domain while interacting with other agents to achieve system-wide efficiency and economical goals. The agents communicate and coordinate through a distributed online optimization framework, utilizing the alternating direction multiplier method (ADMM) to ensure effective consensus despite the inherent nontransparency of information exchange. This MAS based approach allows for dynamic adaptation of strategies based on local data and global objectives, significantly enhancing the responsiveness and adaptability of MA-IES. We further integrate an objective function reliant on a tiered carbon pricing mechanism to assess and minimize the environmental impact of operations. Enhanced by adaptive penalty coefficients within the ADMM, our MA-IES framework demonstrates improved convergence rates and robustness in operational scenarios. Empirical validation through detailed case studies confirms the superior performance of our MAS-based model, demonstrating its potential to realize an efficient and economical low-carbon operation of MA-IES.

This article investigates the event-triggered predefined time gliding formation control for multiple aircraft based on the leader-follower mode. For the banked-to-turn (BTT) aircraft, The main technical challenge is to realize a predefined time control from the aerodynamic control surfaces to the direction of flight speed under an event-triggered mechanism. First, under the leader-follower mode, the desired tracking commands for the leader's trajectory inclination angle and trajectory declination angle are designed, with followers set to track the leader's outputs. Second, for each BTT aircraft, the inner and outer loop control system is constructed, and the virtual angle of attack and flight path angle laws are formulated to decouple the inner-outer structure. For the outer loop system, the predefined time-stabilized of trajectory inclination angle and trajectory declination angle is achieved by introducing the time scale function. Concerning the inner-loop system, a sliding mode surface with predefined time stabilization is constructed, and a predefined time extended state observer (PTESO) is designed to estimate the total disturbances. An event-triggered predefined time control is proposed for the aerodynamic control surfaces to realize the aircraft's flight direction tracking. Finally, the stability of the closed-loop system and the avoidance of the Zeno phenomenon for each aircraft is proved using Lyapunov's theory. The simulation results verify the effectiveness of the proposed formation control in the article.

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.