International Journal of Adaptive Control and Signal Processing最新文献

In this paper, an iterative learning-based spatiotemporal fault estimation issue in switched reaction–diffusion systems is investigated. Initially, average dwell-time switching rules are utilized to describe a class of switched reaction–diffusion systems characterized by mode jumps. Then, different from the existing fault estimation methods, a fault estimator is designed for spatiotemporal faults to realize an accurate estimation of faults by using the iterative learning strategy. Subsequently, to improve the speed of fault estimation, an adaptive iterative learning-based fault estimation law is proposed, which can achieve faster fault estimation by continuously adjusting the iterative learning gain. Moreover, sufficient conditions for the convergence of the fault estimation error are obtained by using the $��\lambda �$-norm and the mathematical induction methods. Finally, an illustrative example is presented to check the practicality and superiority of the proposed fault estimation scheme.

Multi-agent systems (MASs) are widely used in many necessary fields such as cybersecurity, precision agriculture, satellites, and smart grids. In many practical processes, a proportion of states cannot be obtained directly. Therefore, it is desired to design an observer. This article explores the utilization of output-feedback adaptive control techniques to achieve consensus in MASs based on partial differential equations (PDEs) with reaction-diffusion terms and time delays. First, in dealing with time-invariant delays, an observer according to the Luenberger method is proposed to assess the state of agents. Convergence conditions of the estimated states are obtained by employing Lyapunov functions and Wirtinger inequality. Using the obtained estimated states, a boundary adaptive control strategy is proposed to make the error system consensus. The merit of this control method lies in the fact that communication between agents only occurs at the spatial boundary position, not throughout the entire spatial domain, and the parameters of the system can be constantly changed. Consensus criteria of MASs with time-invariant delays are obtained by using the output-feedback adaptive control, and the results are further generalized to the model with time-varying delays. Lastly, two experimental scenarios are provided to show the practicality of the proposed theories.

In this article, the issue of resilient filtering for descriptor nonhomogeneous Markovian jump cyber-physical systems under hybrid cyber-attacks has been studied. First, occurrences of both deception attacks and replay attacks are modeled as two mutually independent Bernoulli distributed random variables. To depress the ratio of sampled data transmissions over the network, the dynamic version of event-triggered transmission policy is properly proposed. Then, criteria of stochastically admissible for the filtering error systems guaranteeing mixed $��{�H�}_{�\infty �}�$ and passive performance are addressed via linear matrix inequalities (LMIs). The gains of resilient filter and the dynamic event-based transmission policy can be simultaneously gained by optimizing the LMIs. Finally, two examples that include a numerical example and a direct current motor system are provided to testify the feasibility and effectiveness of the developed technique.

This paper deals with the problem of the parameter estimation for feedback nonlinear output-error systems. The auxiliary model-based recursive least squares algorithm and the auxiliary model-based stochastic gradient algorithm are derived for parameter estimation. Based on the stochastic process theory, the convergence of the proposed algorithms are proved. The simulation results indicate that the proposed algorithms can estimate the parameters of feedback nonlinear output-error systems effectively.

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.