International Journal of Adaptive Control and Signal Processing最新文献

In order to improve the penetration ability of missiles in the end guidance stage, and considering the delay problem when designing the guidance control loop separately, this article mainly studies the design problem of an integrated guidance control law under input saturation and attitude angle constraints. For the lower triangular structure model of the integrated guidance and control system, this article mainly uses an improved backstepping method to design the controller, and introduces an error compensation term in the design of the virtual controller to improve system stability. Introducing auxiliary variables and barrier Lyapunov functions to handle saturation and state constraint problems; in the face of the main problems of atmospheric disturbances, unmodeled dynamic errors of the system, and unknown acceleration of the target, this article mainly adopts a finite time observer for processing. The final simulation results demonstrate the effectiveness and superiority of the controller designed in this article.

This paper presents an adaptive robust constraint controller for a continuous stirred tank reactor (CSTR) system based on a self-organizing fuzzy neural network (SOFNN). Due to the high complexity of the chemical reactions, the CSTR system contains many strong nonlinearities and uncertainties. This is the first time to introduce the SOFNN into the adaptive controller design of the CSTR system, which improves the adaptability to dynamic system changes through the adjustment of the fuzzy network structure. Meanwhile, the time-varying integral barrier Lyapunov functions (TVIBLFs) are employed to ensure the dimensionless reactant concentration and mixture temperature within a reasonable scope, which can improve the stability and safety of the CSTR system. Based on Lyapunov stability analysis, all the signals in a closed-loop system are ultimately bounded. Simulation results substantiate the efficacy of the proposed control scheme.

This paper introduces a novel hybrid approach, termed ZOA-SNN, for fault detection and identification in converter-interfaced microgrids. By integrating the Zebra Optimization Algorithm (ZOA) with Spiking Neural Network (SNN) technology, the proposed method provides a comprehensive solution suitable for both grid-connected and autonomous microgrid operation scenarios. The technique effectively isolates faults in the microgrid while maintaining operation continuity, particularly in islanded conditions. When operating in grid-connected mode, distributed generators (DGs) provide electricity as needed. When the grid is not available, power sharing amongst DGs is controlled by voltage angle droop control. By isolating malfunctioning portions, the proposed protection system reduces load shedding, while DG control guarantees smooth islanding and resynchronization. Evaluation on the MATLAB platform demonstrates the superior performance of the proposed technique compared to existing algorithms such as Augmented Lagrangian Particle Swarm Optimization (ALPSO), Graph Convolutional Network (GCN), and Buffalo Optimization (BO). With an accuracy, recall, precision, and F1-score reaching 98.5%, 99.2%, 99.1%, and 99.1%, respectively, the ZOA-SNN approach excels in fault detection and classification. Additionally, it significantly reduces computation times for parameter calculation, enhancing efficiency in microgrid control systems. These results highlight the innovation and advantages of the ZOA-SNN approach in enhancing the reliability and efficiency of fault detection systems in microgrid environments.

This article is aimed to study the parameter estimation problems of a non-commensurate fractional-order system with saturation and dead-zone nonlinearity. In order to reduce the structural complexity of the system, the model separation scheme is used to decompose the fractional-order nonlinear system into two subsystems, one includes the parameters of the linear part and the other includes the parameters of the nonlinear part. Then, we derive an auxiliary model separable gradient-based iterative algorithm with the help of the model separation scheme. In addition, to improve the utilization of the real time information, an auxiliary model separable multi-innovation gradient-based iterative algorithm is presented based on the sliding measurement window. Finally, the feasibility of the presented algorithms is validated by numerical simulations.

This article investigates the robust secure output tracking control for a class of uncertain two-dimensional (2-D) networked control systems. The 2-D systems are described by the well-known Fornasini–Marchesini (FM) local state-space model, the parameter uncertainties are assumed to reside in a polytopic region, and the deception attacks are supposed to occur randomly in the transmission process. In the problem formulation, Bernoulli random variables are used to characterize the phenomena of deception attacks. As main results, a novel $��{�H�}_{�\infty �}�$ performance analysis condition for the augmented closed-loop system is proposed by using a novel parameter-dependent Lyapunov function and some zero equalities. Furthermore, both parameter-dependent and parameter-independent controllers have been designed, respectively, such that the output of the 2-D systems tracks the output of a given reference model well in the $��{�H�}_{�\infty �}�$ sense. The design conditions are presented in terms of linear matrix inequalities (LMIs). In the end, a numerical simulation on a practical thermal process is applied to illustrate the effectiveness of the proposed method.

In this article, we propose an adaptive Kalman filtering with adaptive window length based on Q-learning for dynamic systems with unknown model information. The iteration step length of the Q-function is quantitatively adjusted through the influence function. The adaptive Kalman filtering algorithm is used to set an appropriate weight matrix for the Q-function to estimate unknown model parameters. One numerical example and a practice-oriented case are given to illustrate the effectiveness of the proposed method. It is shown that this filtering can provide state estimates of best accuracy among all the compared methods when the model mismatch and noise statistical characteristics change.

This article presents a novel multi-thread attracting manifold (MTAM) adaptive control scheme. The novel contribution of this new controller lies in how the attracting manifold design method is spread across multiple estimates of the truth while simultaneously weighting between said individual estimates to arrive at an adaptive composite estimate which outperforms single thread adaptive controllers. A notable benefit of this new formulation is that it allows for both the weighting of multiple threads and monotonic decrease of parameter error to aid the performance of transient error dynamics, without arbitrarily increasing adaptation gains. The benefits of the proposed controller over existing adaptive control schemes are analyzed through two aerospace flight control applications from the literature.

In this article, the issue of adaptive fuzzy finite-time command filtered control is discussed for nonlinear stochastic systems subject to unknown dead-zone constraints and unmodeled dynamics. The packaged unknown nonlinearities are approximated by introducing fuzzy logic systems. An improved technique is introduced to cope with unknown functions with the structure of nonstrict-feedback in the operation of controller design. Under the criterion of finite-time stability, a novel fast convergent control scheme is developed. Additionally, the effect of filter errors bought by the command filters is diminished via applying corresponding error compensating signals and a measurable dynamic signal is adopted to handle unmodeled dynamics. The improved designed controller not only guarantees all the closed-loop signals remain finite-time bounded, but also makes the system output follows the given desirable trajectory under the bounded error. The usefulness of the designed strategy can be verified through the numerical and practical examples.