International Journal of Adaptive Control and Signal Processing最新文献

The initial state inconsistency and iteration-varying trajectory problems are considered in adaptive iterative learning control (AILC) to enhance the tracking performance of the non-strict feedback systems with unknown input nonlinearities. Through constructing an error reference trajectory independence of the reference signal, the restrictions on the initial condition and reference trajectory are both relaxed. Subsequently, a backstepping-based AILC methodology is systematically presented to ensure that the error reference trajectory can be followed by the actual tracking error. Integral Lyapunov functions are employed to design the recursive controllers, avoiding potential singularity problems resulting from the differentiation of gain functions. Rigorous analysis is provided without imposing constraints on the control gain functions to demonstrate tracking error convergence. Numerical simulations are included to illustrate the efficacy of the proposed method.

An adaptive optimal controller is proposed in this paper to maximize the captured power of a variable speed wind power system. The proposed controller is a combination of optimal and adaptive control components. The Adaptive Dynamic Programming technique is used to design the optimal control component to overcome the nonlinear problem of system dynamics and ensure stability. While the neural network is used to approximate unknown disturbances and system uncertainties. After that, the adaptive control component fully compensates for the effects of these unknown elements. Neither optimal nor adaptive control components necessitate prior knowledge of system dynamics. Furthermore, the approximation network updates only the weight matrix norm rather than the weight matrix of the neural network in each interval time, which significantly reduces computation. The stability analysis of the closed-loop system is obtained using Lyapunov stability theory. The correctness and robustness of the control scheme are validated in two different scenarios using MATLAB/Simulink. The presented robust adaptive optimal controller is also compared to other existing controllers to demonstrate its benefits.

This article investigates the fuzzy sliding mode tracking problem for discrete-time nonlinear interconnected systems (ICSs) via a previewable information approach. First, an augmented system containing tracking errors and previewable reference and disturbance signals is established for discrete-time nonlinear ICSs according to a previewable length. Second, novel sliding surfaces are given and some sufficient conditions of making the sliding motion asymptotically stable are derived in term of linear matrix inequalities (LMIs). Next, fuzzy sliding mode control (SMC) laws are designed so that the controlled system can reach the sliding surfaces and keep on them thereafter, and compared to not having previewable signals, these designed control laws can also enable system outputs to better track the reference signals asymptotically. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control law.

The concerns of this article is to investigate the fixed time function combination synchronization (FCS) among three different chaotic systems with disturbances. First, a new stability theorem is presented by virtue of the integral inequality technique and a more accurate upper estimation of the convergence time is given. Next, the fixed time FCS problem is considered for the perturbed chaotic systems in which the scaling functions are generated by the bounded dynamical systems. Besides, the synchronization time is related to the control parameters rather than the initial values. The time upper bound can be calculated out by the simple formula, which is the function of control parameters. Finally, a simulation example is provided to illustrate the correctness of the derived criteria.

In the presented study, the solution of the fuzzy nonlinear optimization problems (FNLOPs) is calculated using a recurrent neural network (RNN) model. Since there is a few research for solving FNLOP by RNN's, we give a new approach to solve the problem. By reducing the original program to an interval problem and then weighting problem, the Karush–Kuhn–Tucker (KKT) conditions are given. Moreover, we use the KKT conditions into a RNN as an important tool to solve the problem. Besides, the global convergence properties and the Lyapunov stability of the dynamic model are studied in this study. In the final step, some illustrative examples are considered to establish the obtained results. Reported results are compared with some others network models.

The Optimization-Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. The ODMGC involves a multi-step optimisation process that categorises grey-scale images of breast thermograms based on mean brightness. Then, based on the grey levels of the pixels, we grouped each categorisation into sub-regions. Followed by each group has undergone individually optimised base enhancement. This process enhances the contrast between cancerous and normal tissues, eliminates over- and under-enhancement, and supports breast tumour diagnosis. The optimised-based enhancement images serve as a reference point for the histogram specification of the V component of the thermograms in the HSV (Hue, Saturation, and Value) model. Further, we evaluated the proposed model using both qualitative and quantitative measures. Finally, using dimension-reduced significant Grey-Level Co-occurrence Matrix (GLCM) features, we validated the results with a Random Forest (RF) classifier. The algorithm was successfully implemented in MATLAB 2020a, and the classifier was developed in Jupyter Notebook using Python. The subjective comparison confirmed the proposed method's superior resolution in normal and malignant cases. The classifier results showed an accuracy of 96.4%, sensitivity of 98.1%, and specificity of 96.9%.

This article is mainly concentrated on the predefined-time adaptive fuzzy control of stochastic nonlinear systems. To handle nonlinear functions that are uncertain, fuzzy logic systems (FLSs) are used to approximate them. Compared with the existing studies, a Lyapunov-type criterion for practically predefined-time stochastic stabilization (PPSS) is put forward to guarantee the stabilization of the system. The stabilization time is merely dependent on one design parameter, which means the parameter can be adjusted to set the stability time.

In this paper, the fixed-time anti-synchronization and synchronization fault-tolerant control problem is considered for two identical Liu-Chen-Liu chaotic systems with uncertain parameters. With the help of fixed-time stability theory and adaptive backstepping method, we propose two novel adaptive controllers. In the control scheme proposed in this paper, the adaptive backstepping technique is used to deal with the unknown parameters contained in the chaotic systems. Besides, the piecewise functional method is used to solve the singularity problem in the controller design process. The designed controllers achieve anti-synchronization and synchronization of two identical Liu-Chen-Liu chaotic systems within the preset time interval. Simulation results are conducted to show the effectiveness of two proposed adaptive controllers.

Most of the existing adaptive filter algorithms pay more attention to improving performance while ignoring the computational complexity and the impact of the impulsive environment. When encountering the impulsive noise environments, the performance of traditional nonlinear adaptive filter may be significantly reduced and usually needs high computational cost. Therefore, this article proposes a hyperbolic tangent Leclerc robust nonlinear adaptive filter based on the low complexity decomposable Volterra model (HTLNAF-DVM). The filter is implemented by imposing a rank-one structure on the full Volterra model to get a product of linear filters, and employs a hyperbolic tangent Leclerc function as a robust norm to effectively improve the robustness against the impulsive noise. In addition, we give the theoretical analyses of the steady-state mean-square performance of the proposed HTLNAF-DVM. Finally, the simulation results prove that the proposed HTLNAF-DVM algorithm has better performance than the existing algorithms and fit well with the theory.

The state saturated recursive filtering issue is researched in this article for nonlinear complex networks with energy harvesting sensors and false data injection (FDI) attacks. In communication networks, the energy of the sensors is used to transmit data from sensors to remote filters. Due to ample energy being a prerequisite for the transmission of data, the energy harvesting technology is provided. During the process of data transmission, the measurement signals may be attacked by the false data. Thereinto, the Bernoulli random variables are used to depict FDI attacks. The primary goal is to devise a filter that minimizes the upper bound for the filtering error covariance. Subsequently, a discussion is shown for the proposed filtering bounded analysis for the filtering error. Finally, a numerical simulation experiment is carried out to demonstrate the applicability and effectiveness for the proposed novel filtering algorithm.