International Journal of Adaptive Control and Signal Processing最新文献

This paper addresses the prescribed-time optimized formation tracking control problem for unmanned aerial vehicles (UAVs) while considering collision avoidance. The UAV system is divided into two parts: The position subsystem and the attitude subsystem. To achieve prescribed-time convergence with adjustable accuracy, we develop a novel time-varying transformation function that incorporates both the specified convergence time and desired precision parameters. For collision-free formation maintenance, a modified barrier Lyapunov function is constructed to simultaneously enforce performance constraints and prevent inter-agent collisions. The control architecture is enhanced through an innovative reinforcement learning (RL) strategy, where a fuzzy logic system (FLS)-based actor-critic structure is employed to approximate the optimal control policy by solving the derived Hamilton-Jacobi-Bellman (HJB) equations. The proposed algorithms are validated by simulation.

This article focuses on the recursive parameter estimation problem of exponential autoregressive (ExpAR) models. Considering the difficulty of the nonlinear optimal problem arising in identifying the ExpAR model, Newton search is used to minimize the criterion function to make the search direction more accurate. A penalty term is incorporated into the criterion function to regularize the parameter updates. To achieve higher accuracy performance under white noise interference, a multi-innovation Newton recursive algorithm is proposed to make more use of data. To improve the estimation accuracy, a hierarchical two-stage identification algorithm is adopted. The linear parameters are estimated using recursive least squares, followed by the estimation of the nonlinear parameters through a multi-innovation Newton recursive algorithm with penalty terms. A simulation example is provided to test the effectiveness of the proposed algorithms.

This article is concerned with the issue of the decentralized output feedback control for large-scale nonlinear systems with measurement uncertainty via non-identification adaptive technique. Generalized Lyapunov inequations based two dual-scaling adaptive control methods are proposed, which are applicable to lower/upper-triangular large-scale nonlinear systems with large measurement uncertainty, respectively. Particularly, the uncertain measurement sensitivity is allowed to vary within a positive interval and to be non-differentiable. Resorting to the dual-domination idea, global state regulation is achieved by adaptive output feedback. Specifically, for lower-triangular growth interconnected nonlinear systems with output-polynomial growth constraint, a couple of high-dynamic-gain observer and controller are presented. In contrast, a low-dynamic-gain control scheme is proposed for a class of upper-triangular growth large-scale systems with interconnected nonlinearities satisfying input-dependent growth condition. Notably, the proposed control schemes are both non-backstepping, and all design parameters are given by explicit constraint inequations.

In this article, the observer-based non-fragile $��{�H�}_{�\infty �}�$ tracking control problem of networked control systems (NCSs) with dynamic quantization is studied. The Takagi-Sugeno (T-S) fuzzy model is employed to approximate the nonlinear components of NCSs. By considering the actual network transmission process, the network bandwidth is limited, to ensure the effective transmission of information, dynamic quantizers are introduced from the T-S fuzzy model to the observer and controller to T-S fuzzy model. The goal is to design an observer-based tracking controller to ensure the tracking performance of the system. A two-step decoupling approach is employed to address the coupling terms present in the system model and the controller. The required parameters for the observer-based tracking controller can be determined through the resolution of linear matrix inequalities (LMIs). Finally, through the establishment of the photovoltaic (PV) maximum power point tracking (MPPT) fuzzy model, the viability and practicality of the proposed method within the actual system model have been validated.

This paper proposes a novel approach to integral sliding mode leader-following consensus tracking control for nonlinear multi-agent systems with homogeneous dynamics under hybrid cyber attacks, including deception attacks and denial-of-service (DoS) attacks. The significance of this work lies in its ability to enhance system resilience in adversarial environments while optimizing resource efficiency. To address hybrid cyber attacks, an integral sliding mode surface function is introduced, and an online estimation strategy is presented to estimate the unknown cyber attack patterns without prior knowledge. According to this, a new adaptive sliding mode controller is developed to ensure the reachability of the specified sliding surface. The convergence of multi-agent systems under the specified control strategy is analyzed by means of Lyapunov stability theory and the finite-time method. Furthermore, by introducing auxiliary variables, the dynamic and static event-triggered mechanism within a framework is considered to minimize the use of communication resources, and a strict proof analysis has the property of no Zeno behavior. Lastly, the validity of the theoretical findings is validated through two simulation examples.

This paper focuses on the adaptive fault-tolerant control problem for nonlinear unmanned surface vessel (USV) systems subject to disturbances, actuator faults, and drift angle. First, a unified model is proposed by integrating the Norrbin model, the drift angle dynamics, and the second-order rudder model, aiming to provide a more comprehensive and accurate description of USV dynamic behavior. The corresponding parameters are estimated using data from the “Qingshan” vessel. To address the challenges of coupled time-varying disturbances, actuator faults, and drift angles in the proposed system, an adaptive backstepping control algorithm is designed. The dynamic surface control scheme is introduced to reduce the computational complexity typically associated with traditional backstepping methods. The Nussbaum function technique is used to compensate for the degradation of control performance caused by actuator faults, thereby enhancing the robustness and stability of the USV heading tracking control system. Finally, numerical simulations are conducted based on the established unified model. The results demonstrate that the proposed adaptive controller achieves exceptional performance.

This paper focuses on the parameter estimation issue of the nonlinear exponential autoregressive model with non-Gaussian noise. By exploiting the property that the Cauchy kernel correntropy is insensitive to non-Gaussian noise and the kernel bandwidth, a Cauchy kernel correntropy-based criterion function is presented to obtain robust estimation performance. Since the nonlinear exponential autoregressive model includes the coupling term of the linear and nonlinear parameters, the identification model is decomposed into two fictitious sub-models with a common parameter vector. A Cauchy kernel correntropy-based robust partially coupled recursive least squares stochastic gradient algorithm is proposed by coordinating the coupling term arising from the model decomposition. Compared with the existing recursive least squares and least mean squares algorithms, the proposed algorithm not only demonstrates robustness to non-Gaussian noise but also achieves higher estimation accuracy. The simulation examples exhibit the effectiveness of the proposed algorithm.

The cubature Kalman filter (CKF) is widely used for state estimation in nonlinear systems but struggles with noise uncertainties, non-Gaussian measurement noise, and outliers. This paper introduces a novel Q-learning-based adaptive Student's $��t�$-distribution maximum correntropy CKF (QL-STMCCKF) to address these issues. By integrating reinforcement learning and maximum correntropy criteria with the CKF, the method improves robustness. The Student's $��t�$-distribution kernel handles heavy-tailed non-Gaussian noise, while Q-learning adaptively updates process and measurement noise covariances, enhancing estimation performance. A maximum likelihood estimation (MLE)-based variant, MLE-STMCCKF, is also developed for comparison. The algorithms are tested on a benchmark aircraft tracking problem and validated with offline hardware data from a grid-connected 3-phase voltage source inverter. Comparative analysis highlights the superior performance of the QL-STMCCKF in challenging conditions.

In this paper, a non-linear adaptive direct current controller based on periodic estimation is proposed to significantly minimize the periodic torque ripples for the permanent-magnet synchronous motor system in the discrete-time setting. The discrete-time adaptive controller is designed to track the desired motor currents of motor and provide periodic disturbance rejection by constructing the estimation algorithm based on the digital processing of unknown periodic signals in each consecutive motor period, which requires only knowledge of periodicity. The asymptotic stability of the closed-loop system dynamics is proven through the convergence of current errors to zero as the period of the motor shaft approaches infinity. To test the robustness against periodic uncertainties and disturbances varying with the rotor displacement and to demonstrate the achievement of the desired steady-state response for stator current quality, detailed simulation experiments are carried out with comparisons to the classical deadbeat controller.

The accurate prediction of wind power is crucial for enhancing the integration of wind energy into the grid. Wind power prediction models require high-dimensional input to ensure reliable results. However, challenges arise in obtaining wind power data due to instrument failures. To achieve accurate forecasting, it is essential to fill in missing data and accurately interpret dynamic properties. This study addresses issues such as missing data from sensor failures by introducing the ExZe-DenseLSTM model for precise wind power predictions. The model collects important variables like variance, standard deviation, kurtosis, skewness, and correlation and uses the K-Nearest Neighbors (KNN) technique for data imputation. For better performance, the model mixes DenseNet with Long Short-Term Memory (LSTM), and feature selection is carried out using the Improved Wrapper Approach. To increase the wind power forecasting model's training efficiency and prediction accuracy across a range of learning rates, the Extended Zebra (ExZe) algorithm is used to optimize the model's loss function. The proposed method outperforms existing prediction techniques, producing better outcomes with an MAE of 0.180, MAPE of 107.21, and MSE of 0.094, respectively.