International Journal of Adaptive Control and Signal Processing最新文献

In this work, we investigate the error feedback regulator problem for an unstable wave equation in which the output is anti-collocated with the control input, and all feasible channels have disturbance. Through the implementation of a variable transformation, an auxiliary system is derived where the measured error serves as its boundary output, and all unknown disturbances are positioned at the output terminal. Afterward, an adaptive observer is developed to estimate the state of the auxiliary system, as well as the unknown amplitudes of the disturbance and reference signals, depending on the tracking error. We construct an auxiliary system for this observer, wherein all disturbance estimation terms are consolidated at the end alongside the control. By the backstepping method, the final regulator is obtained which is proved to be applicable. Numerical results are shown for the main results.

Distributed adaptive fault-tolerant consensus control (FTCC) enables nonlinear multi-agent systems (NMASs) to achieve control tasks with expected or slightly reduced performance metrics in the presence of faults in certain components. When dealing with NMASs under sensor faults and input saturation, the control design becomes extremely challenging. Therefore, this paper proposes a novel distributed adaptive FTCC method for a class of strict-feedback NMASs with multiple sensor failures and input saturation. First, to surmount the obstacles presented by multiple sensor failures, a novel constructive design approach combining parameter separation and function recombination is proposed. Then, to tackle the challenges encountered in the design process of the derivative of the virtual control law (VCL), a first-order filter (FOF) is introduced. Furthermore, to alleviate the repercussions caused by input saturation in the system, an auxiliary system is employed to offset the variation between saturated and designed control inputs. Combining the improved backstepping design technique, an adaptive state feedback controller is recursively developed. It has been verified that all signals within the closed-loop NMASs remain bounded, and the output consensus errors converge to an arbitrarily small vicinity of the origin. Finally, simulation experiments validate the efficacy of the proposed controller.

This study addresses the adaptive global predefined-time tracking control problem for a class of switched nonlinear systems with input saturation constraints. Firstly, by introducing the hyperbolic tangent function, a piecewise smooth function is constructed. This function not only effectively approximates the input saturation constraints but also accurately handles them. Subsequently, a multi-dimensional Taylor network (MTN) is utilized to provide a novel estimation method for the nonlinear terms in the backstepping control process. Additionally, a finite-time differentiator is introduced to accurately estimate the first-order derivative of the virtual controller. Notably, the proposed multi-switching-based global predefined-time controller not only ensures that the tracking error converges to a small neighborhood of zero within a predefined time, but also achieves globally uniform ultimate boundedness for all signals in the closed-loop system. Finally, to further validate the effectiveness of the control algorithm, both numerical and simulation examples are provided.

Measurement delays, nonlinearities and external uncertainties cannot be avoided for wind energy system under complex working environment in practical. This article proposes an event-triggered Lyapunov-based model predictive control (LMPC) framework for wind energy systems. To better reflect the practical environment, a nonlinear system model is established for explicitly incorporating measurement delays and external uncertainties. An LMPC method is designed for wind energy system with Lyapunov constraints to improve the control performance. A triggering scheme is proposed for LMPC to reduce optimization computation time in controller design, which makes the proposed LMPC more applicable. The closed-loop stability is demonstrated for the proposed event-triggered LMPC with measurement delays. Finally, simulation experiments are conducted on a 5-MW wind energy system to validate and illustrate the effectiveness of the proposed LMPC approach.

In this article, the method of fault detection using a sliding mode interval observer (SMIO) is studied for uncertain nonlinear singular systems with an actuator fault. Assuming that the actuator fault does not exist, the SMIO of uncertain nonlinear singular systems is designed by two transformations. The output of SMIO is used to construct the upper and lower bounds of the output when the system does not contain an actuator fault, and the fault detection is realized by judging whether the system output exceeds the constructed upper and lower bounds. Based on the system after rank transformation, an adaptive sliding mode fault-tolerant control (ASMFTC) scheme is designed to compensate for the influence of the fault on the uncertain nonlinear system, and a fault estimation strategy is proposed. Finally, the correctness and effectiveness of the proposed method are illustrated by two examples.

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.