International Journal of Adaptive Control and Signal Processing最新文献

In this study, a novel perspective on an adaptive bipartite consensus tracking scheme for multi-agent systems (MASs) in the face of sensor attacks is presented. The bipartite consensus tracking problem of MASs is transformed into the tracking problem of a single agent based on the shortest path method, and the assumption of structural balance for a digraph is removed. A significant challenge encountered in this article is that the original sensor measurement states cannot be directly and accurately measured and used after being subjected to malicious deception attacks. Therefore, to solve this issue, a novel coordinate transformation method including compromised states is proposed. Furthermore, the Nussbaum function is only introduced in the final step to avoid the effects of unknown attack gains resulting from deception attacks in the system, and a set of tuning functions are designed to resolve the singularity issue encountered during the adaptive iterative process of previous studies. A distributed security control scheme is designed using backstepping and Lyapunov stability theory, ensuring that all closed-loop system signals remain globally and uniformly bounded. Simulation results with a group of single-link robots validate the efficacy of the presented approach.

An adaptive fixed-time extended state observer (AFESO)-based nonsingular fast terminal sliding mode control (NFTSMC) method is proposed for a class of underactuated overhead crane systems with unknown external disturbances. Firstly, an improved fixed-time nonsingular fast terminal sliding mode surface is designed, which not only solves the singularity problems in terminal sliding mode control but also achieves system stabilization within bounded convergence time. Moreover, an adaptive fixed-time extended state observer is presented to estimate both matched and unmatched disturbances; then, the estimated value is used for feedforward compensation to compose the controller. Subsequently, to improve control quality and enhance robustness of the control system, a weakly reinforcement learning (RL) algorithm is used to update parameters of a chattering free reaching law parameter, upon which a novel fixed-time auxiliary error compensation system is constructed to solve the input saturation problem. The fixed-time stability analysis of the closed-loop system is derived by Lyapunov theory. Finally, the rapidity and effectiveness of the developed control method are demonstrated through simulation results.

This study focuses on adaptive fuzzy finite-time control for nonstrict-feedback nonlinear systems afflicted with actuator faults and asymmetric dead-zone, utilizing an event-triggered approach. Fuzzy logic systems are used to approximate the system's unknown terms. A relative threshold event-triggering mechanism is devised to reduce communication requirements. This mechanism ensures that the actuator only receives system input when a significant event occurs, thereby optimizing control efficiency. The proposed strategy integrates event-triggered adaptive fuzzy control with the backstepping method to achieve finite-time stability. This approach guarantees that the tracking error converges to a region near the origin, and ensures semiglobal practical finite-time stability for all signals within the closed-loop system. A numerical example and a real-world example of a pendulum system are used to illustrate the efficacy of the control approach.

This study proposes an adaptive gain scheduled attitude control strategy for the reentry phase of reusable launch vehicles (RLVs), which is model-independent and offers a convenient implementation. It originally integrates an extended state observer (ESO) with a sigmoid estimator to address the wide envelope of velocities and altitudes, along with the strong nonlinearities and aerodynamic uncertainties. In particular, the ESO is used for disturbance estimation, whereas the sigmoid estimator is applied for disturbance classification. The proposed design achieves the adaptive online estimation and compensation of the RLV control gain and reduces the model dependence. In addition, the closed-loop finite-gain stability is investigated based on the Lyapunov theory, and the tracking boundedness can be proved. Finally, the effectiveness and robustness of the proposed design are verified based on numerical simulations and Monte Carlo tests, and the advantages of weak model dependence and strong ability on disturbance rejection are highlighted in the proposed design compared with the PID control and linear active disturbance rejection control.

A new adaptive disturbance rejection control scheme is proposed for the turbulence compensation of the aircraft in maneuvering flight. The aircraft dynamics in maneuvering flight are studied under random multidirectional turbulence conditions, based on the mutual switching of several “dominant” wind directions, and a piecewise linear system model is established to represent the aircraft dynamics. A new piecewise adaptive control scheme is designed based on the model reference adaptive control when the aircraft in maneuvering flight encounters the turbulence wind in different dominant directions. Desired closed-loop system stabilization and output tracking have been proven. An aircraft simulation study is shown to verify the effectiveness of our proposed method.

This article addresses the issue of adaptive practical predefined-time (PPT) fuzzy tracking control for nonlinear systems with delayed input. A novel PPT input delay auxiliary system is first developed to eliminate the impact of the delayed input. The practical predefined-time stable (PPTS) command filters are adopted to solve the problem of “term explosion” caused by the derivative of virtual controllers, and the error compensation signals are constructed to reduce the errors caused by the filters. The fuzzy logic systems (FLSs) are exploited to approximate the unknown functions in controlled systems. Furthermore, drawing support from the adaptive backstepping control technique, an adaptive PPT fuzzy controller is developed to ensure that all the system signals are PPT bounded, and the tracking error trends to a small neighborhood near the origin. Eventually, two simulation examples prove the validity of the developed mechanism.

Herein, a new barrier function (BF)-based adaptive integral terminal sliding mode control (ABF-ITSMC) with output constraints methodology is proposed for high-order nonlinear systems (HONSs) exposed to unknown lumped disturbance. The proposed algorithms utilize the backstepping control (BSC) technique for management of high-order mismatched uncertainties by incorporating the dynamic surface control (DSC) via first order low pass filter (FOLPF) to eliminate “complexity explosion.” To deal with the output constraints requirement, a barrier Lyapunov function is introduced for the design of the virtual control law. Besides, an integrate sliding manifold surface is designed to obtain the finite time convergent and singularity free features to enhance the robustness. Additionally, an adaption control gain law is devised by BF to precisely estimate the real-time lumped disturbance without the requirement of any boundary information. Lyapunov stability theory is employed for proving that the uniformly and eventually bounded tracking deviation of the overall system. Lastly, simulations with two cases are conducted to validate the devised method with respect to the strength and effectiveness by comparing the derived outcomes of the method with that of the existing method.

This article presents a decomposition-based least squares estimation algorithm for the multivariate input nonlinear system. By using the hierarchical identification principle, the algorithm breaks down a nonlinear system into two subsystems, one containing the parameters of the linear dynamic block and the other containing the parameters of the nonlinear static block. Treating the unknown variables contained in the information vector of the model is to replace them with the outputs of an auxiliary model. The comparative results between the hierarchical recursive algorithm developed in this article and the recursive least squares algorithm are provided to test the proposed algorithms have lower computational cost and the higher estimation accuracy. Furthermore, the convergence of the hierarchical recursive algorithm is analyzed, which can guarantee the stability of the algorithm. The simulation results confirm the efficacy of the derived algorithm in effectively estimating the parameters of the nonlinear systems.

This article introduces native space embedding for robust adaptive control of ordinary differential equations that contain vector-valued functional uncertainties in a reproducing kernel Hilbert space (RKHS). The proposed approach is based on a two-phase method for analyzing and designing adaptive controllers. In the first phase, a limiting distributed parameter system (DPS), which describes the ideal closed-loop system's performance, is introduced. The limiting DPS is not realizable in practice since it evolves in a generally infinite-dimensional space. In the second phase, consistent finite-dimensional approximations of the DPS are introduced to determine realizable controllers. Uniform ultimate bounds on the trajectory tracking error dynamics are derived for the functional uncertainty classes contained in the native space. These bounds are derived in terms of the power function of the RKHS or in terms of the fill distance of centers that define the scattered basis for approximations. Two numerical examples demonstrate the applicability of the proposed results.

In this article, the adaptive fuzzy fault-tolerant control (FTC) issue of a class of nonstrict feedback uncertain nonlinear systems with deferred asymmetric time-varying (DATV) full-state constraints, dead zone, actuator fault, unknown control directions and disturbances is discussed. To eliminate the restriction that the initial states of the system must be within the constraints, a new deferred constrained (DC) function is devised to prevail over the conservatism of time-varying asymmetric constraints (TVAC). To achieve state constraints and simplify design, a model transformation is formulated to transform constrained system into unrestricted system. The fuzzy logic systems (FLSs) are employed to approximate the uncertain nonlinear function. To handle “explosion of complexity” (EOC), the command filter is introduced to substitute the derivatives of the control laws. To tackle actuator fault, dead zone and unknown control directions, a FTC scheme is formulated using backstepping method and the Nussbaum-type function. Furthermore, it is theoretically proven that the formulated control method can ensure that all signals in system are bounded and the tracking error converges to a small neighborhood including the origin in a finite time. The reliability of the formulated control scheme is demonstrated by the simulation result of the robot manipulator system.