International Journal of Adaptive Control and Signal Processing最新文献

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.

The main objective of this study is to develop a non-fragile saturation controller for a fractional-order (FO) permanent magnet synchronous generator (PMSG) and utilize a fuzzy quantized mechanism. To this end, first, fractional-order control is introduced into the non-linear PMSG model to improve the convergence rate beyond the existing integer-order control techniques. Next, the non-linear PMSG model is converted to linear sub-models using the Takagi-Sugeno (T-S) fuzzy method. Utilizing refined sector conditions, we conduct a theoretical analysis of the Mittag-Leffler (M-L) stabilization for the resultant closed-loop systems. This analysis employs certain inequality techniques applied to the M-L function and FO Lyapunov theory. Moreover, based on the polytopic representation approach, sufficient conditions ensuring M-L stabilization are established by a closed-loop system. Furthermore, we have devised two separate convex optimization approaches to effectively reduce the actuator costs and expand the admissible initial region (AIR). Finally, the method proposed in this study demonstrates the superiority and efficiency of the derived results.

Within the framework of privacy protection, this paper explores the issue of finite-time dynamic event-triggered fuzzy control for vehicle suspension systems (VSSs) with imperfect communication channels. Firstly, a privacy protection mechanism is introduced into the VSSs to protect the privacy of data, which avoids data being accessed or misused without authorization, thereby enhancing the safety of the driving experience. Secondly, under the privacy protection mechanism, a novel dynamic event-triggered mechanism (DETM) is proposed for VSSs. Compared to existing DETMs, the designed DETM not only adjusts the threshold based on real-time environmental changes and achieves better system performance while reducing data transmissions but also reduces the risk of data leakage during transmission. Then, a finite-time state encrypted fuzzy controller subject to imperfect communication channels is designed, which ensures that the closed-loop system is finite-time bounded. Simultaneously, the requirements for the VSSs are also met. Finally, a simulation example is provided to illustrate the effectiveness of the designed control approach.

This article proposes an event-triggered command filtering adaptive fuzzy tracking control strategy for nonlinear systems with multiple unknown high-frequency gains and actuator constraints. During the design phase, a fuzzy logic system is used to approximate unknown nonlinear functions, whereas a novel equivalent transformation technique is introduced to simplify the design complexity of multiple input constraints by converting the input dead zones and saturation nonlinearities into a unified functional form. Subsequently, command filtering technology is used to address the issue of “complexity explosion” in control systems, and two additional adaptive laws are developed to assist in designing the compensation mechanism, which can both handle multiple unknown high-frequency gains and eliminate the impact of filtering errors on the control performance. Furthermore, a relative threshold event-triggered controller is developed to decrease data redundancy, and the viability of its triggering mechanism is demonstrated by excluding the Zeno phenomenon. The designed controller can ensure that the tracking error converges to a small vicinity near the origin, while all signals within the closed-loop system remain bounded. Finally, the effectiveness of the proposed solution is validated through simulation results.