ABSTRACT In this paper, we investigate the practical prescribed‐time (PPT) tracking control problem for a class of nonlinear non‐strict‐feedback systems with arbitrarily bounded initial states. A radial basis function neural network (RBFNN) is employed to approximate unknown and possibly non‐differentiable system dynamics. By introducing a preset error trajectory, the tracking error is guided along a predefined evolution, it not only avoids the singularities and parameter‐coupling issues commonly associated with time‐varying mappings but also allows arbitrarily bounded initial errors. A Lyapunov‐like function is further constructed to guarantee closed‐loop stability while implicitly accommodating the filtering errors generated by the dynamic surface control (DSC) and maintaining bounded transient behavior. Based on Lyapunov analysis, all closed‐loop signals are shown to be uniformly bounded. The effectiveness of the proposed method is demonstrated through two illustrative examples.
ABSTRACT This brief paper points out a serious flaw in the development of the controller of [“Adaptive finite‐time cooperative platoon control of connected vehicles under actuator saturation,” Asian Journal of Control 24 (2022): 3552–3565], making the proposed approach therein untenable.
ABSTRACT To enhance ship dynamic positioning in polar environments under severe wind and ice loads, this paper proposes a discrete‐time sliding mode control strategy based on the set‐membership filtering. A discrete‐time nonlinear ship model is first established within a linear parameter‐varying framework to address modeling nonlinearities. Then, a recursive set‐membership filter is designed to provide robust interval state estimations of system states and disturbances under unknown‐but‐bounded noises. These estimation values are incorporated into an discrete‐time sliding mode control law for active uncertainty suppression. In addition, stability is established via difference inequality analysis, ensuring uniform ultimate boundedness. Finally, simulations results validate the effectiveness and precision of the proposed method under extreme conditions.
ABSTRACT This paper proposes an implicit event‐triggered set membership estimator (IET‐SME) for discrete‐time LPV systems subject to bounded uncertainties. By extracting implicit information from non‐triggering conditions via support functions, virtual measurement updates are constructed to restrain zonotope uncertainty growth during communication silences. A co‐design framework jointly tunes observer gains and triggering matrices and derives an ‐type index for the ultimate estimation‐error bound. Simulations on vehicle dynamics and an 8‐dimensional mass‐spring benchmark show that the IET‐SME improves estimation accuracy while reducing communication rates, demonstrating its effectiveness in scenarios with limited communication resources.
ABSTRACT This paper establishes criteria for controllability and observability in a class of linear time‐invariant impulsive differential‐algebraic equations (DAEs) defined on a time scale. Controllability is defined as the ability to transfer the system state from any initial condition to any desired state within a finite time interval using an appropriate control input. Observability is characterized as the ability to uniquely determine the initial state from output measurements over a finite time interval. The main contributions are the derivation of necessary and sufficient conditions for both state controllability and state observability. These conditions are formulated in terms of rank‐based tests and the properties of Gramian matrices, which are constructed using the Drazin inverse to handle the system's algebraic constraints and impulsive dynamics. The theoretical results are validated by solving a illustrative controllability and observability problem, demonstrating the efficacy of the proposed approach.
ABSTRACT In this article, we study a class of distributed adaptive time‐varying group formation tracking (TGFT) problems for linear multi‐agent systems (MASs) with multiple leaders under a directed topology, where the followers' inputs are subject to saturation and the leaders' control inputs are unknown. TGFT aims to drive followers to adjust their position states under the guidance of one or more leaders within each subgroup, thereby synchronously achieving the desired time‐varying sub‐formations. The leaders, whose control inputs are unknown, create non‐predictable and actionable trajectories. First, we thoroughly examine the TGFT problem and accomplish agent grouping by manipulating the properties of the Laplacian matrix. Subsequently, we propose a control protocol for group formation with multiple leaders. This protocol effectively accounts for the effects of unknown control inputs from the leaders, ensures efficient communication, establishes feasible constraints for formation tracking, and ultimately enables followers to successfully complete the desired formation tracking. Furthermore, we prove the stability of the multi‐agent system using the designed protocol constructed in this article, demonstrating that the general linear MAS can accomplish time‐varying formation tracking under saturated control inputs. Finally, we validate the theoretical results through numerical simulations, confirming the practical applicability of the proposed approach.
ABSTRACT This paper proposes an adaptive event‐triggered tracking control scheme for a class of nonlinear switched systems within the predefined time. Neural networks (NNs) are employed to approximate the uncertain functions. Compared with the traditional switching threshold strategy, an improved event‐triggered mechanism is developed to avoid threshold mismatch during dynamic processes while effectively enhancing the system performance. During the controller design process, using a polynomial fitting method in the neighborhood of zero ensures the continuity of the controller and avoids singularity problems. The boundedness of all signals within a predefined time is proven through the construction of a common Lyapunov function via event‐triggered adaptive backstepping control, and the Zeno behavior is excluded. Finally, with the aid of the simulation results, the effectiveness of the designed control technique is shown.
ABSTRACT For stochastic nonlinear high‐order systems with unmodeled dynamics together with time‐varying both state and input delays, this paper investigates the fixed‐time tracking control problem for the first time. First, an adaptive neural network‐based controller is designed by integrating the neural network method with adaptive backstepping to solve algebraic loop issues under non‐strict feedback. An improved Lyapunov–Krasovskii function is constructed to compensate for time‐varying state delays, while dynamic and compensation signals are used to handle unmodeled dynamics and input delays, respectively. Then, based on the semi‐global practical fixed‐time stability theory, the boundedness of all signals in the closed‐loop system is proven, demonstrating that the convergence time is independent on system initial values. Finally, simulation examples are given and demonstrate the effectiveness of the devised control scheme.
This paper investigates the linear constrained regulation problem for discrete-time delay systems and uncertain discrete-time delay systems. The control input is assumed to be subject to asymmetric constraints. The novelty of the proposed approach lies in the consideration of cases where the origin of the control input is situated on the boundary of the constraint set, a situation frequently encountered in real-world systems. We use the concept of positive invariance to establish delay-independent conditions, formulated as linear programs, for designing a state feedback law that ensures constraint satisfaction and asymptotic stability of the closed-loop system. A numerical example is presented to demonstrate the effectiveness of the proposed methodology.
This paper addresses the adaptive control problem for vehicle platoon systems (VPSs) with uncertain target trajectory and unknown control direction. Unlike the existing control methods, an online real-time reconstruction method is introduced to predict the reference trajectory utilizing the generalized regression neural network (GRNN) according to some historical data. On this basis, an adaptive control strategy based on a dynamic-memory event-triggered mechanism (DMETM) is developed, and the Nussbaum-type function method is utilized to handle the unknown control gain. The presented control strategy allows the vehicle to trace the predicted position value of the uncertain target. Under the proposed adaptive neural controller, it can be proved that all signals within the closed-loop system maintain semi-global uniform boundedness. Finally, a simulation example validates the effectiveness and feasibility of the proposed control method.
Abstract Robot‐assisted telerehabilitation systems have recently garnered significant attention within the robotics community, largely due to the effectiveness of master‐slave bilateral teleoperation in delivering rehabilitation therapy. These systems facilitate real‐time physical interaction between therapists and patients remotely through interconnected robotic manipulators. The growing demand for such remote solutions stems not only from a shortage of therapists and limited access to clinical facilities, but was further underscored by the COVID‐19 pandemic, which emphasized the need for tele‐systems capable of maintaining continuous rehabilitation services during lockdowns and social distancing. However, communication time delays, primarily caused by data transmission over the internet or wireless networks, remain a major obstacle to achieving optimal system performance, safety, and user experience. This paper presents a review focused on the impact of communication delay in bilaterally controlled robotic systems for telerehabilitation. We begin by introducing the general architecture of a typical telerehabilitation system, followed by an in‐depth discussion of how delays affect robotic performance and control. We then examine recent advancements in control strategies designed to mitigate these delays. The survey paper highlights key achievements, identifies persistent challenges, and outlines future research directions for developing robust, delay‐tolerant telerehabilitation systems.
Abstract Volatile organic compounds (VOCs) are major air pollutants and pose significant hazards to human health. The climate chamber is widely used for VOC emission detection. The reliability of detection results depends on whether the temperature and humidity in the chamber strictly satisfy the required standards, which imposes requirements on temperature and humidity control in practice, while the climate chamber system exhibits highly nonlinear dynamic characteristics with strong multivariable coupling and significant uncertainties, making precise control theoretically challenging. To address this issue, an adaptive nonlinear temperature and humidity controller for the climate chamber used for VOC emission detection is proposed. Although the climate chamber model exhibits a complex pure‐feedback structure with these characteristics, the implicit function theorem is employed to enable direct construction of the control law via backstepping without additional decoupling or linearization. Meanwhile, model uncertainties associated with the water mass in the temperature control tank and that in the dew‐point humidity generator, including unknown control coefficients, are considered. For the first time, the Nussbaum gain technique combined with an adaptive method is employed in the climate chamber closed‐loop system to compensate for these uncertainties. In addition, actuator faults in the heating equipment are considered to reflect practical operation. Finally, the effectiveness of the proposed control method is verified through theoretical analysis and comparative simulations.
The remarkable performance of the Autonomous Underwater Vehicle (AUV) makes it a valuable tool for exploring resources and conducting marine scientific research. Under the directed communication topology, the paper addresses the problem of distributed finite-time error constrained adaptive cluster synchronization for heterogeneous multi-AUV systems. To achieve cluster synchronization in multi-AUV systems, certain AUVs are selected as pinning nodes, and a novel distributed finite-time adaptive pinning control algorithm with adaptive pinning gains is proposed. A tan-type barrier Lyapunov function (BLF) is employed to constrain the position and orientation errors. In addition, the integral quadratic constraints (IQCs) are employed to characterize nonlinear model uncertainties, and an adaptive strategy is designed to estimate the model uncertainties. Then, external ocean disturbances are estimated by a neural network (NN) adaptive mechanism. It is proved that position and orientation errors converge to a small neighborhood of zero in finite time. Finally, to validate the effectiveness of the proposed strategy, simulation and comparative studies are conducted.
This article presents an active vibration controller that employs homogeneous system properties to suppress the vibrations of a cable-mesh reflector antenna caused by external disturbances. An overall cable-mesh reflector antenna dynamic model with piezoelectric (PZT) actuators is established using the finite element method. To facilitate the active vibration controllers design, the overall cable-mesh reflector antenna dynamic model is transformed into a modal coordinate system, and its order is reduced based on the modal cost analysis method. A linear-quadratic regulator (LQR) is first designed, and then upgraded to a homogeneous controller by employing the generalized homogeneous system theory and saturation function. The homogeneous controller and LQR are compared in the suppression of the free and forced vibrations of the antenna. For the active vibration control of the antenna, it is demonstrated that the designed homogeneous controller, compared with LQR, has faster convergence speed, better control performance and robustness for the suppressions of different types of vibrations. The effectiveness of the designed homogeneous controller for the active vibration control is verified by the simulation results on the 5-meter diameter cable-mesh reflector antenna.
ABSTRACT This paper deals with the problem of event‐triggered non‐fragile control for networked control systems (NCSs) with time‐varying delay. The first step is to design a thorough dynamical framework for NCSs that includes uncertainty components. This framework incorporates time‐varying delays, external disturbances, and parameter uncertainty. The closed‐loop system with a non‐fragile term is integrated into the event‐triggered scheme (ETS) via Lyapunov‐Krasovskii functionals (LKFs), ensuring the system is asymptotically stable (AS). Furthermore, the design conditions for controller performance are formulated, and our results are expressed in terms of linear matrix inequalities (LMIs). The consequences of the suggested strategy are demonstrated numerically using MATLAB with different parameter settings. These results demonstrate that the proposed strategy surpasses existing approaches, showcasing its superiority.
Abstract This study explores an encoder/decoder mismatched parameter for input quantization with an algorithmic recipe to construct LF for CT nonlinear systems. The sign‐definiteness of LF determines the decomposition of the state space into attractive and nonattractive segments; a CT sector is constructed, and CLF determines its boundary. Here, quantization parameters exhibit a time‐varying ratio due to nonsynchronous adjustment, which causes quantization discrepancy issues that are attenuated by a hands‐off controller based on On‐Off logic to acquire asymptotic stability. Theoretical research and simulation investigations confirm the efficacy of the technology for input quantization.
ABSTRACT This article presents a rigorous proof of convergence for an iterative algorithm that identifies Two‐Time‐Scale Systems (TTSS) using adaptive prefilters. The proof is grounded in the Banach Fixed‐Point Theorem (Contraction Mapping Principle) and establishes that the iterative mapping of the algorithm is a contraction on the compact, convex space of cutoff‐frequency pairs . Three complementary results constitute the main contribution: (i) a quantitative bound on the off‐diagonal terms of the Jacobian in terms of the spectral‐separation ratio , showing they are and therefore negligible; (ii) an explicit product‐of‐sensitivities estimate for the diagonal terms, proving and uniformly over ; and (iii) an extension from a local to a global contraction via the convexity of and a Mean Value Theorem argument. The framework is validated on the longitudinal dynamics model of the Lockheed F‐104G aircraft, a classical TTSS, where the contraction factors are computed explicitly as and , yielding an overall contraction constant . These results provide the essential theoretical foundation for the practical deployment of the algorithm in complex TTSS identification problems.
ABSTRACT This manuscript deals with designing a practical observer for a distinguished class of tempered fractional‐order systems (TFOS). Tempered fractional calculus modifies classical fractional operators by incorporating an exponential tempering factor, which allows correct modeling of systems that exhibit fading memory while preserving the inherently nonlocal nature of fractional dynamics. In this paper, we propose an observer architecture for nonlinear TFOS subject to a disturbed Lipschitz condition and derive sufficient stability criteria using Lyapunov analysis and free‐weighting matrices, resulting in linear matrix inequalities (LMIs) based on a quadratic Lyapunov function. The main result is the practical generalized Mittag‐Leffler stability of the estimation error, which explicitly depends on the fractional order and the tempering parameter. The efficacy and robustness of the proposed approach are demonstrated through a numerical example.
ABSTRACT This paper considers the leader–follower consensus of continuous‐time linear multi‐agent systems (MASs) with unknown dynamics on undirected topologies vulnerable to denial‐of‐service (DoS) attacks. First, to mitigate limitations on communication bandwidth, a fully distributed adaptive dynamic event‐triggered (ADET) control strategy is proposed. This strategy relies solely on local information, avoids the use of any global network parameters, and ensures system stability while achieving leader–follower consensus. Second, aiming to resolve the previously open problem of consensus for MASs with unknown dynamics under DoS attacks, we design a distributed adaptive law incorporating exponential terms and double damping coupling weights. By constructing an appropriate Lyapunov function, we rigorously prove that consensus can still be attained in the presence of such attacks. Furthermore, an online data‐driven learning algorithm is employed to obtain a system representation, and the stability conditions are reformulated into a data‐based linear matrix inequality (LMI) framework. This approach eliminates the dependence on an accurate system model and offers a feasible research pathway for unknown dynamical systems under DoS attacks. Finally, numerical simulations are conducted to validate the effectiveness of the proposed control scheme and supporting theoretical results.
ABSTRACT In this study, the global practical Mittag–Leffler stabilization of a class of nonlinear fractional‐order systems satisfying a (weak) one‐sided Lipschitz condition is investigated. For the considered one‐sided Lipschitz nonlinear systems, a sufficient condition ensuring the existence of a state observer is first established. Then, an observer‐based state feedback controller is proposed, and conditions guaranteeing the practical stability of the resulting closed‐loop fractional‐order system are derived via a linear matrix inequality approach. The separation principle is obtained for the observer‐based control design. The main results extend and improve existing results in the literature. A numerical example is provided to illustrate the effectiveness and applicability of the proposed approach.
