This paper develops an event-triggered adaptive tracking control scheme for strict-feedback nonlinear systems with output constraints. Current methods remain conservative and lack mechanisms for controllable constraint deactivation. To meet both safety and performance requirements, an adaptive constraint-handling mechanism (ACHM) is designed to modulate the constraint effect based on the system state and autonomously deactivate it once the tracking error enters a state-dependent safe region. A state-dependent shift function characterizes the deactivation process and ensures a smooth, analytically tractable transition between constrained and unconstrained phases. Embedding this function into an error-transformation framework yields a self-regulating constraint region that tightens for large errors and relaxes as the error decreases, reducing conservatism without compromising safety. Moreover, a dynamic event-triggered mechanism (DETM) with an error-dependent threshold is incorporated into the adaptive control strategy, enhancing transient responsiveness and significantly reducing communication load. A simulation study on a spring-mass-damper system verifies the effectiveness and advantages of the proposed method.

This paper investigates cooperative formation tracking control strategy of a multiple quadrotor unmanned aerial vehicles (multi-QUAVs) systems subject to mismatched disturbances and actuator saturation, aiming to ensure that each follower tracks the reference trajectory of the leader with a desired geometric configuration within a user-prescribed time. To address singularity problem that arises from the differentiation of scaling functions, a non-scaling virtual control law construction strategy is proposed, in which the time-varying scaling function is directly injected into the control channel as an external gain. Furthermore, RBFNNs are utilized as online approximators, and an anti-windup compensator is designed to reduce adverse effects of truncation errors induced by actuator saturation. Additionally, an event-triggered control is incorporated to effectively save communicational and computational resources, and exclusion of the Zeno phenomenon is rigorously proved. Stability analysis shows that all signals of the closed-loop system are bounded, and tracking error of every quadrotor unmanned aerial vehicle (QUAV) converges to an arbitrarily small neighborhood of origin within prescribed time, with the convergence being independent of initial conditions. Finally, two simulation examples of multi-QUAVs formation flight are presented to validate effectiveness and robustness of this work.

An adaptive fuzzy control strategy is proposed in this paper, which employs a two-channel event-triggered output feedback mechanism tailored to achieve predefined accuracy for nonlinear cyberphysical systems (CPSs) under the influence of deception attacks and denial-of-service (DoS) attacks. Applying existing methods is challenging because both deception attacks and DoS attacks must be considered simultaneously. For this reason, an innovative fuzzy state observer has been developed to address the unavailable damaged system states separately. Furthermore, by combining adaptive fuzzy techniques with single-parameter learning algorithms, deception attacks, fuzzy weights, and external disturbances are transformed into linear composite uncertainties with a single parameter. In addition, the dual-channel event triggering mechanism significantly reduces the burden of communication and computation. At the same time, Zeno behaviour is avoided. The developed control strategy ensures boundedness of all signals in the closed-loop system and is able to limit the tracking error to a predefined accuracy. The simulation examples illustrate the effectiveness of the proposed methodology.

This paper aims to address the problem of fixed time adaptive fuzzy fault-tolerant control for stochastic nonlinear systems in the presence of multiple faults and actuator saturation. During the research, three main challenges are encountered: First, the system is subject to disturbances from stochastic noise and unknown nonlinear terms; second, the system is simultane ously affected by both additive and multiplicative sensor faults; and third, the actuators not only experience faults but also face saturation issues. To tackle these challenges, this paper proposes an innovative adaptive fuzzy fault-tolerant control algorithm. The algorithm leverages the powerful approximation capability of fuzzy logic systems to handle unknown nonlinear problems and employs multiple adaptive laws to address actuator faults and saturation issues separately. Additionally, to reduce the consumption of communication resources, an event-triggered mechanism is introduced, effectively avoiding the occurrence of Zeno behavior. Based on these methods, the proposed algorithm ensures the boundedness in probability of all signals in the closed loop system and drives the tracking error to a small neighborhood around the origin. Finally, the effectiveness of the algorithm is verified through simulation example.

This paper investigates a membership-degree-driven switching framework for interval type-2 (IT-2) fuzzy systems, addressing uncertainties and nonlinearities in cyber-physical systems (CPSs) under multi-channel dynamic switching (MCDS) attacks. The integration of attack behaviors with operating-region information associated with fuzzy membership inequalities enables the construction of an adaptive attack architecture with multiple attack targets and types. Under such a framework, in the observer-to-controller (OTC) channel, the attack switches between denial of service (DoS) attacks and deception attacks, while in the sensor-to-observer (STO) channel, it switches between false data injection (FDI) attacks and DoS-deception composite attacks. Meanwhile, a homogeneous polynomial framework is introduced for modeling system uncertainties and hybrid attack behaviors, along with an observer-based fuzzy control scheme that incorporates a multi-model switching high-order free-weighting matrix (MSHFM). This combined approach effectively mitigates the impact of MCDS attacks and guarantees the exponential stability and H∞ performance of the system. Finally, hardware-in-the-loop (HIL) experiments on active vehicle suspension systems (AVSSs) validate the proposed framework, demonstrating the effectiveness and resilience of the designed attack strategy and control scheme.

Guaranteeing a prescribed convergence-time bound for stochastic nonlinear systems remains challenging when actuator faults and unmodeled dynamics are present. This difficulty becomes more pronounced when both loss-of-effectiveness and bias faults occur, since they may significantly degrade the transient regulation performance of the closed-loop system. To address this issue, this paper develops an adaptive fault-tolerant stabilization scheme for a class of uncertain stochastic nonlinear systems. With the predictable convergence-time mechanism, the adaptive controller yields mean-square practical fixed-time stability, and the corresponding settling-time estimate is explicitly tunable. A Lyapunov-based analysis together with a time-varying gain technique is employed to establish sufficient conditions ensuring boundedness of all closed-loop signals and convergence of the system states within the prescribed convergence-time bound. Moreover, auxiliary normalized performance indices are introduced in the post-processing stage to quantify the transient regulation burden caused by actuator faults. Finally, a stochastic nonlinear system and a quadrotor attitude subsystem are used for simulation validation, covering representative composite-fault cases and four actuator fault scenarios. The results illustrate the performance and robustness of the proposed strategy.

This paper proposes an adaptive fuzzy fault-tolerant tracking strategy for uncertain nonlinear systems subject to full-state constraints and potential faults in sensors and actuators. To address the problems caused by full-state constraints, a barrier function is employed as an analytical tool to ensure that the system's state remains within a safe operational range. Meanwhile, the combined use of parameter separation and multi-adaptive law techniques effectively minimizes the influence of faults on system performance. Additionally, a finite-time command filter with error-compensation signals is embedded to prevent explosion of complexity. To further alleviate the communication load, an event-triggered fault-tolerant controller is designed, effectively avoiding Zeno behavior. The design of this controller ensures that all signals in the system remain bounded and enables the output signal to track the reference signal within a predetermined error range. In the end, the validity of the proposed strategy is confirmed by simulation results.

This article investigates the global output-feedback stabilization for uncertain feedforward nonlinear systems via dual-channel asynchronous periodic event-triggered control. Based on the sampled states, periodic event-triggered mechanisms that eliminate the need for continuous monitoring of system behavior are established to reduce the communication load on both sensor-to-controller and controller-to-actuator channels. Subsequently, an output feedback control scheme is proposed, wherein the time-varying gain is crucial in effectively compensating for system uncertainties and sampling/actuator errors. In particular, the proposed control scheme exhibits greater flexibility, as the sampling periods of different triggering mechanisms can be set to different values without imposing strict constraints on their upper bounds. Based on the established control framework, further investigation is conducted on uncertain feedforward nonlinear systems with inherent time variations and low-order powers.

This paper develops a designated-time adaptive tracking control strategy for nonlinear systems subject to multiple sources of uncertainty, including output constraints, unmodeled dynamics, and unknown disturbances. By incorporating an enhanced fuzzy logic system for parameter estimation and a bounded command filtering approach, this work proposes a systematic tracking control scheme that effectively avoids the issue of computational complexity explosion. To address the significant uncertainties induced by constraints, an asymmetric barrier Lyapunov function is used for analysis and design under the condition of known control coefficients. Furthermore, a controller constructed based on an event-triggered mechanism ensures uniform boundedness and designated-time convergence of all signals. The feasibility and effectiveness of the proposed control method are validated through a practical application case.

This paper proposes an adaptive fast finite-time tracking consensus protocol for high-order nonlinear multi-agent systems. To overcome the limitation of finite-time stability, where the convergence speed slows down when the initial state is far from the origin, the fast finite-time stability theory is incorporated into the multi-agent systems to ensure rapid convergence of the tracking error. Furthermore, the power integrator technique is integrated into the backstepping framework to address the inherent singularity issues in high-order systems. Meanwhile, neural networks are used as online approximators to model unknown nonlinear functions, with the tanh(.) function adopted to mitigate the impact of approximation errors effectively. The developed dynamic event-triggered controller can reduce the frequency of control updates, effectively saving communication resources. Finally, two simulation examples demonstrate the effectiveness of the proposed strategy.

Quadrotor Unmanned Aerial Vehicle (QUAV) have found extensive application across a wide range of practical missions. However, their compact and lightweight configurations render them particularly vulnerable to external disturbances and input delays. This paper investigates a class of prescribed-time prescribed-performance distributed algorithms for non-strict feedback multiagent systems (MASs) with input delays, and applied the proposed algorithms to QUAV. The developed algorithms guarantee the boundedness of all closed-loop signals and ensure that tracking errors converge to a prescribed accuracy bound within prescribed time, even in the presence of external disturbances and input delays. Consequently, the requirements for convergence time, transient performance, and steady-state accuracy are simultaneously satisfied. By incorporating Padé approximation and intermediate variables to mitigate the effects of input delays, integrating finite-time differentiators with an adaptive prescribed-time controller, high-precision and robust control of the QUAV attitude dynamics is achieved. Finally, the effectiveness of the control scheme is demonstrated through a numerical simulation example and a practical simulation example.

The present study explores the issue of fixed-time control for nonlinear systems that are affected by stochastic perturbations. The issue of infinite gain is effectively addressed by employing a function defined by variable gain over time, and two novel theorems are established. In the first theorem, the scenario is analysed in which both the drift and diffusion terms in stochastic nonlinear systems are known, and this demonstrates that the system achieves fixed-time stability in probability. However, given the potential imperfections inherent in real-world systems, such as model uncertainties and external disturbances, the concept of practical mean-square fixed-time stability is further introduced in Theorem 2. Traditional approaches typically rely on parameter-dependent upper-bound functions to estimate the settling time, which lack flexibility and adaptability to varying system requirements. In contrast, the proposed fixed-time control strategy, which includes a prescribed upper-bound on the dwell time, provides greater flexibility. It enables users to specify the upper limit of the dwell time in accordance with practical requirements. Furthermore, the design of the state-feedback controller employs fuzzy logic systems to approximate the unknown drift and diffusion terms in stochastic nonlinear systems, and by integrating a time-varying gain function, it enables the arbitrary specification of the upper bound on the dwell time. To verify the validity of the prescribed control scheme, this paper presents two simulation case analyses. A comparison and analysis of the results with those obtained by alternative methods demonstrates the rationality and quality of the prescribed control mechanism.

ABSTRACT This article proposes an improved command‐filtered adaptive practical fast finite‐time tracking strategy for multi‐input multi‐output (MIMO) stochastic nonlinear systems. Compared to existing approaches, it targets three critical unresolved challenges: (1) intermittent output constraints active only during finite intervals, (2) control singularity in finite‐time backstepping designs, and (3) synergistic handling of concurrent practical imperfections. To address these challenges, a shift‐barrier function combination enables seamless constraint transitions, a piecewise continuous function guarantees singularity‐free control, an innovative disturbance observer estimates unknown disturbances, and Pade approximation mitigates input delay. Theoretical analysis confirms all closed‐loop signals are bounded in probability while satisfying intermittent constraints. In the end, to demonstrate the effectiveness of the proposed strategy, the method is applied to a 2‐link flexible robotic manipulator (FRM) that tracks different trajectories.

This paper investigates a dynamic event-triggered practically predefined-time control (PPTC) scheme for stochastic nonlinear systems (SNSs) under state constraints and input dead zones. A nonlinear state-dependent function (NSDF) is introduced to enforce state constraints. To address input dead-zone nonlinearities and mitigate the complexity growth inherent in backstepping, novel predefined-time compensating filters (PTCFs) and predefined-time filters (PTFs) are designed. These filters guarantee the convergence of filter states within a predefined time and effectively suppress the influence of dead zones. Building on this, a semiglobal predefined-time adaptive fuzzy tracking control algorithm is developed, where a fuzzy logic system (FLS) approximates unknown nonlinear dynamics. Furthermore, a dynamic event-triggered mechanism (DETM) is incorporated into the framework to reduce communication load. The present control scheme ensures tracking error convergence within a predefined time and uniform boundedness of all closed-loop signals in the p-th moment. Finally, a simulation example is conducted to demonstrate the effectiveness of the proposed strategy.

Optimized fuzzy prescribed performance control for stochastic networked nonlinear systems under denial-of-service (DoS) attacks is addressed in this research. Leveraging a meticulously crafted fuzzy estimator, unmeasurable system states during DoS attacks are modeled. Meanwhile, a simplified prescribed performance error transformation facilitates the derivation of a new Hamilton-Jacobi-Bellman equation, enabling the design of an optimized controller. Additionally, in the controller design procedure, fuzzy-logic systems integrated with reinforcement learning (RL) are utilized to approximate the unknown nonlinearities. In the optimized backstepping design, the proposed controller ensures the tracking error converges to the performance bound within a predefined finite time, even during DoS attacks. Moreover, employing Lyapunov stability theory, it is strictly proved that all signals in the closed-loop system are semiglobally uniformly ultimately bounded in probability. An event-triggered mechanism is introduced, which not only alleviates computational burden but also eliminates Zeno behavior. Ultimately, numerical and practical simulations are given to show the effectiveness of the proposed optimization method.

This paper investigates the temperature tracking issue of a continuously stirred tank reactor system under multiple uncertainties. Firstly, an innovative adaptive switching tracking algorithm is developed to realize excellent temperature tracking, achieving higher product qualities. Based on the proposed algorithm, an adaptive command-filter fuzzy controller is constructed for the plant. Different from the traditional controller, the proposed controller combines prescribed performance functions, effectively circumventing the increased complexity and achieving higher accuracy. Furthermore, a modified event-triggered strategy is presented to avoid the potential influence of asynchronous switching. Based on the mode-dependent average dwell time method and multiple Lyapunov functions, the designed controller enables the saving of communication resources without the necessity of strict assumptions. In addition, the effect of non-affine and dead-zone input is considered and the corresponding mathematical model of system input is reconstructed. Finally, the effectiveness of the tracking algorithm is fully demonstrated by the simulation of a continuously stirred tank reactor system.

This work deals with the tracking issue for multi-input multi-output flexible-joint manipulator systems with stochastic noise and performance constraints. By combining the speed function with the neural network control method, an accelerated fixed-time control has been developed that exhibits several attractive features: (1) the proposed fixed-time control scheme has been custom-designed for multi-input multi-output flexible-joint manipulator systems to eliminate the influence of initial conditions on the tracking performance of the manipulator; (2) the phenomenon of the complexity explosion is effectively avoided by employing command filters, thereby significantly enhancing computational efficiency of flexible-joint manipulator systems; (3) prescribed performance control is employed to ensure that the tracking error of link position in the flexible-joint manipulator is strictly confined within predefined boundaries; (4) the introduction of the speed function β enables each component of the manipulator to converge according to a predefined pattern and speed before entering the residual region. Finally, numerical simulations and physical experiments conducted on the Quanser flexible-joint manipulator platform validate the efficacy of the proposed approach.

This paper introduces a dynamic event-triggered adaptive fault-tolerant tracking control strategy, tailored to address the control challenges of stochastic nonlinear cyber-physical systems under sensor failures and deception attacks on the controller-actuator channel. In the design of the control strategy, firstly, a prescribed performance control is introduced to strictly constrain the transient and steady-state performance of the output tracking error. Subsequently, to address the impacts of multiplicative and additive sensor faults as well as unknown nonlinear elements in the system, a fuzzy logic system is employed to alleviate their adverse effects on system performance. Concurrently, to improve the transmission efficiency of control signals, a dynamic event-triggering mechanism is adopted, which adaptively adjusts the threshold parameters according to the real-time tracking performance of the system. Based on the aforementioned approach, a novel adaptive tracking fault-tolerant control scheme is proposed, which can not only effectively cope with sensor faults and deception attacks, but also guarantee the probabilistic boundedness of all control signals and strictly satisfy the preset tracking performance specifications. Ultimately, the effectiveness of the proposed method is validated through two simulation examples.

The present paper investigates the fault-tolerant control problem for nonlinear systems with stochastic noise and dead-zone output. In contrast to the extant literature, the present work effectively compensates for the impact of unknown dead-zone output on the system by constructing a dead-zone output approximation model and introducing a Nussbaum-type function. Furthermore, considering the multiple effects of stochastic noise and actuator faults, this paper employs a fault-tolerant control strategy to reduce the risk of system performance degradation and ensure system safety. On this basis, an efficient and readily deployable fault-tolerant control scheme is proposed through further integration of adaptive techniques and fuzzy logic systems. The stability of the system is thoroughly analyzed by constructing a Lyapunov function and incorporating desired performance criteria, which proves the stability of the proposed control scheme. The efficacy and practicability of the proposed control scheme are substantiated by two simulation illustrations.

This paper presents a reinforcement learning-based adaptive fault-tolerant optimal control strategy for a class of stochastic nonlinear systems subject to dead-zone input, sensor faults, and actuator faults. Initially, a reinforcement learning algorithm with an identifier-critic-actor neural networks structure is introduced, and an innovative optimal control scheme featuring prescribed-time convergence is designed. To tackle the issue of unmeasurable states due to sensor faults, a fault-tolerant coordinate transformation technique is employed. By leveraging the universal approximation property of neural networks, the unknown nonlinear terms in the system, as well as the uncertainties arising from faults and dead-zone, are effectively estimated. Moreover, to optimize the utilization of communication resources, an event-triggered fault-tolerant optimal controller is designed. Theoretical analysis demonstrates that the proposed algorithm ensures that all signals in the closed-loop system remain bounded in probability and that the tracking error converges to a neighborhood of the origin within the prescribed time. Finally, simulation results further validate the effectiveness of the designed control strategy.