Zong-Yao Sun

This paper addresses the fixed‐time adaptive control problem for nonlinearly parametrized high‐order systems with unknown time delays and asymmetric output constraints. A suitable Lyapunov‐Krasovskii functional combined with a backstepping control method forms the foundation for developing a fixed‐time control framework, complemented by a piecewise barrier Lyapunov function capable of dealing with symmetric and asymmetric output constraints or unconstrained outputs without redesigning the controller structure. During the procedure of control design, the tuning function is constructed to remove the overparametrization of unknown parameters. The presented adaptive control strategy ensures that system states are globally bounded and converge to the origin in an adjustable time through selected design parameters. Meanwhile, the system output can always be kept within prespecified output constraints. Notably, unlike existing schemes that handle symmetric constraints or known time delays, the proposed controller offers a unified framework capable of accommodating asymmetric time‐varying constraints and completely unknown delays without requiring any redesign. Finally, the effectiveness of the proposed control scheme is validated through two simulation studies: a mass‐spring system and a numerical example.

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 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 work presents a robust adaptive tracking control strategy for a category of nonlinear systems characterized by multiple uncertainties, encompassing sensor/actuator faults, external disturbances, and fully unknown nonlinearities. The challenges lie in avoiding the circular reasoning in traditional neural network algorithms and circumventing the escalating complexity by repeating differentiation of virtual control signals. To the escalating complexity arising from repeated differentiation of virtual control signals, we introduce a series of finite‐time command filters. By means of dynamic signals with fast convergence for compensating tracking/errors and an event‐triggered scheme, the constructed controller guarantees that all closed‐loop signals are uniformly bounded, while the tracking error converges to a small neighborhood of the origin within a finite‐time instant, and the Zeno phenomenon is avoided. Lastly, simulation results for a robotic manipulator system substantiate the efficacy of the developed scheme across a spectrum of fault scenarios and initial conditions.

This work addresses a challenging adaptive tracking issue for uncertain nonlinear systems, featuring a nontriangular structure, full state constraints, and dead‐zone input. A new constrained adaptive control approach is presented by exploring the adding one power integration and fuzzy control. With the selection of a suitable barrier Lyapunov function, we design an adaptive controller, which guarantees the desired tracking accuracy and the boundedness of all system signals. Besides, the system states do not violate the constraint. The proposed method is verified using numerical and practical examples.

This article addresses a key tracking control problem for brushless direct current motor servo system (DCMSS): ensuring specified tracking precision before the designated time for the angle and position of the motor relative to a reference trajectory, even when subjected to input saturation and disturbances, while maintaining the tracking error within a predetermined, time‐dependent constraint during operation. Based on a time‐dependent barrier function defined by the settling time and ultimate tracking precision, we design a robust continuous state‐feedback control by combining a designated‐time disturbance observer with an anti‐windup compensator. Improving upon existing DCMSSs tracking schemes, the proposed strategy drives the error in the tracking falls within a specified zone before the designated time. Additionally, it minimizes excessive overshoot and transient oscillations while assuring the ultimate uniformly boundedness of all signals. At last, two groups of simulations for the typical application of DCMSS (i.e., active steering system) are conducted to demonstrate our method's effectiveness.

This paper explores the adaptive exponentially designated-time stabilization issue via event-triggered feedback for a kind of uncertain high-order nonlinear systems. The motivation mainly comes from the following two challenges: the undesired singularity problem arising from infinite control gains at the prescribed-time instant, the effective tradeoff between the control amplitude and the triggering duration. The goal is to build an event-triggered mechanism comprising a skillful triggered rule alongside a time-dependent threshold. Utilizing the designed control strategy, the solutions' existence and the prevention of Zeno phenomenon are successfully guaranteed by using a new transformation equipped with a time-varying function and redesigning the continuous state-feedback dominance approach with an array of integral functions involving embedded sign functions. Better than existing prescribed-time methods, our approach not only ensures that state variables converge to a small compact set before a designated time and stay there henceforth, and converge to the origin exponentially, but also ensures that the controller continuously works on the whole-time horizon. Two illustrative examples are given to show the effectiveness of the devised scheme.

This article explores a new strategy for designated time stabilization of stochastic time-varying nonlinear systems. Conventional prescribed-time stabilization has limitations in practical engineering due to singularities induced by infinite control amplitude and a lack of manipulation of the state response after a prescribed time. To address these challenges, we build a hybrid stabilization controller using state-scale techniques and a finite-time stabilization process that is bounded in probability over the full range, guaranteeing that the closed-loop system has a solution that is almost surely unique at a designated time. Compared to the current prescribed stabilization results, the proposed strategy not only ensures that the state of the system converges in probability to a compact set at a designated time and belongs to the set after which it eventually enjoys fast finite-time convergence. Finally, the effectiveness of the strategy is verified by simulating a real mass-spring mechanical system.