This paper introduces a novel event-driven output watermarking scheme for replay attack detection of cyber-physical systems. Conventional input-watermarking techniques continuously embed watermarks into control inputs, inevitably degrading control performance. In contrast, the proposed method departs from them by employing on-demand, event-triggered embedding directly into system outputs. This strategic shift eliminates the persistent performance penalty associated with input-based methods while ensuring reliable detection by selectively utilizing high-amplitude watermark signals. Furthermore, unlike existing heuristic approaches, a systematic parameter design framework is further developed to analytically reveal the impact of key parameters such as embedding frequency and signal covariance on detection reliability. By formulating the covariance design as a convex optimization problem, the framework enables automated synthesis of near-optimal watermarks under practical constraints. Theoretical and simulation results confirm that the proposed output-watermarking strategy not only bypasses the forward-path filtering effects inherent in input-based methods but also yields a more significant deviation in the residual distribution, thereby improving detection performance without increasing the false-alarm rate. The scheme provides a scalable, resource-efficient, and control-preserving solution for securing CPSs against stealthy replay attacks.

An adaptive tracking control policy is investigated for uncertain nonlinear systems under the finite-time asymmetric output constraints (FTAOCs). Unlike common output constraints, FTAOCs are characterized as constraints that are initially imposed during system operation and are then removed after a certain time. To tackle this challenge, we have designed novel shift and barrier functions that transform FTAOCs into guarantees of boundedness for an auxiliary variable. Additionally, we have developed an adaptive estimation algorithm to estimate unknown parameters and proposed an adaptive control strategy. Simulation studies on the Resistance-inductance-capacitance (RLC) circuits have been conducted to demonstrate the feasibility of our proposal. In comparison with state-of-the-art methods, our algorithm offers the flexibility to simultaneously address both unconstrained and constrained requirements of nonlinear systems, without requiring revisions to the controller structure.

This study introduces a novel finite time fault tolerant controller integrating nonsingular terminal sliding mode (NTSM) and reinforcement learning (RL) strategies for manipulator systems with actuator faults. Leveraging an actor-critic network architecture, the RL algorithm facilitates the computation of the cost function and the approximation of unknown nonlinear dynamics. The inherent properties of NTSM mitigate the effects of parameter uncertainties, thereby enhancing system robustness. Furthermore, an adaptive law is crafted to counteract the deleterious effects of actuator faults. Through the direct Lyapunov function approach, it is demonstrated that the closed-loop system achieves semi-global practical finite-time stability. This control strategy diminishes the dependence on precise model accuracy and augments the system's fault tolerance. The viability of the proposed algorithm is corroborated by simulation results, and its efficacy is further validated through experiments conducted on the 6-DOF Kinova Jaco 2 platform.

In this article, we propose an adaptive neural network event-triggered control (ETC) to suppress the vibration of a high-rise building under uncertainty. This neural network efficiently handles unmodeled components in the system and approximates unknown nonlinear functions. An ETC mechanism with a relative threshold strategy is introduced, balancing the control effectiveness of the active mass damper (AMD) and extending operational lifespan. The ultimate boundedness of the system is verified using the Lyapunov direct method, ensuring convergence of vibration displacement and acceleration toward zero. The efficacy of this control scheme is demonstrated through detailed numerical simulations and experimental analyses.

For a mobile dual flexible manipulator (MDFM) system, this article focuses on the problem of cooperative trajectory tracking under unknown dynamics and time-varying trajectories. The dynamic model of the wheeled mobile manipulator system in 2-D space is established. Taking into account the unmodeled dynamics of the system, unknown terms of the system are approximated by integrating the radial basis function neural network (RBFNN) structure. By introducing the servo system, the cooperative trajectory tracking control (CTTC) strategy is designed, which realizes the system's cooperative operation, time-varying trajectory tracking, and vibration suppression. The performance of the proposed control scheme is verified through theoretical analysis and numerical simulations.

In this article, neural network (NN)-based sliding mode control schemes are proposed for an n-link robotic manipulator with system uncertainties, input deadzone, and external perturbations. A novel error-shifting function is proposed to release initial conditions. NNs are employed to approximate the unknown parameters of both system uncertainties and input deadzone. To update the sliding mode scheme, two advanced sliding mode surfaces with error-shifting function and barrier function are proposed to reduce the dependency of prior information and to realize a finite time convergence result, collectively. It should be pointed out that the proposed methods do not require initial states to satisfy the prescribed constraint caused by the barrier function and can be applied under unknown initial conditions. Furthermore, finite-time convergence for both tracking errors and NN weights is guaranteed. The effectiveness of the proposed schemes is demonstrated by simulation and experiments on the KINOVA robot.

This article discusses cooperative control of a mobile dual flexible manipulator system with asymmetric time-varying output constraints and actuator failures. The shift function and a barrier Lyapunov function (BLF) are used to guarantee output constraints when the initial states of the system violate the prescribed constraints. Moreover, an adaptive fault-tolerant control scheme is developed to deal with actuator failures, while suppressing system’s vibration and achieving cooperative operation. Finally, theoretic analysis proves the uniform bounded stability of the system and numerical simulation verifies the effectiveness of the proposed control method. Note to Practitioners—The purpose of this article is to develop a cooperative dual flexible manipulator system with performance limitations and actuator failures. The system can realize the task of stably grasping and moving a rigid object. The existing research on the grasping task of flexible manipulators only focuses on coordinated operation, which limits the application of the dual flexible manipulator system in practical engineering. To further study the problem, this article considers the output constraints and actuator failures of the dual flexible manipulator system in the actual process and proposes a cooperative fault-tolerant control framework. The control framework uses shift function and BLF to deal with output constraints and adopts adaptive technology to deal with actuator failures. In addition, the cooperative operation task of grasping object with dual flexible manipulators is realized. Simulation shows that this control strategy is feasible.

In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for a class of nonlinear bilateral teleoperation systems. First, an adaptive control scheme is applied to estimate the upper bound of delay, which can resolve the predicament that delay has significant impacts on the stability of bilateral teleoperation systems. Then, radial basis function neural networks (RBFNNs) are utilized for estimating uncertainties in bilateral teleoperation systems, including dynamics, operator, and environmental models. Novel adaptation laws are introduced to address systems' uncertainties in the fixed-time convergence settings. Next, a novel adaptive fixed-time neural network control scheme is proposed. Based on the Lyapunov stability theory, the bilateral teleoperation systems are proved to be stable in fixed time. Finally, simulations and experiments are presented to verify the validity of the control algorithm.

This article focuses on flexible single-link manipulators (FSLMs) under boundary control and in-domain control. The actuators of the system include the dc motor at the end of the joint and m piezoelectric controllers installed at the flexible link, which is regarded as an Euler-Bernoulli beam. The problem of the infinite number of actuator failures, including the partial loss of the effectiveness and total loss of effectiveness, is solved by the adaptive compensation method. By introducing the relative threshold strategy, the event-triggered control (ETC) scheme is proposed to achieve angle regulation and vibration suppression while reducing the communication burden between the controllers and the actuators. The Lyapunov direct method is utilized to prove that the system is uniformly ultimately bounded and both the angular tracking error and elastic displacement converge to a neighborhood of zero. Numerical simulation results are provided to demonstrate the effectiveness of the proposed control law.

A neural learning-based finite-time control policy is presented for a robotic manipulator with unknown backlash-like hysteresis and system uncertainties. Adaptive neural networks are adopted to deal with unknown robotic dynamics. In order to eliminate the effect of unknown backlash-like hysteresis, a robust adaptive term is designed in the backstepping design process. A neural network-based finite-time controller is designed by introducing a fractional order term, which guarantees the finite-time convergence of both neural networks and adaptive terms, and this type of convergence improves control accuracy to a certain extent. With the Lyapunov stability theory, the proposed scheme can be proved to make the errors be semiglobally finite-time stable (SGFTS). The effectiveness of the proposed control is shown by simulation and experimental results.

This article studies a single boundary regulator for the flexible tail of an autonomous robotic fish to implement complex oscillating body motions. The dynamic model of the flexible tail is derived by Hamilton’s principle, and conforms to the partial nonuniform Euler–Bernoulli beam with the uneven parameters. Then, a boundary control at the body–tail junction is proposed to manipulate the oscillation of the flexible tail, which is given in the form as a torque. The exponential stability of the error system is deduced by the integral Lyapunov synthesis. For further considering the boundary disturbance at the same point with control and the distributed disturbance, a disturbance observer is proposed. By appropriately choosing the designed parameters, the uniformly ultimate boundedness with disturbances is proved and the tracking error converges to a small neighborhood of 0. Finally, some simulations are presented to illustrate the effectiveness of the proposed control.

This article develops a robust fault tolerant (FT) control scheme for an n-link uncertain robotic system with actuator failures. In order to eliminate the influence of both the uncertainties and actuator failures on the system performance, the Gaussian radial basis function neural networks are used to compensate for the actuator failures and uncertain dynamics. An adaptive observer is designed to compensate for external disturbance. In addition, in order to accelerate the recovery of system stability after failure, a nonsingular fast terminal sliding mode is given. Finally, the simulation results on a two-link manipulator confirms the superior performance of the proposed neural networks-based FT controller, and the experiment results on the Baxter robot further verify the effectiveness of the control method.

In this paper, vibration control and output constraint are considered for a Timoshenko beam system with input backlash and external disturbances. By integrating iterative learning control (ILC) into adaptive control, two dual-loop adaptive ILC schemes are proposed in the presence of the input backlash. Two observers are designed to estimate two bounded terms, which are divided from the backlash inputs. Based on the defined barrier composite energy function, all the signals are proved to be bounded in each iteration. Along the iteration axis: 1) the endpoint transverse displacements and the endpoint angle displacements are restrained; 2) the transverse vibrations and the rotation vibrations are suppressed to zero; and 3) the spatiotemporally varying disturbance and the time-varying disturbances are rejected. Simulations are provided to manifest the effectiveness of the proposed control laws.

This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore-Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known. Subsequently, only the robot joint is measurable in practice; output feedback control is designed with a high-gain observer to estimate unmeasurable states. Through the Lyapunov stability analysis, system stability is achieved with the proposed control, and the system output achieves convergence without violation of the joint constraints. Simulation is conducted to approve the feasibility and superiority of the proposed NN control.

This paper mainly regulates a flexible wing of a micro aerial vehicle to track two spatiotemporally varying trajectories. By utilizing Lyapunov's direct method, two boundary control laws are designed to guarantee uniform boundedness of the closed-loop target system along the time axis. Based on Schur complement lemma, nonlinear inequalities derived from the theoretical deduction are rewritten as matrixes, which are solved through the LMI toolbox in MATLAB. In addition, the tracking control problem is formulated as an optimization problem. The simulation examples are conducted to prove the effectiveness of the proposed boundary control laws.