An event-triggered practical predefined-time controller is constructed in this paper for a class of Euler-Lagrange systems under deception attacks. A novel speed function is introduced to reduce the computational complexity, while novel coordinate transformations are used to reduce the controller design complexity. Combining the novel speed function and barrier Lyapunov function, the closed-loop system converges to any desired neighborhood within the preset timeframe with excellent transient performance. Due to the presence of deception attacks, the Nussbaum gain method and fuzzy universal approximation are used to eliminate attack effects, while the event-triggered mechanism based on the relative threshold is introduced to reduce communication resources and the probability of being attacked. Finally, the efficiency of the proposed method is validated through a robotic arm simulation.

The practical preset time fault-tolerant control (FTC) problem is explored in this article for uncertain Euler-Lagrange systems with input saturation and guaranteed performance. To cope with the uncertainty of the Euler-Lagrange systems, the adaptive neural network (NN) is exploited to approximate the unknown continuous function. Most existing results that consider input saturation and actuator faults simultaneously need to design compensation strategies separately, which increases the complexity of control algorithms. To overcome the above obstacle, the Nussbaum gain technique is used to deal with the effects of input saturation and actuator faults in this article. Besides, with the help of error transformation technology and speed function, the proposed control algorithm can ensure that the tracking error converges within the preset time and its overshoot is constrained within the prescribed performance boundaries. Furthermore, the boundedness of all closed-loop system signals is confirmed. Finally, comparative simulation results are depicted to highlight the superiority of the designed control algorithm.

This article is concerned with the input and output event-triggered control simultaneously for uncertain Euler-Lagrange systems with input saturation via backstepping technology. An auxiliary dynamic system is designed to eliminate the effects of saturation. Fuzzy logic systems are applied to the state observer design, which enables the observer to obtain satisfactory observation results without relying on the system dynamic parameters. The event-triggered weight adaptive law is designed to alleviate system's computational burden. Due to the existence of output triggering, conventional recursive backstepping technology is infeasible as the virtual control law is discontinuous and no longer differentiable. Consequently, dynamic surface control is introduced to circumvent the aforementioned problem. Compared with the existing literature that considers output triggering, the assumption of the system function satisfying the global Lipschitz continuity condition is relaxed in this article. Besides, variable transformation technology is used to ensure that the output is constrained within asymmetric performance functions. Finally, the simulation results are depicted to verify the validity of the derived method.

This article systematically studies the issue of adaptive neural network (NN) output-feedback control for uncertain nonlinear systems using event-triggered output. First, to tackle the problem of unmeasurable states, a compact state observer using event-triggered output is constructed. Then, since the event-triggered output signals are discontinuous, the virtual control laws in backstepping design are no longer differentiable. Hence, the dynamic surface control scheme is introduced to resolve this problem. Unlike existing work requiring system functions to satisfy Lipschitz continuity condition, adaptive NN control is incorporated into the designed algorithm to relax the above constraint. What is more, the event-triggered mechanism is also used for parameter estimation to avoid waste of computing and communication resources. Finally, the results of comparative simulations and the DC brush motor experiment are depicted to demonstrate the practicality and effectiveness of the proposed method.

This article investigates the tracking problem of event-triggered prescribed performance fuzzy fault-tolerant control (FTC) for unknown Euler–Lagrange systems with actuator faults and external disturbances. First, the barrier Lyapunov functions (BLFs) and prescribed performance functions are synthesized to guarantee that the tracking errors satisfy the preset transient performance. Different from existing prescribed performance control methods, which require the initial values of the tracking errors to be within the prescribed performance functions, an error transformation method is introduced to ensure that the tracking errors with any bounded initial values can enter the preset boundaries within a preset time. Then, considering the unavailability of system parameters, the fuzzy logic systems are used to approximate unknown parameters of the system. What is more, to solve the problem of limited communication and computing resources in practical systems, an improved event-triggered control (ETC) scheme is proposed, which can reduce the communication and computation burden without satisfying the input-to-state stability assumption. Meanwhile, the Zeno phenomenon can be avoided. Furthermore, the effects of actuator faults and the event-triggered mechanism are handled by Nussbaum gain technology. Finally, the superiority of the proposed control algorithm is verified by simulation results.

In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.

This article is devoted to designing a novel aperiodic sampled-data-based event-triggered control strategy for Takagi–Sugeno fuzzy systems. First, via taking the structural features of fuzzy subsystems and the available information of fuzzy membership functions into consideration, an improved fuzzy-dependent adaptive event-triggered mechanism, which designs different adaptive event-triggered mechanisms for corresponding fuzzy subsystems, is proposed to provide extra design flexibility and further optimize communication efficiency. Then, the two-side looped-functional method and dynamic partitioning approach are introduced in the construction of the novel Lyapunov–Krasovskii functional (LKF). These two methods contribute to deriving preferable stability criterion and stabilization approach via relaxing the positive definite constraint on LKF and fully utilizing the inner system state during the whole aperiodic sampling interval. Eventually, two simulation examples are introduced to verify the effectiveness of the proposed control strategy and its advantages in lightening communication frequency.

The paper considers the output-feedback issue of uncertain nonlinear systems under false data injection (FDI) attacks. A fuzzy state observer is constructed to observe unavailable state values and compensate for adverse effects caused by FDI attacks. Since the event-triggered mechanism (ETM) is introduced to the controller-to-actuator and sensor-to-controller channels simultaneously, the system output is discontinuous, which leads to a non-differentiable virtual control law in the backstepping method. In this scenario, the conventional backstepping approach becomes impractical. The dynamic surface control (DSC) scheme is utilized to overcome the above obstacle, where first-order filters are introduced to avoid differentiating the virtual control laws. Unlike the existing literatures that consider output triggering, our method doesn't require the system function to satisfy the global Lipschitz continuity condition. Finally, numerical simulation outcomes are provided to showcase the efficacy of the designed control strategy,