This paper proposes a novel dynamic memory event-triggered predefined-time control method for stochastic nonlinear time-delay systems with unknown input hysteresis. Different from existing results, this study focuses on the predefined-time stability of stochastic nonlinear systems. In the design of the predefined-time controller, command-filter technology is integrated, and the impact of filter errors on system performance is effectively mitigated. Considering the significant effect of historical data of unknown input Bouc-Wen hysteresis signals on current input signals, a dynamic memory event-triggered controller is proposed to enhance control accuracy. Simulations are conducted to validate the performance of the proposed control method.

This paper proposes a disturbance observer-based adaptive finite-time singular perturbation control scheme for flexible joint manipulators with state constraints. Firstly, a fuzzy logic system-based observer is designed to estimate unknown external disturbances. Then, a fuzzy adaptive finite-time singular perturbation controller is developed to address model uncertainties and improve the response speed of the rigid subsystem. In particular, the singular perturbation method avoids the design of unnecessary virtual control laws and error compensation signals by decoupling the original system into the reduced-order rigid and fast subsystems, which reduces the computational burden. Stability analysis verifies that the closed-loop signals converge within finite time, while ensuring that all states of the rigid subsystem remain within constraint bounds. Finally, the effectiveness of the proposed control scheme is demonstrated by simulation results.

This article investigates the pursuit–evasion (PE) game under the pure pursuit (or evasion) strategy for multi-uncrewed surface vehicles (USVs), along with the decision-oriented fixed-time velocity regulation controller (FTVRC) design. A novel exponential-type function approximation (ETFA) method is utilized to construct a differentiable performance index (PI), which reflects the decision-making basis for the player. Based on the differentiable PI, the player autonomously decides to generate the expected pursuit and evasion velocity, which includes size and direction information. To enable the player to execute its decision result, the FTVRC with a simple structure and low computational burden is designed to track the generated expected velocity. To cope with the unknown exogenous disturbances, a fixed-time disturbance observer (FTDO) is proposed to estimate the exogenous disturbances in real-time. According to the Lyapunov theory, all errors can achieve fixed-time convergence and the settling time is upper bounded. Finally, the simulation results show that the control scheme proposed in this article can achieve the PE target, and the superiorities of the ETFA method and the fixed-time control (FTC) method are demonstrated in the comparative simulations.

Three-phase inverters have been widely implemented for stand-alone power conversion applications where the utility grid is not available. In these applications, high-quality output voltage regulation of inverters is crucial for the reliable operation of local loads. However, critical load conditions (e.g., unbalanced loads, nonlinear loads, and load variations) bring time-varying load disturbances, deteriorating the steady-state and transient performance of the output voltage. To address this issue, a finite-time adaptive control (FTAC) with a cascade structure is proposed in this article. Firstly, novel adaptive laws are designed to estimate time-varying load disturbances. By incorporating the designed adaptive laws, cascade finite-time controllers are then constructed for both the outer voltage loop and inner current loop. The stability analysis shows that the voltage tracking errors tend to an arbitrarily small neighborhood of zero within a finite time, enabling fast and accurate output voltage control of stand-alone inverters under load disturbances. Meanwhile, all the signals in the closed-loop system are bounded. Simulation and experiment results validate the effectiveness and superiority of the FTAC strategy.

This article proposes a secure recursive estimator-based command filtered event-triggered control (EBCFETC) scheme for cyber-physical islanded AC microgrids (MGs) with unknown nonlinear loads under deception attacks. A discrete-time dynamic model of the islanded AC MG is given, and the voltage regulation problem is transformed into an output feedback tracking control issue. First, a secure recursive extended state estimator is designed to handle unknown nonlinear loads and estimate the immeasurable MG states under deception attacks. In particular, an upper bound on the estimation error covariance of the recursive estimator is obtained, and the real-time gain matrix is derived from the upper bound. Then, an EBCFETC strategy is developed by utilizing the backstepping technique, and an event-triggered mechanism is introduced to reduce the transmission frequency of control signals. The scheme guarantees that both the estimation error and the signals of the closed-loop system are bounded, and the tracking error converges to a small neighbourhood near zero. Finally, the simulation results verify the validity of the proposed EBCFETC method.

This article studies the recursive state estimator-based fuzzy adaptive control scheme for discrete-time uncertain nonlinear systems with state saturations and missing measurements. A fuzzy extended state Kalman filter is proposed to obtain the estimated states of the system. First, an auxiliary function on the nonlinear rate of change is constructed and approximated using a fuzzy logic system, which reduces the error caused by directly given the upper bound of the autocorrelation function. Subsequently, the real-time gain and upper bounds on the error covariance of the estimator are obtained, and the stability analysis of the estimation algorithm is given. Furthermore, a recursive estimator-based control strategy is developed, where the virtual control function and adaptive law are designed to enhance the performance of the controller. The proposed control method ensures that the closed-loop system signals are bounded and the errors are converged. Finally, the validity of the scheme is demonstrated by illustrative example.

This article presents an event-triggered adaptive acrlong NN command-filtered control for a class of multi-input and multi-output (MIMO) nonlinear systems with unknown rate-dependent hysteresis in the actuator and the constraints on full states. The acrlong ETM is used to reduce the communication frequency between controller and actuator. The command filter technique is first employed to solve the dilemma between the nondifferentiable control signal at triggering instants and rate-dependent hysteresis input premise while avoiding the "explosion of complexity" problem. During the backstepping design, the barrier Lyapunov functions are utilized to guarantee that system states will stay in certain regions and the unknown nonlinear items are approximated by adaptive neural networks. The compensating signals are constructed to eliminate filtering errors. The estimates of unknown hysteresis parameters are updated by adaptive laws. The stability analysis is given and the effectiveness of the proposed method is verified by simulation.

This article studies the problem of finite-time (FT) adaptive constraint control for flexible joint robots (FJR) stochastic system. First, by combining the command filtered backstepping method with FT control, not only does it solve the “explosion of complexity” problem, but it also ensures that the error of the FJR stochastic system converges in FT. Second, the asymmetric time-varying output constraint problem of FJR stochastic system is solved by designing a nonlinear transformation function (NTF) only depends on the system output, which reduces the difficulty of system stability analyses and relaxes the constraints on the initial value of the output. Third, by exploiting the fuzzy logic system, the adverse effect of the unknown stochastic nonlinear disturbances generated by the harmonic drive of the FJR system is effectively overcome. Furthermore, by utilizing the boundary information of dead-zone slopes, the adverse impact of the dead-zone inputs on the efficacy of control is effectively compensated. Finally, the Lyapunov approach is employed to indicate that the signals are convergent, and the simulation results demonstrate the effectiveness of the control algorithm.

This article focuses on the finite-time formation control problem for multiple quadrotor unmanned aerial vehicles (QUAVs) with malicious attacks, and presents a finite-time fuzzy adaptive output-feedback control scheme. First, the positional and angular velocities are estimated by developing the fuzzy state observer to replace actual values for controller design. Second, the problem of “computational complexity” is avoided and the effect of filtered error is eliminated by introducing the finite-time command filtered technique and constructing the error compensation mechanism, respectively. Meanwhile, the adaptive parameters are used to estimate the boundaries of malicious attack signals, overcoming the challenge of requiring bounds for attack signals in the backstepping design process. Based on the finite-time stability theory, it is proven that all signals are bounded in the multiple QUAVs system, and the formation tracking errors can converge to a sufficiently small neighborhood near the origin in a finite time. Finally, the validity of the algorithm is verified by a simulation example.

In this article, the improved command-filteringbased fixed-time fuzzy adaptive control of strict-feedback uncertain nonlinear systems with full state constraints is studied. Firstly, a new filtering approach is designed to improve the convergence speed and solve the “explosion of complexity” problem. And the filtering error can be effectively eliminated by a novel compensating mechanism. Then, barrier Lyapunov function with the filtering approach handles full state constraints in the system, so that the states will not violate the specified ranges. The proposed method ensures the tracking error converges to the neighborhood of the origin rapidly in a fixed time. Finally, the effectiveness and advantages of the proposed method is verified through simulation of a single link robot system.

The widely studied finite/fixed-time control guarantees fast convergence of the controlled systems. Yet, the adjustment of settling time remains complex, and the optimality of control signal is not considered. In this article, a predefined-time optimal tracking control scheme is proposed for uncertain nonlinear systems with input saturation. With the aid of fuzzy approximation, the reinforcement learning actorcritic structure is established, in which the actor and critic network are used to implement control actions and evaluate execution costs, respectively. Then, by introducing the actorcritic structure into the command filtered backstepping design framework, the approximated optimal control signals containing the predefined-time parameter are derived, and an easily tunable upper bound on the settling time with respect to the predefinedtime parameter is obtained. With the approximation of saturated nonlinearity using tanh function, the input saturation constraint is satisfied. Stability analysis proves that all signals in the closedloop system can converge to a small neighborhood near the origin in a predefined time. Eventually, comparative simulations on quadrotor attitude system are carried out to assess the validity of the developed control strategy.

This brief presents a modified event-triggered command filter backstepping tracking control scheme for a class of uncertain nonlinear systems with unknown input saturation based on the adaptive neural network (NN) technique. First, the virtual control functions are reconstructed to address the uncertainties in subsystems by using command filters. A piecewise continuous function is employed to deal with the unknown input saturation problem. Next, an event-triggered tracking controller is developed by utilizing the adaptive NN technique. Compared with standard NN control schemes based on multiple-function-approximators, our controller only requires a single NN. The closed-loop system stability is analyzed based on the Lyapunov stability theorem, and it is shown that the Zeno behavior is also avoided under the designed event-triggering mechanism. Simulation studies are performed to validate the effectiveness of our controller.

An adaptive fuzzy finite-time singular perturbation control is proposed for flexible joint manipulators with state constraints. First, the flexible joint manipulator system is decoupled into a rigid subsystem and a fast subsystem through singular perturbation technique. Second, a finite-time controller is introduced to improve the response speed of the rigid subsystem so that it can converge within a finite time. And then, all the rigid subsystem states are confined within the scope of the constraint by the barrier Lyapunov function. Third, the model’s uncertainties and unknown external disturbances are handled by adaptive fuzzy technique. Finally, the effectiveness of the new control scheme is illustrated by the simulation.

This article deals with the tracking control problem for a class of multi-input and multioutput (MIMO) nonlinear systems with uncertain dynamics under the premise of feedback path transmitted by dynamic event-trigger mechanism. The neural network adaptive plant model is designed to generate predictive system states for controllers. Command filters are introduced to fix the jumping problem of virtual controllers while avoiding the issue of “explosion of complexity” caused by the recursive differentiate behavior in conventional event-triggered backstepping controllers design. Moreover, dynamic event-trigger conditions are constructed to decide the feedback path aperiodically transmit plant states instants. Simulation results indicate that this proposal can reduce the communication times considerably without degrading system performance.

This article presents a modified command filter backstepping tracking control strategy for a class of uncertain nonlinear systems with input saturation based on the convex optimization method and the adaptive fuzzy logic system (FLS) control technique. First, the effect of complex uncertainties is eliminated by introducingncommand filters and a single FLS. Then, the update laws of FLS weights are designed based on the convex optimization technique. Next, a new piecewise continuous function is employed to deal with the input saturation problem. The closed-loop system performance is also analyzed using the Lyapunov stability theorem and the Lasalle invariant principle. Finally, the simulation and experimental results are presented to show the effectiveness of our controller.

This article focuses on the problem of designing an adaptive fuzzy event-triggered finite-time output feedback control for stochastic nonlinear systems with input and output constraints. A fuzzy observer is designed to estimate the unmeasured states. The quartic asymmetric time-varying barrier Lyapunov function is established to ensure constraint satisfaction. By utilizing the stochastic theory, finite-time command filtered backstepping method and event-triggered mechanism, a finite-time event-triggered controller is recursively designed, which can not only guarantee finite-time convergent property, but also reduce communication pressure. Meanwhile, the matter of “explosion of complexity” is removed by introducing the finite-time command filter and the effect of filtered errors is offset by constructing error compensation signals. Moreover, an auxiliary system is introduced to handle the input constraint. Finally, the effectiveness of the theoretical results is demonstrated by the simulation example.

This article considers the situation of sensors transmit plant states to controller in a dynamic event-triggered manner and develops a fuzzy-model-based adaptive command filtered tracking control method for nonlinear strict-feedback systems with uncertain dynamics. First, the dynamic event-trigger rules are designed and implemented in sensor-to-controller channel to reduce the communication frequency in network controlled system. Then, the adaptive fuzzy-model is designed to generate approximated states for controller during the event-trigger intervals to avoid an open-loop like system operation, which can be caused by the traditional zero-order-hold policy. Moreover, command filter technique is incorporated to solve the issue of “jumping of virtual controller” and circumvent “explosion of complexity” problem during the fuzzy-model-based event-triggered controller design process. Meanwhile, the filtering errors are eliminated by compensate signals. Finally, two simulation examples are conducted and the results show the effectiveness and superiority of the proposed method.

We develop a modified adaptive control scheme for uncertain nonlinear systems based on command-filtered backstepping in this study. Our main task is to construct the virtual stabilizing functions in the presence of the uncertain control gain functions. First, the command-filter technique is employed to predict the system performance. Next, a new adaptive control strategy is introduced to stabilize each subsystem. In the final step, the actual stabilizing function is designed by utilizing the hyperbolic tangent function. The proposed strategy overcomes the problem of the input saturation and guarantees the convergence of all the system signals. The simulation study for a numerical nonlinear system and experimental results from a PMSM control platform are presented to validate our control strategy.

This article considers the problem of finite-time (FT) tracking control for a class of uncertain multi-input-multioutput (MIMO) nonlinear systems with input backlash. A modified FT command filter is designed in each step of backstepping, which ensures the output of the filter can faster approximate the derivatives of virtual signals, suppress chattering, and relax the input signal limit of the Levant differentiator. Then, the corresponding improved FT error compensation mechanism is adopted to reduce the negative impact of filtering errors. Furthermore, a neural-network-adaptive technology is proposed for MIMO systems with input backlash via FT convergence. It is shown that desired tracking performance can be implemented in finite time. The simulation example is presented to illustrate the effectiveness and advantages of the new design method.