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.

Nowadays, humanoid robots are expected to possess perception and interaction abilities that can be applied in social applications. In this paper, efforts are made to enable a humanoid robot to identify potentially interactive human groups while avoiding pedestrians in dynamic scenarios. To this end, an integrated framework is proposed. In this framework, a group identification module is first developed to classify individuals into groups and effectively infer the group behavioral trend by monitoring their temporal characteristics. Then, an interactive decision maker based on Laplacian dynamics is established to guide the humanoid robot in making interactive decisions to ensure the target groups. Based on the above perception and decision designs, a novel robot behavior planning algorithm is designed to both approach interactive targets in a socially compliant manner and avoid non-interactive ones. Simulation and experiments based on a humanoid robot have demonstrated that the framework can achieve higher social planning speed and better interaction quality while maintaining satisfactory avoidance performance. These findings indicate that the Laplacian-based decision-maker can enable humanoid robots to efficiently respond to groups with various behavior patterns.

This paper presents a learning-enhanced predefined-time adaptive optimal control strategy for a quadrotor unmanned aerial vehicle subject to disturbances. First, a predefined-time disturbance observer with a tunable convergence time bound is developed to ensure rapid and accurate estimation. Within a command filtered backstepping architecture, actor-critic neural networks are incorporated to achieve adaptive optimal control with learning capability for both position and attitude subsystems. Specifically, novel learning laws facilitate the rapid online updating of network weights, where the critic network approximates the value function while the actor network optimizes the control policy to minimize control cost. The proposed framework effectively compensates for disturbances and filtered error effects, ensuring that all tracking errors converge within a predefined time. Rigorous analysis establishes the predefined-time stability of the closed-loop system. Finally, comparative simulation results are provided to demonstrate the effectiveness of the proposed strategy.

This paper addresses the adaptive neural network-based boundary control problem for a rotating flexible beam system (RFBS) subject to unknown distributed and boundary disturbances, input dead-zone nonlinearity, and parametric uncertainties. The system is composed of a flexible beam with one end fixed to the center of a rotating disk, the other end is attached to a tip mass. The system is governed by coupled partial and ordinary differential equations (PDEs–ODEs), presenting significant challenges due to its infinite-dimensional nature. To achieve simultaneous vibration suppression and strict enforcement of boundary output constraints, a novel adaptive boundary control scheme is developed. The proposed controller incorporates a barrier Lyapunov function (BLF) to handle output constraints, radial basis function (RBF) neural networks to compensate for unknown system dynamics and dead-zone effects, and a disturbance estimator to reject boundary perturbations. By constructing a composite Lyapunov function candidate, the uniform ultimate boundedness (UUB) of the closed-loop system is proved via Lyapunov stability analysis. Numerical simulations are conducted to illustrate the effectiveness of the proposed control approach.

This paper investigates the asynchronous impulsive stability problem in stochastic large-scale networks (SL-SNs) subjected to denial-of-service (DoS) attacks. Unlike centralized synchronous impulsive effects, decentralized asynchronous impulsive effects allow each node to possess different impulsive sequences and impulsive strengths based on local information and self-status. The concept, namely node-dependent average impulsive interval, is introduced to tackle the difficulties arising from asynchronous impulsive effects. Subsequently, a piecewise continuous differentiable balancing function is designed to balance the impulsive instants and non-impulsive intervals of each node. Based on the Lyapunov function and graph-theoretic methodology, novel sufficient conditions to guarantee the exponential stability of the system are given by using the balancing function. Theoretical methods are effective for SL-SNs concurrently exhibiting stabilizing and destabilizing impulses, avoiding a classified discussion of these two cases and successfully giving criteria for achieving exponential stability. Particularly, theoretical analysis reveals the potential impacts of DoS attacks and mixed impulses on the stability of SL-SNs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approaches.

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.

In this article we focus on solving the optimal control problem for stochastic game systems while considering multiple constraints. Initially, applying the asymmetric time-varying mapping functions, the constrained nonzero-sum stochastic differential games are transformed into the ones in unconstrained forms. Moreover, the actor-critic architecture is designed to achieve the Nash equilibrium, the critic fuzzy logic systems (FLSs) and actor FLSs are utilized to approximate the optimal value functions and the control policies, respectively. Subsequently, a dynamic event-triggered mechanism is devised and an additional dynamic variable is defined to characterize the past triggering information, which provides a larger inter-event time. Mathematical analysis reveals the stability of closed-loop system, the weight convergence and the characteristics of dynamic event-triggered mechanism. Finally, simulation results prove the effectiveness of the proposed 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.

A novel observer-based fuzzy adaptive fault-tolerant control approach is proposed for multi-motor synchronously driving servo systems in the situation of a single-motor malfunction. Different from most existing fault-tolerant control strategies based on analytical redundancy, this paper leverages inherent hardware advantages of multi-motor systems and develops a structural redundancy-based fault-tolerant control by directly turning off the faulty motor, which enhances the reliability of the remaining healthy motors driving the asymmetric system. To further address the unknown nonlinearities and measurement inaccuracies introduced by the unbalanced faulty system, a fuzzy logic system is developed to identify the state-based unbalanced torque and additional friction dynamics, and a state observer is then designed to estimate the load speed. Moreover, the command-filtered backstepping technique is utilized in the control design process to reduce the computational complexity caused by the repeated signal derivations. After stability analysis, an experiment on the multi-motor prototype is implemented to show the effectiveness of the designed fault-tolerant control approach.

In the application of multimotor driving systems, achieving high control performance while reducing the occupation of communication networks holds practical significance. This article proposes an event-triggered fixed-time command filtered control strategy that not only facilitates load tracking and motor synchronization but also conserves communication resources. In tracking control design, the fixed-time control is incorporated into the backstepping procedure to accelerate convergence rate and improve tracking precision. Command filters are utilized to reduce computational complexity, and a compensation mechanism with double powers is constructed to eliminate filtering errors. In synchronization control design, a practical grouping control approach tailored for four motors is proposed, with control inputs superimposed on current commands to quickly achieve speed synchronization and reduce mechanical abrasion. Subsequently, an event-triggered mechanism is designed for the final composite control signal, which greatly conserves communication resources while maintaining desirable performance. The closed-loop system is proven to be practical, fixed-time stable, and free of Zeno behavior. Experiments conducted on a four-motor driving turntable demonstrate the effectiveness of the proposed strategy.

This article concentrates on the distributed fixed-time adaptive event-triggered attitude control problem for multiple quad-rotor unmanned aerial vehicles (QUAVs) with prescribed performance. By utilizing the fuzzy logic system and constructing a piecewise continuous function, the unknown nonlinear dynamics of multiple QUAVs and the problem of singularity are skillfully addressed, respectively. The command filter that has fixed-time convergence is devised to avert the “explosion of complexity” problem, while the impact of filtered error is eliminated by virtue of the fractional-power-based error compensation signals. Moreover, a fixed-time performance function is embedded into the distributed attitude control algorithm to ensure that the synchronization errors converge to the preassigned performance confines. It is strictly proved that all closed-loop signals are fixed-time bounded, and the disagreement errors are steered into a small region nearby the zero in a fixed time. Finally, numerical simulations are given to demonstrate the efficiency and superiority of the devised fixed-time control scheme.Note to Practitioners—This article aims at designing an event-triggered distributed attitude control algorithm to relax the communication burden for multiple QUAVs subject to external disturbances. In practical applications, the communication bandwidth and the onboard energy of QUAVs are limited, while the traditional time-triggered approaches neglect these realistic restrictions. Thereby, by incorporating a relative threshold event-triggered mechanism into the command filtered backstepping design process, not only can the “explosion of complexity” issue and the impact of filtered error be surmounted, but also the frequency of controller updating is reduced. Additionally, the construction of a prescribed performance function with fixed-time convergence results in the improvement of both transient and steady-state performances for multiple QUAVs, thus meeting practical requirements more effectively.

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.

A dynamic event-based optimal learning scheme is provided in the paper for nonlinear systems subject to constrained inputs and stochastic disturbances. An actor-critic structure is constructed to learn the stochastic optimal solution, which includes critic fuzzy logic system (FLS) and actor FLS. The experience replay technique and gradient-descent adaption method are used to periodically tune the critic FLS, which can approximate the optimal cost function. Based on the static event-triggered control mechanism (ETCM), a dynamic ETCM is designed, which can incorporate past triggering information and generate a longer inter-event time. The actor FLS is updated at aperiodic jumping points, which can approximate the optimal control policy. The combination of dynamic ETCM and learning structure ensures the stochastic stability of closed-loop system. The efficiency of the controller is illustrated on a numerical example and a manipulator system.

The issue of optimal output feedback control of uncertain nonlinear systems with quantized input is considered in this work. The fuzzy logic system is employed to approximate unknown nonlinearities and optimal cost. By incorporating the observer technique into command filtered backstepping control framework, the feedforward quantized control signal is designed. Then, the optimal feedback control signal for the constructed affine system is derived via single network adaptive dynamic programming. Finally, an optimal output feedback quantized control scheme is proposed. With the aid of adaptive compensating technique, the requirement for prior knowledge of quantization parameter is eliminated. The boundedness of all the signals in the closed-loop system is proved, and the output of system can reach the reference trajectory. Comparative simulations are implemented to verify the effectiveness of the proposed control strategy.

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.

In this paper, a discrete-time adaptive fuzzy command filtered backstepping control scheme is presented for the altitude and attitude control problems of the quadrotor unmanned aerial vehicle (UAV). The discrete-time controller is designed by using a novel command filtered backstepping method based on the discrete-time system model of the quadrotor, and model uncertainties are handled by using adaptive fuzzy control. Furthermore, the problem of causality is solved by utilizing the first-order filters. The effectiveness of the proposed control scheme is demonstrated by simulation and experiment.

The control design method for a class of non-strict feedback nonlinear systems is studied in this brief considering uncertain nonlinearities and unknown non-symmetrical input dead-zone. Combining with the finite-time command filtered backstepping (FCFB) technique, a novel finite-time adaptive control approach is proposed in which a neural network-based methodology is adopted to cope with the uncertain nonlinearities in the non-strict feedback form. The input dead-zone model is transformed into a simple linear system with unknown gain and bounded disturbance which is estimated by an adaptive factor. Using the finite-time Lyapunov theory, the system convergence is proved. And the effectiveness of the proposed control scheme is verified through comparative numerical simulations.

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.

This article investigates the problem of command-filtered event-triggered adaptive fuzzy neural network (FNN) output feedback control for stochastic nonlinear systems (SNSs) with time-varying asymmetric constraints and input saturation. By constructing quartic asymmetric time-varying barrier Lyapunov functions (TVBLFs), all the state variables are not to transgress the prescribed dynamic constraints. The command-filtered backstepping method and the error compensation mechanism are combined to eliminate the issue of "computational explosion" and compensate the filtering errors. An FNN observer is developed to estimate the unmeasured states. The event-triggered mechanism is introduced to improve the efficiency in resource utilization. It is shown that the tracking error can converge to a small neighborhood of the origin, and all signals in the closed-loop systems are bounded. Finally, a physical example is used to verify the feasibility of the theoretical results.

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.

In this article, an adaptive neural-network (NN) command-filtered output-feedback control strategy is proposed for a class of stochastic nonlinear systems (SNSs) with the actuator constraint. The problem of "explosion of complexity" existing in the conventional backstepping design procedure for SNSs is successfully resolved based on the command filter technique, and the error compensation mechanism is introduced to remove effectively the influence of filtered error. By using the NNs to identify the unknown nonlinear functions, a neural-network-based state observer is designed to estimate the unmeasurable states of the SNSs. Based on the quartic Lyapunov function, the stability of stochastic closed-loop systems is analyzed. It is proved that all signals of the closed-loop systems are bounded in probability, and the tracking error approaches a small neighborhood of the origin in probability. Finally, the effectiveness of the developed control algorithm in this article is verified by a comparison example.

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.

In this note, state-estimator-based adaptive control is under consideration for a sort of nonlinear stochastic switched systems by Takagi–Sugeno (T–S) fuzzy modeling and sliding mode technique. A new fuzzy sliding surface is established by a reformed state estimator, and a novel adaptive fuzzy reaching motion controller synthesis is carried out to force the state trajectories onto the designated sliding surface in limited moments. A new mean-square exponential stability of the resultant plant with passivity is ensured under the average dwell time method and stochastic stability theory. The key challenge overcome here is that one conditional assumption involved in the previous sliding mode control-based strategies for the considered systems is no longer required despite the unavailable states, unknown perturbations, and specified switching signal. At last, a practical data communication network model (DCNM) with simulation is offered to verify the feasibility of the theoretical result.

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.

This article investigates the issue of security control for stochastic Markov jump cyber-physical systems (SMJCPS) against actuator failures (AF), randomly occurring injection attacks (ROIA), and inaccessible states by virtue of state estimator-based adaptive sliding mode control (SMC) strategy. The knowledge of the states is generated with an estimator not requesting any input information from which a novel switching surface of linear type (SSL) is established. Then, an adaptive SMC input is developed to ensure the attainability of the SSL in limited steps, almost surely under stochastic noise, unknown ROIA, and potential AF. In the light of the arrival of the SSL and stochastic stability theory, a new stochastically stable criterion for the target SMJCPS operating on the defined SSL is deducted in the occurrence of AF, ROIA, and more generally uncertain transition rates. At last, a simulation study is performed, in which the raised control scheme is realized and certified by a tunnel diode circuit model.

This article investigates the finite-time command-filtered backstepping control problem for dual- motor servo systems. The advantages of the finite-time controller include fast convergence and high robustness, which can improve the dynamic and steady control performance of the system. However, the controller often contains nonlinear and exponential terms increasing the computational complexity. Consequently, for the convenience of practical applications, the command-filtered technique is utilized to decrease the computational burden causing by repeated derivatives in the backstepping process. Besides, LuGre friction model is used to describe the system friction and a fuzzy logic system is designed to handle it. Only one adaptive parameter is estimated, which makes it suitable for industrial applications. Finally, the stability of the closed-loop system is proved and two experiments are carried out to show the advantages of the proposed scheme.

This article proposes an adaptive fuzzy neural network (NN) command filtered impedance control for constrained robotic manipulators with disturbance observers. First, barrier Lyapunov functions are introduced to handle the full-state constraints. Second, the adaptive fuzzy NN is introduced to handle the unknown system dynamics and a disturbance observer is designed to eliminate the effect of unknown bound disturbance. Then, a modified auxiliary system is designed to suppress the input saturation effect. In addition, the command filtered technique and error compensation mechanism are used to directly obtain the derivative of the virtual control law and improve the control accuracy. The barrier Lyapunov theory is used to prove that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation studies are performed to illustrate the effectiveness of the proposed control method.

This brief is concerned with neural network (NN)-based adaptive finite-time output feedback attitude tracking control for rigid spacecraft in the presence of actuator saturation, inertial uncertainty, and external disturbance. First, a neural state observer is designed to estimate the unknown state. Then, based on the estimated state, the adaptive neural finite-time command filtered backstepping (CFB) is applied to construct virtual control signal and controller with updating law. The finite-time command filter is given to avoid the computation complexity problem in traditional backstepping, and the compensation signals based on fractional power are constructed to remove filtering errors. Using Lyapunov stability theory, we show that the attitude tracking error (TE) can converge into the desired neighborhood of the origin in finite time and all the signals in the closed-loop system are bounded in finite time although input saturation exists. The numerical simulations are used to show the effectiveness of the given algorithm.

This article studies the adaptive fuzzy event-triggered output feedback control for nonlinear systems. The systems have the nonstrict feedback structure and variable disturbances simultaneously. A fuzzy state observer with an adaptive parameter is first proposed, and a less conservative condition to solve the observer gains is obtained. By constructing the command filter, the compensator, the fuzzy controller, the event-triggered mechanism, and the adaptive parameter, the adaptive fuzzy output feedback control scheme is built to guarantee that all the signals of the closed-loop systems are bounded, and the tracking errors are less than a prescribed accuracy in finite time. Simulation examples confirm the effectiveness 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 deals with the adaptive neural network (NN) finite-time (FT) command-filtered tracking control problem for a class of nonlinear systems with time-varying full-state constraints. Based on the asymmetric time-varying barrier Lyapunov functions (TVBLFs), the issue of time-varying full-state constraints is settled. The influence of unknown items in the system can be eliminated by the adaptive NN control method. Moreover, the improved FT command filter is introduced to relax the restriction on the input signal and solve the explosion of complexity (EOC) problem. Meanwhile, the FT error compensation mechanism is developed to eliminate the influence of filtering error. It is shown that the proposed strategy can guarantee FT boundedness of all the signals in the closed-loop system and FT convergence of the tracking error. An example verifies the effectiveness of the proposed control method.

In this paper, the observer based neural adaptive finite-time containment control strategy for non-strict feedback nonlinear multi-agent systems is studied. The finite-time command filter is used to overcome the explosion of complexity problem and the established fractional power based error compensation signal is applied to compensate the filtering error caused by the filter. The distributed finite-time command filtered backstepping control method combines with the neural adaptive control technology and state observer is given, which ensures the containment control errors reach to the desired neighborhood of the origin in finite-time in the presence of uncertain dynamics and unmeasurable states in the system. The given numerical simulations show the effectiveness of the proposed control strategy.

Admissibility analysis and control synthesis for nonlinear discrete-time singular systems are considered in this article. With regard to the type-1 and interval type-2 fuzzy singular systems, the partition of membership functions and scale transform is imposed, and new switched fuzzy systems, which are equivalent to the original systems, are established. A relaxed stability criterion is derived to ensure the admissibility of the system by using the piecewise Lyapunov function and singular value decomposition. Moreover, two classes of switched controllers are designed for the systems. One is for type 1 systems and the membership functions are consistent with those of the systems. The other can be applied to both of the fuzzy systems by introducing linear membership functions in each subregion. Two criteria are obtained to guarantee that the closed-loop systems are admissible. Several illustrative examples are provided to show the effectiveness of the developed methods.

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.

This article is concerned with the containment control of multiple manipulators with uncertain parameters. A novel distributed adaptive backstepping strategy is given in the finite-time control framework. The finite-time command filters (FTCFs) used in the strategy can avoid the explosion of complexity problem for conventional backstepping. To further improve the control performance, the filtering errors caused by the used FTCFs are removed by using the error compensation mechanism (ECM). The proposed virtual control signal, the control torque, and the adaptive updating law can guarantee the set tracking errors converge to an adjustable neighborhood of the origin in finite time in the presence of uncertain parameters. Because the virtual control signal and ECM only use the local information, the established method is completely distributed. Two simulation examples are given to show the effectiveness of the proposed scheme.

The problem of adaptive fuzzy observer-based tracking control for nonlinear singular systems is considered in this article. The nonlinear singular systems are composed of two kinds of subsystems, differential subsystem and algebraic subsystem, which are coupled to each other. The systems can be nonstrict feedback structures. Through designing a new state observer and a linear controller, an error system with a tunable parameter is obtained. By constructing new one-sided Lipschitz conditions, the regularization and impulse-free conditions are proposed for the error system. With the help of the tunable parameter in the observer, we design an output feedback controller for the nonlinear singular systems to ensure that all states of the closed-loop system are bounded and the tracking observer errors remain in a neighborhood of the origin. Two examples are provided to illustrate the effectiveness of the presented method.

This article considers the problem of finite-time adaptive fuzzy prescribed performance control (PPC) via output-feedback for nonstrict-feedback nonlinear systems. The fuzzy state observer is designed to estimate the unmeasured system states. To rapidly approximate the derivative of virtual signal, a novel finite-time command filter is proposed. The fractional power error compensation mechanism is established to remove filtered error. By integrating the PPC and command filter technique into backstepping recursive design, a finite-time adaptive output-feedback controller is constructed, and the stability of closed-loop system is strictly proved. The designed control strategy shows that the closed-loop system is practical finite-time stable, and the output tracking error converges to a residual set within prescribed performance bound in finite time. Finally, a numerical comparison and practical examples are provided to demonstrate the validity of the developed finite-time control algorithm.

In this article, the problem of finite-time adaptive fuzzy tracking control for multi-input and multi-output (MIMO) nonlinear systems with input saturation is investigated. The new finite-time command filter is introduced for generating command signals and their derivatives to work out the matter of “explosion of complexity,” and the modified fractional power-based error compensation mechanism (ECM) serves as removing the effect of filter error. Then, the finite-time adaptive control scheme is established via the backstepping recursive design technique. It guarantees all the signals of the closed-loop system (CLS) are finite-time bounded while the output tracking errors are regulated to a sufficiently small neighborhood of the origin in finite time. Finally, the effectiveness of the proposed finite-time control scheme is verified by a numerical comparison example.

In this article, we discuss fast finite-time consensus (FFTC) problems for uncertain nonlinear multiple mechanical systems with actuator faults and time-varying asymmetric output constraints. In order to guarantee the constraints are satisfied, an appropriate nonlinear mapping (NM) is employed to transform the original system with output constrains into an corresponding unconstrained one. Combining neural networks technology, graph theory, fast finite-time control theory, and backstepping technology, the actuator faults are considered to propose a distributed adaptive finite-time consensus (FTC) protocol, which can guarantee the position errors as well as the velocity errors reaching a region in finite time. Finally, an illustrative example is presented to support the obtained theoretical results.

In this article, a new command filtered backstepping based finite-time adaptive fuzzy tracking control scheme for a class of unknown nonlinear systems with full-state constraints is established. First, the proposed finite-time command filter will filtering the virtual control signal and get the intermediate control signal within finite-time, so the problem of calculating complexity will not occur in the backstepping process. Then, the fraction-power-based error compensate signal is set up, which can eliminate the influence of filtering error on the control performance. Considering that the unknown nonlinearities exist in the system, the fuzzy logic system based adaptive control technique is used to deal with them, and only one parameter needs to be estimated. It is shown that the states will not violate the prescribed constrains, all the signals in the closed-loop system are bounded in finite-time and the tracking error can converge to the desired neighborhood of the origin in finite time under the barrier Lyapunov function and fraction-power-based virtual control signals. Finally, the effectiveness of the control method is shown by the simulations.

This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criterion of practical finite-time stability is first developed. Compared with the traditional command filter technique, the main advantage is that the improved error compensation signals are designed to remove the filtered error and the Levant differentiators are introduced to approximate the derivative of the virtual control signal. The finite-time adaptive neural controller is proposed via the new command filter backstepping technique, and the tracking error converges to a small neighborhood of the origin in finite time. Finally, the simulation results are provided to testify the validity of the proposed method.

This article is concerned with the tracking control problem for uncertain high-order nonlinear systems in the presence of input saturation. A finite-time control strategy combined with neural state observer and command filtered backstepping is proposed. The neural network models the unknown nonlinear dynamics, the finite-time command filter (FTCF) guarantees the approximation of its output to the derivative of virtual control signal in finite time at the backstepping procedure, and the fraction power-based error compensation system compensates for the filtering errors between FTCF and virtual signal. In addition, the input saturation problem is dealt with by introducing the auxiliary system. Overall, it is shown that the designed controller drives the output tracking error to the desired neighborhood of the origin at a finite time and all the signals in the closed-loop system are bounded at a finite time. Two simulation examples are given to demonstrate the control effectiveness.

In this paper, the problem of adaptive finite-time attitude containment control for multiple spacecrafts with unknown external disturbances in spacecraft formation flying is investigated. A distributed control strategy combined with finite-time command filtered backstepping (FTCFB) and an adaptive technique is proposed, which can guarantee the containment errors of attitudes between leader spacecrafts and follower spacecrafts reaching the desired neighborhood in finite time. Moreover, the applied second-order sliding mode differentiator in FTCFB can make the output of the command filter fast approximate the derivative of the virtual signal at the second step of backstepping, which can further improve the control quality. A simulation example is given to show the effectiveness of the new designed technique presented.

This paper proposes an adaptive funnel control (FC) scheme for servo mechanisms with an unknown dead-zone. To improve the transient and steady-state performance, a modified funnel variable, which relaxes the limitation of the original FC (e.g., systems with relative degree 1 or 2), is developed using the tracking error to replace the scaling factor. Then, by applying the error transformation method, the original error is transformed into a new error variable which is used in the controller design. By using an improved funnel function in a dynamic surface control procedure, an adaptive funnel controller is proposed to guarantee that the output error remains within a predefined funnel boundary. A novel command filter technique is introduced by using the Levant differentiator to eliminate the "explosion of complexity" problem in the conventional backstepping procedure. Neural networks are used to approximate the unknown dead-zone and unknown nonlinear functions. Comparative experiments on a turntable servo mechanism confirm the effectiveness of the devised control method.

In this paper, the tracking control problem is considered for a class of multiple-input multiple-output (MIMO) nonlinear systems with input saturation and unknown direction control gains. A command filtered adaptive neural networks (NNs) control method is presented with regard to the MIMO systems by designing the virtual controllers and error compensation signals. First, the command filtering is used to solve the "explosion of complexity" problem in the conventional backstepping design and the nonlinearities are approximated by NNs. Then, the error compensation signals are developed to conquer the shortcoming of the dynamic surface method. In addition, the Nussbaum-type functions are utilized to cope with the unknown direction control gains. The effectiveness of the proposed new design scheme is illustrated by simulation examples.

This paper considers the fuzzy finite-time tracking control problem for a class of nonlinear systems with input saturation. A novel fuzzy finite-time command filtered backstepping approach is proposed by introducing the fuzzy finite-time command filter, designing the new virtual control signals and the modified error compensation signals. The proposed approach not only holds the advantages of the conventional command-filtered backstepping control, but also guarantees the finite-time convergence. A practical example is included to show the effectiveness of the proposed method.

Adaptive fuzzy control via command filtering is proposed for uncertain strict-feedback nonlinear systems with unknown nonsymmetric dead-zone input signals in this paper. The command filtering is utilized to cope with the inherent explosion of the complexity problem of the classical backstepping method, and the error compensation mechanism is introduced to overcome the drawback of the dynamics surface approach. In addition, by utilizing the bound information of dead-zone slopes, a new adaptive fuzzy method that does not need to establish the inverse of the dead zone is presented for the unknown nonlinear systems. Compared with existing results, the advantages of the developed scheme are that the compensating signals are designed to eliminate the filtering errors and only one adaptive parameter is required, which will make the proposed control scheme more effective for practical systems. An example of position tracking control for the electromechanical system is given to demonstrate the usefulness and potential of the new design scheme.

This paper is concerned with the finite-time consensus tracking control problems of uncertain nonlinear multiagent systems. A neural network-based distributed adaptive finite-time control scheme is developed, which can guarantee the consensus tracking is achieved in finite time with sufficient accuracy in the presence of unknown mismatched nonlinear dynamics. Such a finite-time feature is achieved by the modified command filtered backstepping technique based on the high-order sliding mode differentiator. Moreover, the proposed control scheme is completely distributed, since the control laws only use the local information. In addition, although mismatched uncertainty nonlinear dynamics are considered, only one parameter needs to be updated for each agent in the control scheme, which will simply the computations and make the proposed scheme more effective for applications. An example is included to verify the presented method.

In this paper, command filtering-based fuzzy control is designed for uncertain multi-input multioutput (MIMO) nonlinear systems with saturation nonlinearity input. First, the command filtering method is employed to deal with the explosion of complexity caused by the derivative of virtual controllers. Then, fuzzy logic systems are utilized to approximate the nonlinear functions of MIMO systems. Furthermore, error compensation mechanism is introduced to overcome the drawback of the dynamics surface approach. The developed method will guarantee all signals of the systems are bounded. The effectiveness and advantages of the theoretic result are obtained by a simulation example.

This paper considers the problem of discrete-time adaptive position tracking control for a interior permanent magnet synchronous motor (IPMSM) based on fuzzy-approximation. Fuzzy logic systems are used to approximate the nonlinearities of the discrete-time IPMSM drive system which is derived by direct discretization using Euler method, and a discrete-time fuzzy position tracking controller is designed via backstepping approach. In contrast to existing results, the advantage of the scheme is that the number of the adjustable parameters is reduced to two only and the problem of coupling nonlinearity can be overcome. It is shown that the proposed discrete-time fuzzy controller can guarantee the tracking error converges to a small neighborhood of the origin and all the signals are bounded. Simulation results illustrate the effectiveness and the potentials of the theoretic results obtained.

This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.