This paper investigates the robust adaptive predefined-time trajectory tracking control issue for underwater tracked robot with unknown track slipping and unknown dead-zone input. Fuzzy logic system (FLS) is employed to approximate the unknown nonlinear dynamic. A pair of parameter adaptive laws is designed to counteract the effects of unknown track slipping. To ensure that the trajectory tracking error converges to a specified accuracy within a predefined time, a predefined-time performance function is designed. Then, a barrier Lyapunov function (BLF) is constructed by embedding the performance function to avoid the singularity problem. In addition, a disturbance observer is constructed to observe and compensate for hydrodynamic disturbances present in the system dynamics, while a dynamic event-triggered mechanism is proposed to alleviate the computational load and reduce the actuator update frequency. Under the adaptive backstepping recursive design framework, a predefined-time event-triggered robust adaptive trajectory tracking control algorithm with performance constraints is proposed, which ensures system stability while enforcing predefined performance on the tracking error. Finally, the effectiveness and superiority of the proposed control scheme are validated through comparative simulations and data analysis.

This article addresses the resilient cooperative optimal output regulation (COOR) control problem for nonlinear strict-feedback multiagent systems (MASs) under denial-of-service (DoS) attacks. By constructing the resilient adaptive distributed observers, the leader's dynamics and states can be estimated by each follower. In the control design, a control input constructed by feedforward and feedback control input is proposed based on the system data. Neural networks (NNs) are employed to learn solutions of the feedforward and optimal feedback control problems. Meanwhile, to handle the influence caused by unknown nonlinear dynamics, combining off-policy integral reinforcement learning (IRL) algorithm with actor-critic NNs (A-C NNs), an optimal feedback security control law is designed. To illustrate the feasibility and effectiveness of the proposed optimal control strategy, numerical and practical simulation examples are provided. Unlike prior studies limited to linear systems, this work explicitly accounts for complex nonlinear dynamics, significantly broadening the applicability of resilient COOR control problem in real-world applications.

This article investigates the dynamic event triggered fuzzy adaptive secure control issue for nonlinear vehicle platoon system under stochastic FDI attacks and switching topology. First of all, fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear dynamics. By the aid of the prescribed performance functions, the local spacing errors can be constrained within a preset region. By adopting the mathematical expectation method, the stochastic FDI attack signals can be solved. Secondly, under the framework of backstepping control, by constructing the dynamic event-triggering mechanisms, as well as the prescribed-performance technique, a robust intelligent adaptive performance constraint event-triggered secure platoon control algorithm is developed. Via Lyapunov stability theory and Barbalat’s Lemma, the developed control strategy can ensure all signals of the whole platoon system are bounded, and the following vehicles can asymptotically track the leading vehicle. In addition, the individual and string stabilities can also be guaranteed. Finally, the simulations are provided to further validate the feasibility and effectiveness of the proposed control strategy and theory.

The resilient cooperative control issue for uncertain multi-Euler-Lagrangian (EL) systems under a locally-finite number of adversaries that may arbitrarily manipulate communication links is investigated in this paper. We propose a resilient distributed backstepping control strategy and characterize the corresponding necessary and sufficient condition to guarantee the asymptotic consensus to the target state regardless of arbitrarily injected malicious communication information. To restrain the impacts of adversaries, the filtered coordinate-based weighted Mean-Subsequence-Reduced (CW-MSR) mechanism is adopted, which induces the unknown topology switching and may cause the discontinuity of the virtual controls when applying the backstepping method. To overcome the difficulty, locally uniform adjacency weights are selected in the CW-MSR mechanism, and consequently the continuity of the (non-smooth) virtual controls is ensured even in the presence of the topology changes. The non-smooth analysis method is given to prove the asymptotic consensus for the considered system.

This article considers the issue of fuzzy adaptive dynamic event triggered output feedback consensus control for nonlinear multiagent systems (MASs) suffered from stochastic false data injection attacks. First, fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions. By estimating the unknown attack signals, a novel fuzzy state observer is proposed. Based on the fuzzy state observer, a robust adaptive fuzzy secured dynamic event triggered control method is developed, where the dynamic surface control technique (DSC) is used to solve the computational complexity problem in backstepping design. The proposed control method ensures that each agent can observe system’s states well, the consensus tracking errors and output tracking errors converge to a small neighborhood of the origin, and all signals in the closed-loop system keep bounded. Finally, simulation results are provided to verify the effectiveness of the proposed control method.

In this article, a fuzzy adaptive prescribed-time performance constrain containment control issue is researched for nonlinear multi-agent systems (MASs). The original system’s prescribed time control issue will be converted into the corresponding controlled system’s asymptotic tracking control issue by using the time-domain mapping technique. Fuzzy logic systems (FLSs) are ultilized to identify the unknown dynamics. Combining prescribed-performance control and time-domain mapping techniques, a fuzzy adaptive prescribed-time performance constraint containment control algorithm is proposed, which can demonstrate that all signals in controlled system are bound within prescribed-time, and followers can converge to the convex hull created by the leaders by using Lyapunov stability theory. Furthermore, the virtual errors and local containment error asymptotically converge to a preset region. Finally, the simulations are provided to illustrate the effectiveness and feasibility of the developed control method. Note to Practitioners—This article investigates a fuzzy adaptive prescribed-time containment control issue for nonlinear multi-agent systems (MASs). The time-domain mapping technique can be used to convert the prescribed time control issue of the original system in finite horizon into the asymptotic tracking control issue of the corresponding controlled system in infinite horizon. In contrast to previous similar works, this article proposes a prescribed-time control algorithm, where the convergence time can be set offline and it does not rely on any design parameters. Moreover, this study can guarantee containment and virtual errors converge to the prescribed region by using the prescribed performance function and the barrier Lyapunov function.

This article studies the fuzzy adaptive resilient output control for nonlinear multi-agent systems (MASs) under Byzantine attacks. Firstly, fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics, then a fuzzy state observer is constructed to identify the unmeasured states in the controlled system. The mean-subsequence-reduced (MSR) technique is introduced to filter the unavailable neighbor information of each agent. On the basis of the distributed backstepping technique, a special Nussbaum function is utilized to construct an attack compensation scheme. Combining the compensation scheme and MSR technique, an observer-based robust fuzzy adaptive resilient output control design algorithm is proposed. The developed resilient control method enables the system outputs follow a given protocol under Byzantine attacks. Finally, the feasibility and effectiveness of the proposed control method and theory can be illustrated via the simulation results. Note to Practitioners—In this article, an observer-based resilient fuzzy adaptive output control issue is studied for nonlinear multi-agent systems subjected to Byzantine attacks. Fuzzy state observer is constructed to identify the unmeasured states. Since there exist the Byzantine attacks in the controlled system, the neighbor information of each agent cannot be fully used in the controller design. Thus, compared with the existing related results, the mean-subsequence-reduced technique can be introduced in this article to filter the unavailable neighbor information of each agent. Besides, an attack compensation control method via a special Nussbaum function is designed to resist the effect of uncertainties and malicious data caused by attacks. Simulation results ensure the feasibility of the developed resilient control approach and theory.

In this article, the fuzzy adaptive event-triggered consensus control issue is addressed for nonlinear multiagent systems (MASs) under output constraints and Denial of Service (DoS) attacks. First of all, fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions. Then, a novel switching observer is constructed to observe the leader's state and handle DoS attacks. With the help of the exponent-dependent barrier Lyapunov functions (BLFs), the system output can be constrained within a preset region. Based on dynamic surface control (DSC) technique, the issue of computational complexity can be effectively avoided. Combining the designed switching observer and relative thresholds, a robust fuzzy adaptive event-triggered controller is developed, which ensures that the consensus output tracking errors converge to a small neighborhood of zero, and all signals in the closed-loop system keep bounded. Moreover, Zeno behavior can be avoided. Ultimately, simulation results are given to validate the feasibility and effectiveness of the proposed control strategy and theory.

This article focuses on the issue of the cooperative adaptive fuzzy prescribed-time platoon control for nonlinear second-order vehicular platoon systems, which contain the nonlinear dynamic. Fuzzy logic system (FLS) is adopted to identify the unknown nonlinear dynamic in the platoon system. By the aid of the time-domain mapping technique, the prescribed-time control design for the controlled system will be converted to the asymptotic tracking control design of the corresponding controlled system. Combining adaptive backstepping control and vehicle-to-vehicle (V2V) communication topology, a prescribed-time fuzzy adaptive cooperative asymptotic tracking control scheme is proposed, which demonstrates all signals of the platoon systems are bounded within a prescribed time, and the velocities of all following vehicles can asymptotically track the leader vehicle with desired safety spacing. Moreover, the formation tracking errors can asymptotically tend to origin within a prescribed time. Finally, the effectiveness and feasibility of the proposed platoon control approach and theory will be verified by simulations.

This article investigates the data-based cooperative optimal output regulation problem (COORP) for nonlinear strict-feedback multiagent systems (MASs) under an event-triggered mechanism (ETM). By constructing an adaptive distributed observer, each follower can estimate the leader’s dynamic and state. In the control design, a feedforward-feedback control input is proposed based on system data. By utilizing the neural networks (NNs) to learn the solutions of the nonlinear regulator equation and the Hamilton–Jacobi–Bellman (HJB) equation, the feedforward control problem and the optimal feedback control problem can be addressed. Then, an off-policy integral reinforcement learning (IRL)-based optimal cooperative control method is proposed with actor-critic NNs (A-C NNs), and the influence caused by unknown nonlinear dynamics can be handled.FThrough the stability analysis, it is proved that all signals in closed-loop system are uniformly ultimately bounded (UUB), and the system can achieve Nash equilibrium. To demonstrate the effectiveness of the developed optimal control method, a simulation example is provided.

This article examines a neuroadaptive consensus control issue for heterogeneous port uncrewed container transporter (HPUCT) platoon with model uncertainties and actuator saturation. First, a novel robust finite-time estimator, which combines approximation with sliding mode technique, is introduced to approximate the complex nonlinear dynamics and observe external disturbances in HPUCT. The innovative design of the terminal sliding mode surface contributes to achieving stability in state measurement errors within a finite time. Subsequently, utilizing Lyapunov functions, a robust adaptive sliding mode controller is formulated for the consensus control of vehicles, which can ensure internal stability and string stability for the entire vehicle platoon. Additionally, to tackle the issues of saturation, the control strategy is modified by designing an auxiliary system. Finally, comparative simulations confirm the effectiveness of our designed estimator-based robust neuroadaptive consensus controller.

In this article, the problem of predefined-time event-triggered (PTET) robust adaptive fuzzy formation control is studied for nonholonomic multirobot systems (NMRSs) subject to multiple constraints and dead-zones. The adaptive estimation technique and fuzzy logic systems are used to identify nonlinear NMRSs with uncertain dynamics. By combining prescribed performance control method and introducing universal barrier functions, the transient and steady-state performances of the NMRSs are guaranteed, thereby further achieving collision avoidance and connectivity maintenance. On this basis, in order to achieve predefined-time convergence of formation errors and reduce the transmission frequency of control signals from controller to actuator for each robot with input dead-zones, a novel adaptive fuzzy robust PTET formation control strategy is further proposed by introducing predefined-time stability theory and designed trigger mechanism. It can avoid significant resource waste during rapid tracking processes, ensure the nonlinear NMRSs are predefined-time stable and all signals are bounded, which can be proved by constructing the barrier Lyapunov functions. In the end, the simulation studies are carried out to show the efficacy of the developed adaptive PTET formation control strategy.

This paper investigates the reinforcement learning-based adaptive fuzzy optimization control problem for third-order nonlinear vehicle platoon. The unknown nonlinear dynamic is approximated using fuzzy logic system (FLS). By the aid of prescribed performance technique, the designed quadratic spacing errors can be ensured to remain within a preset region. By constructing barrier type optimal cost function, and employing the actor–critic FLSs construction, a fuzzy adaptive optimization prescribed performance control algorithm is developed, which further verifies the individual stability of each vehicle, and all signals of vehicle platoon system are bounded. In addition, the strong string stability of vehicle platoon system can be ensured using the couple sliding mode surface. Finally, simulations are conducted to demonstrate the effectiveness and feasibility of the proposed results.

In this paper, the security-based adaptive fuzzy control issue is studied for strict feedback multiagent systems (MASs) with actuator failures and DoS attacks. Fuzzy logic systems (FLSs) are applied to approximate unknown nonlinear functions. In order to deal with the discontinuous signals caused by DoS attacks, a distributed filter is constructed. Based on the proposed filter and the backstepping control technique, a security-based robust adaptive fuzzy fault-tolerant controller is designed to compensate for the effect caused by intermittent actuator faults. The proposed control method can ensure the tracking error converges to a small neighborhood of the origin and all signals in the closed-loop system are bounded. Finally, simulation results are given to demonstrate the effectiveness of the proposed control method.

In this paper, an adaptive fuzzy prescribed time formation control problem is studied for nonlinear multi-agent systems (MASs) with input saturation under undirected graphs. Firstly, by introducing time-domain mapping, the original nonlinear MASs is transformed into an equivalent time-varying system. Then, the prescribed time formation control problem of the original system can be further transformed into the asymptotic tracking control problem in the infinite time domain. Secondly, fuzzy logic systems (FLSs) are used to approximate the unknown nonlinear dynamics, and the input saturation problem is solved by introducing an auxiliary subsystem. An adaptive fuzzy prescribed time formation control scheme is proposed by combining command filtering technique and backstepping recursive design techniques. By the aid of Lyapunov stability theorem, all signals of the controlled system are bounded, and formation tracking errors asymptotically converge to zero within a prescribed time. In addition, the influence raised by the input saturation can be effectively offset. Finally, the feasibility of the proposed prescribed time formation control scheme can be further verified by a numerical simulation.

This article investigates the finite-time fuzzy adaptive dynamic event-triggered (DET) formation tracking control problem for underactuated unmanned surface vehicles (USVs) subjected to multiple constraints and intermittent actuator faults. The models of USVs contain unknown nonlinear dynamics, so the fuzzy logic systems are employed to estimate them. Meanwhile, considering the frequent updating of actual control input, a DET strategy is designed, which can improve the communication resource utilization. Subsequently, based on the theory of finite-time stability and prescribed performance control technique, a new robust adaptive fault-tolerant control algorithm is presented, which can not only solve the problem of intermittent actuator faults for USVs, but also has the merits of avoiding collision and preserving connectivity. In addition, it also ensures all the signals of the USVs are bounded in finite time. The simulation results can verify the feasibility of the proposed control method.

In this paper, the problem of fuzzy adaptive fixed-time formation control is studied for nonholonomic multirobot systems (NMRSs) subject to unknown dead-zones and multiple constraints. Fuzzy logic systems (FLSs) are used to identify nonlinear NMRSs with uncertain dynamics, and then adaptive technique are utilized to compensate for unknown dead-zones parameters. By combining prescribed performance control (PPC) method, the transient and steady-state performances of the NMRSs are guaranteed, thereby further achieving collision avoidance and connectivity maintenance. On this basis, in order to achieve fixed-time convergence of formation tracking errors and eliminate the effect of unknown dead-zones, a novel adaptive robust fixed-time formation control (FxTFC) strategy is further proposed by introducing fixed-time stability theory and command filter technique. It can avoid the output shafts do not acting for a short time period when the input shafts turn rapidly, ensure the nonlinear NMRSs are fixed-time stable and all signals are bounded, which can be proved by constructing the Barrier Lyapunov Functions (BLFs). In the end, the simulation studies are carried out to show the efficacy of the developed robust FxTFC strategy. The result is that even if there are actuator dead-zones among each robot, formation tracking can also be achieved in fixed-time.

In this article, the problem of adaptive command filter formation asymptotic tracking control (FATC) is investigated for nonholonomic multirobot systems (NMRSs) with multiple constraints. Consider the robot’s uncertain dynamics parts that the unknown system parameters and nonlinear functions, which are approximated by using adaptive technique and fuzzy logic systems (FLSs). The transient and steady-state performance, collision avoidance, and connectivity maintenance can be guaranteed by employing the universal barrier function and prescribed performance control (PPC) approach. In addition, in order to compensate the influence of filtering errors and make followers more accurately and swiftly track leader’s trail, a novel adaptive FATC scheme is further developed by subtly combining the command filter technique and exponential decay function. This scheme can guarantee all signals of controlled systems are bounded and the formation tracking errors asymptotically converge to origin. Finally, the feasibility of FATC scheme is demonstrated for NMRSs.

This article concentrates on the disturbance observer-based fuzzy adaptive prescribed constraint control issue for nonlinear third-order vehicular platoon systems. Fuzzy logic system (FLS) is driven to identify unknown nonlinearity. By designing a novel time-varying performance function without using the initial conditions, the proposed quadratic spacing error policy can guarantee the spacing error remains within a residual set with the predefined convergence rate. By constructing a disturbance observer, the restrictive assumptions about the unknown boundaries of the external disturbance and identify error. Then, combining time-varying performance function and constructed disturbance observer, a new robust fuzzy adaptive performance constraint control approach is presented, which demonstrates the individual stability, and the boundedness of all signals of vehicular platoon systems are able to be ensured. Moreover, the string stability of the vehicular platoon systems can also be guaranteed via sliding mode technique. Eventually, simulation results are displayed to explain the validity of the developed prescribed performance platoon control approach.

This article focuses on the issue of reinforcement learning (RL)-based adaptive optimal finite-time performance constraint control for nonlinear systems. By the aid of RL-based critic-actor neural networks (NNs) construction, an optimal finite-time adaptive performance constraint controller is constructed. Via the adding a power integrator and prescribed performance techniques, a performance constraint-based adaptive finite-time optimal control strategy is developed, which demonstrates the considered system is semi-global practical finite-time stability (SGPFS), and all state errors can remain within a preset error constraint in finite time. Meanwhile, the proposed optimal control strategy can minimum the corresponding cost function. Finally, a numerical example is implemented to verify the feasibility of the developed control strategy and theory.

This article investigates the problem of adaptive neural network (NN) optimal consensus tracking control for nonlinear multiagent systems (MASs) with stochastic disturbances and actuator bias faults. In control design, NN is adopted to approximate the unknown nonlinear dynamic, and a state identifier is constructed. The fault estimator is designed to solve the problem raised by time-varying actuator bias fault. By utilizing adaptive dynamic programming (ADP) in identifier-critic-actor construction, an adaptive NN optimal consensus fault-tolerant control algorithm is presented. It is proven that all signals of the controlled system are uniformly ultimately bounded (UUB) in probability, and all states of the follower agents can remain consensus with the leader's state. Finally, simulation results are given to illustrate the effectiveness of the developed optimal consensus control scheme and theorem.

This article investigates the adaptive neural-network (NN) tracking optimal control problem for stochastic nonlinear systems, which contain state constraints and uncertain dynamics. First, to avoid the violation of state constraints in achieving optimal control, the novel barrier optimal performance index functions for subsystems are developed. Second, under the framework of the identifier-actor-critic, the virtual and actual optimal controllers are presented based on the backstepping technique, in which the unknown nonlinear dynamics are learned by the NN approximators. Moreover, the quartic barrier Lyapunov functions are constructed instead of square ones to cope with the Hessian term to ensure the stability of the systems with stochastic disturbance. The proposed optimal control strategy can guarantee the boundedness of closed-loop signals, and the output can follow the given reference signal. Meanwhile, the system states are restricted within some preselected compact sets all the while. Finally, both numerical and practical systems are carried out to further illustrate the validity of the proposed optimal control approach.

This article investigates the problem of adaptive fuzzy optimal distributed consensus control for stochastic multiagent systems (MASs) with full-state constraints and nonaffine nonlinear faults. Fuzzy logic systems are employed to identify the unknown nonlinearities. To solve the problem of optimal state constraint control, a barrier Lyapunov function based optimal cost function is designed. By introducing Butterworth low-pass filter into control design, the deleterious effects raised by nonlinear fault can be compensated. By utilizing adaptive dynamic programming algorithm in critic–actor construction, a fuzzy adaptive distributed optimal consensus fault-tolerant control method is proposed, which can ensure that all signals of the controlled system are semiglobally uniformly ultimately bounded in probability, and outputs of the follower agents keep consensus with the output of leader. In addition, system states are all not exceeded their constrained bound. Finally, simulation results are provided to illustrate the feasibility of the developed control method and theorem.

This article investigates the problem of fuzzy adaptive consensus tracking control for nonlinear multiagent systems with unknown nonlinear control gain functions. In the control design, fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics, and a distributed state observer is constructed to estimate the unmeasured states. Under the case of directed graph, by constructing the logarithm Lyapunov functions, an adaptive fuzzy distributed control method is presented, which removes the restrictive assumptions about the unknown control gain functions must be constants in traditional adaptive intelligent output feedback control methods. The developed control scheme cannot only ensure that all signals of the controlled system are semiglobal uniformly ultimately bounded, but also make the outputs of all the followers keep consensus with the output trajectory of the leader. Finally, simulation results are given to illustrate the effectiveness of the developed consensus control scheme and theorem.

This article studies the problem of adaptive fuzzy fixed-time decentralized control for nonlinear interconnected systems with stochastic disturbances and unknown nonlinearities. In control design, fuzzy logic systems are utilized to identify the unknown nonlinear functions. Combining stochastic fixed-time Lyapunov stability theory with adaptive backstepping control technique, a fuzzy adaptive fixed-time decentralized control design algorithm is developed by adopting smooth projection operator. It is proved that all the state variables of the closed-loop system are globally fixed-time stable in probability. Finally, both numerical and practical examples are provided to elucidate the feasibility of the proposed control algorithm.

This article studies the problem of the robust fuzzy adaptive finite-time control design for a class of single-input single-output high-order nonlinear systems. The considered plants contain unknown nonlinear functions, unmodeled dynamics, and dynamical disturbances. In this control design, fuzzy logic systems are utilized to approximate unknown nonlinear functions, and dynamical signal functions are introduced to solve unmodeled dynamics and dynamical disturbances. Under the framework of an adaptive backstepping control and adding a power integrator control design technique, a robust fuzzy adaptive finite-time control scheme is developed, which can not only guarantee the controlled system to be semiglobal practical finite-time stable, but also have the robustness to unmodeled dynamics and dynamical disturbances. Both numerical and practical simulation examples are provided to check the effectiveness of the proposed control method.

This article addresses the problem of finite-time neural network (NN) adaptive dynamic surface control (DSC) design for a class of single-input single-output (SISO) nonlinear systems. Such designs adopt NNs to approximate unknown continuous system functions. To avoid the "explosion of complexity" problem, a novel nonlinear filter is developed in control design. Under the framework of adaptive backstepping control, an NN adaptive finite-time DSC design algorithm is proposed by adopting a smooth projection operator and finite-time Lyapunov stable theory. The developed control algorithm means that the tracking error converges to a small neighborhood of origin within finite time, which further verifies that all the signals of the controlled system possess globally finite-time stability (GFTS). Finally, both numerical and practical simulation examples and comparing results are provided to elucidate the superiority and effectiveness of the proposed control algorithm.

This article investigates the adaptive neural network (NN) finite-time output tracking control problem for a class of multi-input and multi-output (MIMO) uncertain nonlinear systems whose powers are positive odd rational numbers. Such designs adopt NNs to approximate unknown continuous system functions, and a controller is constructed by combining backstepping design and adding a power integrator technique. By constructing new iterative Lyapunov functions and using finite-time stability theory, the closed-loop stability has been achieved, which further verifies that the entire system possesses semiglobal practical finite-time stability (SGPFS), and the tracking errors converge to a small neighborhood of the origin within finite time. Finally, a simulation example is given to elaborate the effectiveness and superiority of the developed.

This paper addresses the finite time adaptive fuzzy decentralized control problem for interconnected large scale nonlinear systems in nonstrict feedback forms with output-constraint. The fuzzy logic systems are used to approximate the unknown nonlinear functions and a state observer is constructed to estimate the immeasurable states. In order to deal with the output constraint problem, the barrier Lyapunov function is introduced. By combining backstepping recursion design with a command filter, a finite time fuzzy adaptive decentralized control method is presented. The stability analysis can be obtained based on the finite time Lyapunov stability theory, which demonstrates that the closed-loop system are semi-global practical finite-time stability, the system outputs can track the given reference signals and keep in the given constraint bounds in a finite time. Finally, two simulation examples are provided to elaborate the effectiveness of the presented control scheme.

This paper investigates the finite-time adaptive fuzzy control problem for a class of multi-input and multi-output (MIMO) nonlinear nonstrict feedback systems. During the control design process, fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions, and fuzzy state observer is constructed to estimate the unmeasured states. By combining adaptive backstepping with the dynamic surface control (DSC) technique, a finite-time fuzzy adaptive control scheme is presented to overcome the “explosion of complexity” problem. The stability of the close-loop systems can be proved based on the finite-time Lyapunov stability theory. The presented control scheme demonstrates that the closed-loop systems are semiglobal practical finite-time stability, and tracking errors converge to a small neighborhood of the origin in a finite time. Finally, two simulation examples are provided to show the effectiveness of the presented control method.