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This paper investigates finite-time dynamic event-triggered full-state constraints consensus control for nonlinear multiagent systems with switching topologies and unknown disturbances. First, all the system states will not exceed the predefined range, which has guiding significance for the constraint control of velocity and position in multiagent systems. Second, to balance the control errors and event-triggered control, a novel finite-time dynamic event-triggered scheme is developed for multiagent systems, which can achieve fast convergence rates and save communication resources. Third, to improve the system performances, a finite-time command filter is designed to remove transient errors resulting from filtering errors and explosion of complexity. All closed-loop signals are proved to be bounded, and the tracking errors quickly converge into a narrow neighborhood of zero in finite-time. To sum up, the proposed control scheme balances the effects of unknown disturbances, unknown nonlinear dynamics, and limited communications bandwidth, which can improve the control performance for the multiagent systems under time-varying switching topologies. Finally, simulation results validate the suggested approach using a mechanical platform model. [ABSTRACT FROM AUTHOR]

This paper discusses a fixed-time disturbance observer-based fixed-time prescribed performance control problem for nonlinear systems subject to disturbances and state constraints. Fixed-time disturbance observers are designed to estimate disturbances with unknown upper bounds and the estimation error converges to the origin in a fixed time. The finite-time prescribed performance function predetermines the time the tracking error reaches the steady-state boundary. The prescribed performance constraint for tracking error is converted into an asymmetric time-varying state constraint. The integral barrier Lyapunov function is employed to suppress the tracking error in the time-varying preset boundary while the states remain within constraints. The fixed-time differentiator is used to address the issue of the "explosion of complexity" in the backstepping control. Moreover, it is proved that the tracking error converges to the origin within a fixed time based on the Lyapunov stability theory. Simulation analysis demonstrates the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of event-based adaptive fault estimation and fault-tolerant control for a class of strict-feedback nonlinear systems with full state constraints. The unknown fault and the to-be-tested states of the system are estimated by an innovative adaptive fault observer based on a modified intermediate variable update law with fault switching function. In addition, an event-triggered mechanism and adaptive fault-tolerant controller co-design scheme is proposed according to backstepping algorithm and constrained technique, which not only eliminates the effect of fault presence but also leads to a significant reduction of communication resources. Through theoretical analysis, the constructed controller ensures that all signals within the system are ultimately bounded, and the state errors converge to the desired neighborhoods. Finally, the effectiveness of the method is illustrated by a simulation example of a single-link robot system. [ABSTRACT FROM AUTHOR]

The goal of this paper is to discuss the prescribed finite-time tracking control issue for uncertain full state constrained non-strict feedback nonlinear multi-agent systems (MASs). In the framework of backstepping design, a novel practical virtual control signal is constructed based on hyperbolic tangent function. The design of practical virtual control signal can not only satisfy the same constraints as the corresponding state variables, but also successfully avoid singularity problem. Furthermore, a prescribed finite-time control protocol is proposed on the basis of an improved universal barrier Lyapunov function (UBLF) approach and radial basis function neural network (RBF NN) technique. By the aid of the developed UBLF, the proposed control scheme enables the MAS to complete synchronization objective within an explicitly predetermined settling time and with an error accuracy given by the designer. Based on the stability theorem, it has been theoretically proven that the proposed control protocol can guarantee the MAS achieves synchronous tracking within a pre-given finite time while avoiding violation of the full state constraints. Finally, two numerical simulation examples are provided to further demonstrate the efficacy of the proposed control strategy. By comparing the control effectiveness with previous results, it is found that the convergence rate of the MAS is improved, and all the state variables can be restricted to the constraints throughout the entire control process. [ABSTRACT FROM AUTHOR]

Summary: This paper is dedicated to the problem of adaptive fuzzy fault‐tolerant control (FTC) for a class of stochastic nonstrict feedback nonlinear multi‐agent systems (MASs) with input quantization and full state constraints. Firstly, based on the backstepping technique and the bounded estimation method, the effect of actuator faults can be eliminated by the proposed adaptive updating laws. Secondly, the stochastic barrier Lyapunov functions (BLFs) are constructed to ensure that all state constraints are not violated. Meanwhile, the introduction of input quantization can effectively save network resources and reduce computational burden. It is further shown that all the closed‐loop signals are uniformly bounded, and the consensus tracking errors asymptotically tend to zero. The effectiveness of the proposed control scheme is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

A novel full state‐constrained integrated guidance and control (IGC) schema for aerial interceptors under input saturation with tunnel‐prescribed performance is proposed. To achieve an appointed‐time engagement with less overshoot phenomenon, the tunnel‐prescribed performance control (TPPC) technique is applied to generate virtual guidance laws, where the tracking of desired line of sight (LOS) angles possesses predefined‐time convergent property. For the sake of the flight safety of interceptors in a practical flight environment, the physical limitations on the flight angles and the angular rates of attitude systems are taken into consideration. The integral barrier Lyapunov function (iBLF) technique is adopted to ensure all the attitude states within a user‐defined domain. The nominal virtual commands are generated adaptively by the backstepping method combined with some novel dynamic surfaces. To further eliminate the possible input saturation, a novel one‐order auxiliary system is constructed to compensate for the discrepancy of the virtual input commands. All the signals in the closed‐loop system are strictly proven to be bounded by Lyapunov theory. The simulation results show the effectiveness of the proposed IGC schema by comparison with other IGC laws. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses the problem of adaptive tracking control for multi‐input and multi‐output (MIMO) non‐strict feedback systems with unmodeled dynamics and full state time‐varying constraints. To tackle the interference of unmodeled dynamics, the dynamic signal generated by the auxiliary system is used. Hyperbolic tangent function is used as a nonlinear mapping tool to transform the constrained system into an unconstrained one. Hysteresis quantizer is introduced to mitigate the chattering phenomenon and quantization error in the quantization signal. The derivative of virtual signal can be approximated more efficiently by command filter. Furthermore, an error compensation mechanism is established to mitigate the error introduced by the command filter. Unknown nonlinear functions are approximated by radial basis function neural networks (RBFNNs). Stability analysis of the proposed controller is performed through the Lyapunov stability theory and the output tracking error can be constrained within a specified range. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates trajectory tracking control of the Autonomous Underwater Vehicle (AUV) with the general uncertainty consisting of model uncertainties and unknown ocean current disturbances. A full prescribed performance control strategy based on disturbance observer is developed, which ensures that the tracking error, the velocity error, and the observation error are all constrained. First, under the case of unmeasurable AUV acceleration, a fixed-time observer is constructed to estimate the general uncertainty, which constrains the observation error within the prescribed accuracy by a prescribed performance observer. Then, based on the performance function and corresponding error transformation, a prescribed performance protocol is designed to realize the trajectory tracking control, so that the observation error, the tracking error, and the velocity error are all constrained within the prescribed accuracy range. Simulation results demonstrate the efficiency of the full prescribed performance control strategy while the AUV tracking control with full state constraints is feasible. Moreover, compared with the other two relevant works, this study improves the observation performance by at least 10 %, both in case of deepwater disturbances and near-surface disturbances. • The model uncertainties and disturbances are combined into the general uncertainty. • The general uncertainty is estimated by an improved fixed-time observer. • Tan-type function is used in error transformation of prescribed performance method. • A full prescribed performance control strategy achieves full state constraints. • The observation error, tracking error, and velocity error are all constrained. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose an adaptive Barrier-Lyapunov-Functions (BLFs) based control scheme for nonlinear pure-feedback systems with full state constraints. Due to the coexist of the non-affine structure and full state constraints, it is very difficult to construct a desired controller for the considered system. According to the mean value theorem, the authors transform the pure-feedback system into a system with strict-feedback structure, so that the well-known backstepping method can be applied. Then, in the backstepping design process, the BLFs are employed to avoid the violation of the state constraints, and neural networks (NNs) are directly used to online approximate the unknown packaged nonlinear terms. The presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to zero. Meanwhile, it is shown that the constraint requirement on the system will not be violated during the operation. Finally, two simulation examples are provided to show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

An adaptive event‐triggered control issue based on command filter and dynamic surface control is studied for switched nonlinear systems with full state time‐varying constraints in this article. To handle state constraints, the nonlinear mapping conversion technique is used. Radial basis function neural networks (RBFNNs) are utilized to approximate unknown nonlinear continuous functions. Adaptive control algorithm is designed based on command filtered backstepping technology. Novel even triggered control is constructed for two different cases in the switching interval. Furthermore, all signals in the switched system are proved to be semi‐globally uniformly ultimate bounded (SGUUB) by the aid of dynamic surface control method under arbitrary switching. Meanwhile, all states do not violate the preset constraints and Zeno phenomenon is avoided. The validity of the presented approach is confirmed through a numerical simulation result. [ABSTRACT FROM AUTHOR]

An adaptive neural network fault‐tolerant control method is proposed for the non‐affine multi‐agent systems with actuator failure and input saturation. Through the transformation of the original multi‐agent systems, an equivalent model is proposed. Considering that the signal input of multi‐agent systems is limited, this article will solve the input saturation problem. But in a real system, the actuator would probably fail. Therefore, a fault tolerant control algorithm is proposed to solve the fault problem in the systems. In this article, an adaptive distributed controller is designed to eliminate the uncertainty of the systems. At the same time, the integral barrier Lyapunov function is selected to verify the stability. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the control issue of adaptive fault-tolerant is studied for a class of stochastic nonlinear systems with multiple faults and full state constraints, with multiple faults including the actuator faults and the external system fault. The problem with full state constraints are solved by constructing a logarithmic barrier Lyapunov functions (BLFs). By integrating multi-dimensional Taylor network (MTN) technology into the backstepping process, a new adaptive MTN-based fault-tolerant controller is designed. On the basis of considering multiple faults, the proposed control strategy can ensure that all signals in the closed-loop system are semi-global ultimately uniformly bounded (SGUUB) in probability, and all states of the system are constrained within the given boundary. Finally, three simulation examples are given to illustrate the effectiveness and practicability of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This article studies the adaptive tracking control for a type of multi-input multi-output nonlinear systems, which still is an open issue coming from considerations of system uncertainties, unmeasurable states, and asymmetric time-varying full-state constraints. Fuzzy logic systems (FLSs) are trained online to estimate and compensate for unknown nonlinear system dynamics, effectively improving uncertainties rejection capabilities. To solve unmeasurable states, a state observer, including FLSs, is built and does provide a fairly good observation accuracy. Notably, an asymmetric time-varying barrier Lyapunov function is constructed to realize full-state constraint satisfactions. Based on the above theoretical findings, an adaptive fuzzy tracker is designed in a recursive manner. A Lyapunov analysis demonstrates the stability of the closed-loop system, and particularly, the constrained conditions will not be violated during the whole operation. Simulation results show the usefulness of the developed tracker. [ABSTRACT FROM AUTHOR]

In this manuscript, based on nonlinear multi-agent systems (MASs) with full state constraints and considering security control problem under false data injection (FDI) attacks, the fixed-time formation control (FTFC) protocol was designed, which can ensure that all agents follow the required protocol within a fixed time. Fuzzy logic system (FLS) was used to compensate and approximate the uncertain function, which improved safety and robustness of the formation process. Finally, the fixed-time theory and Lyapunov stability theory were addressed to prove the effectiveness of the proposed method, and simulation examples verified the effectiveness of the theory.

In this paper, the problem of adaptive fuzzy tracking control for a class of uncertain switched nonlinear systems with unknown control direction is studied. Aiming at the problem, an adaptive control scheme with Nussbaum gain technology is constructed by using the average dwell time (ADT) method and the backstepping method to overcome the unknown control direction, and time‐varying asymmetric barrier Lyapunov functions (ABLFs) are adopted to ensure the full‐state constraints satisfaction. The proposed control scheme guarantees that all closed‐loop signals remain bounded under a class of switching signals with ADT, while the output tracking error converges to a small neighborhood of the zero. An important innovation of this design method is that the unknown control direction, asymmetric time‐varying full state constraints, and predefined time‐varying output requirements are simultaneously considered in uncertain switched nonlinear systems for the first time. We set a moment in advance, and make the systems comply with the constraint conditions before running the moment by the shift function nested in the first time‐varying ABLF. Finally, a simulation example verifies the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Summary: This article focuses on the fast finite‐time adaptive tracking control for non‐strict feedback stochastic nonlinear systems. Random disturbances with constraints have rarely been considered in previous studies of fast finite‐time stabilization problems. The corresponding extension of the adaptive fast finite‐time stabilization mechanism to stochastic nonlinear systems with full‐state constraints is one of the innovations of this article. First, neural networks and barrier Lyapunov functions are utilized to handle the problems arising from the unknown nonlinear terms and the state constraints, respectively. Second, an adaptive fast finite‐time tracking control scheme for stochastic systems is designed based on the backstepping technique and a fast finite‐time control strategy, which allows all signals of the closed‐loop system to be bounded. Meanwhile, the designed controller guarantees a fast convergence of the tracking error to a small neighborhood of the origin under full‐state constraints. Finally, two examples are given to illustrate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

To improve the performance of a class of nonlinear system with actuator fault and external disturbance, a fault-tolerant control method with finite-time prescribed performance is proposed based on the super-twisting disturbance observer in this paper. First, the actuator bias fault and external disturbance of the system are regarded as a lumped disturbance, and it is estimated by the designed super-twisting disturbance observer in finite time. Second, a finite-time prescribed performance fault-tolerant controller under full state constraints is designed by combining the backstepping method, the modified barrier Lyapunov function (MBLF) method and the improved prescribed performance function (IPPF) method. Moreover, the "explosion of complexity" problem in the controller design is solved by the command filters with error compensation. Theoretical analysis shows that the closed-loop system can converge to the neighborhood near the equilibrium point in finite time, and all states are always within the constraint boundaries. Finally, the effectiveness and feasibility of the proposed method are verified by the comparisons of simulations and experiments based on a permanent magnet synchronous motor (PMSM) position system. [ABSTRACT FROM AUTHOR]

For stochastic robotic systems actuated by direct current (DC) motors with full-state constraints, adaptive trajectory tracking is investigated in this paper. The main challenge is how to deal with the highly coupled terms with the stochastic disturbance and unknown nonlinear functions. First, as an approximate instrument of unknown nonlinear functions, the fuzzy logic system is adopted to tackle the uncertainty of stochastic robotic systems. Second, the command filter technique is used to solve the "complexity explosion" problem in the traditional backstepping process, and error compensation mechanisms are introduced to eliminate the error influence caused by command filters. Then, based on barrier lyapunov functions (BLFs), the adaptive fuzzy tracking controller is designed such that the state constraints are not breached almost surely, all signals in the closed-loop system are bounded almost surely and the first-order moment of the tracking error converges to an arbitrarily small neighborhood of zero. Finally, the tracking problem of the two-link planar manipulator in stochastic surroundings is analysed to demonstrate the effectiveness and advantages of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Summary: This paper focuses on an adaptive decentralized prescribed performance control problem for a class of large‐scale stochastic nonlinear systems with asymmetric input saturation and full state constraints. Firstly, the obstacle of input saturation is overcome by introducing the Gaussian error functions. Secondly, the transient performance of the system output is realized by introducing the asymmetric error transfer functions. Thirdly, the full state constraints are considered in the backstepping control process, and the boundary of state constraints is ensured by constructing barrier Lyapunov functions. Then, the multidimensional Taylor network is employed to approximate the unknown nonlinearity, and an adaptive decentralized controller is designed. Finally, it is shown that the proposed control strategy can ensure that the closed‐loop system is semi‐global ultimately uniformly bounded in probability, and the tracking error of the system can be kept within an adjustable neighborhood of the origin. Two simulation examples are provided to illustrate the feasibility of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper considers the problem of observer-based adaptive finite-time control for a class of uncertain nonlinear stochastic systems with input saturation, time-varying delay and full state constraints. An adaptive design approach is adopted to deal with saturated input non-linearities which are not required to know any information. Based on the growth restrictions are removed, Lyapunov-Krasovskii functional is imposed to eliminate the influence of time-varying delay. Owing to the unmeasurable and constrained states, the state observer and barrier Lyapunov function are adopted via the controller construction process. Under a criterion of semi-globally finite-time stability in probability (SGFSP), the constructed adaptive finite-time controller can guarantee that all the states in the system are not violated. Finally, the effectiveness of the control scheme is verified by the ultimate simulation results of a chemical reactor system. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of event-triggered control for a class of nonlinear systems with unknown control coefficients, time-varying delays, full state constraints and external disturbances, simultaneously. Firstly, based on integral Barrier-Lyapunov Functionals, an adaptive event-triggered controller is designed to account for the impacts of complete state constraints, unknown time-varying delays and external disturbances. Using fuzzy logic systems, the unknown functions of systems are then roughly estimated. The system redundancy can be significantly reduced by implementing the event-triggered control technique in the meanwhile. By introducing the separation technique and Lyapunov–Krasovskii functionals, it is shown that the strategy can ensure tracking performance greatly and all the closed-loop signals of the system are bounded. Compared with the existing results, the proposed design scheme is less conservative and has a wilder application range. Finally, the simulation results show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

An event-triggered-based adaptive fuzzy control strategy is proposed for full state constraints nonlinear systems against time-varying disturbance, non-strict feedback structure, and input delay in this paper. Fuzzy logic systems (FLSs) are implemented to estimate the unknown nonlinear functions in the systems. The influence of input delay can be compensated by the method of Pade approximation, and the barrier Lyapunov function (BLF) is exploited to handle the problem of full state constraints. The input-to-state stability (ISS) assumption regarding measurement errors can be removed through the co-design of the event-triggered mechanism and the controller. Based on Lyapunov stability theory, all the signals in the closed-loop system are proved semi-globally uniformly ultimately bounded (SGUUB), and only a tiny tracking error between the system output and the reference signals, moreover, the Zeno behavior is avoided. The effectiveness of the adaptive control scheme can be verified by two simulation examples.

This article is concerned with the adaptive learning finite‐time output feedback control design for a class of high‐order nonlinear systems involving full state constraints. First, a state observer is designed to estimate the unmeasurable states and the radial basis function neural networks are used to identify unknown functions in the system. Then based on the state observer, we develop an adaptive learning finite‐time output feedback controller by combining the command filtered technique and error‐tracking method. In addition, the "explosion of complexity" problem can be avoided and the filtered errors caused by dynamic surface control can be compensated by the newly proposed command filter control. By constructing a novel log‐type barrier Lyapunov function, it can be guaranteed that the full state constraints are not violated and all the variables in the controlled systems are bounded in finite time. Finally, two simulation examples are carried out to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper is devoted to solving the global fixed-time event-triggered stabilization problem for a class of nonstrict-feedback stochastic systems with full state constraints. Firstly, the nonlinear state constrained system is converted into a novel constrained free system with the help of nonlinear mappings, which eliminates the feasibility conditions of virtual control signals while satisfying the state constraints. Secondly, a lemma about the variable separation technique is put forward to deal with unknown nonlinear terms in stochastic systems, which avoids the restrictive growth assumption that unknown functions need to be satisfied. Then, a fixed-time event-triggered control strategy based on the symbol function technique is designed by combining the backstepping method with the fixed-time stability theory of stochastic systems. It is proved that all closed-loop signals are globally fixed-time stable in probability. Finally, a numerical example and a single-link robot system are simulated to verify the availability and feasibility of the developed approach. [ABSTRACT FROM AUTHOR]

This paper studies the problem of adaptive fuzzy control for switched multi-input and multi-output (MIMO) nonlinear systems with full state constraints and unknown dead zones. First, fuzzy logic systems (FLSs) serve as approximate instruments of unknown nonlinear functions, which are used to tackle the issue of parameter uncertainties. The unmeasured states are estimated by designing a switched MIMO state observer. Then, an adaptive fuzzy control strategy based on the command filter technique and average dwell time (ADT) method is proposed, which tackles the issue of "explosion of complexity" in the conventional backstepping recursive procedure, and also overcomes the drawback of dynamic surface control by designing compensating signals. Furthermore, the dead zone nonlinearities are handled using the information for dead zone slopes. In particular, barrier Lyapunov functions are employed to testify the stability of the system under our proposed control signals while preventing the constraint violations. Finally, two simulation examples demonstrate the availability and effectiveness of the presented adaptive controller. [ABSTRACT FROM AUTHOR]

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An adaptive neural network (NN) distributed tracking control strategy is proposed for nonlinear strict‐feedback multi‐agent systems, which is affected by time‐varying full state constraints. The influence of asymmetric state constraints is also considered. The constraint bounds are related to both state vectors and time. Different from the usual constant boundary or function boundary, the constraint boundary adopted in this article not only considers the influence of state variables, but also takes time into consideration, which makes the constraint boundary more flexible and more difficult to solve. In addition, in order to solve the disturbance of the unknown function in the system, this article adopts the NN technology to realize the approximation effect of the unknown function, and designs a adaptive distributed controller based on the asymmetric obstacle Lyapunov function and backward step method, the closed‐loop signals are proved to be CSUUB. Finally, the effectiveness of the proposed method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

This paper is concerned with the fixed-time consensus control problem for non-strict feedback multi-agent systems with asymmetric output constraints and full state constraints. Considering the feasibility of controlling execution, a novel practical virtual control signal is developed utilizing both saturation function and hyperbolic tangent function to ensure that this signal can remain within the same restricted range as the corresponding state variable throughout entire operation process. In backstepping steps, the design of ideal virtual control signal also adopts a different form of piecewise function than before, introducing high-order polynomial functions to avoid singularity problems in the derivation process. In addition, function approximation ability of radial basis function neural networks technique is applied to estimate uncertainties derived from the system functions and controller design procedure. Moreover, universal barrier Lyapunov function approach is improved for constructing an adaptive constrained synchronization control scheme. By fixed-time stability theory, it is shown that the tracking errors of the MAS converge to an adjustable region around the origin in a fixed time and the state variables always obey their constraints. And the upper bound of the settling time is merely dependent on design parameters, which is not affected by the initial states of MAS. The effectiveness of the proposed control strategy is shown by a numerical simulation example at last. Two scenarios are provided to demonstrate the advantages of the control protocol proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper is dedicated to the problem of adaptive fault-tolerant tracking control for uncertain nonlinear systems with event-triggered inputs and full state constraints. Based on tuning function method, the effect of actuator faults and parameter uncertainties can be eliminated by designing adaptive updating laws. The barrier Lyapunov functions (BLFs) with lower bounds of unknown coefficients are constructed to ensure that all states of the systems are not violating the constraints. In addition, to reduce the communication burden from the controller to the actuator, an adaptive event-triggered controller is designed based on adaptive backstepping technique. It shows that all signals of the closed-loop system are bounded, and the tracking error of the system asymptotic converges to zero. Finally, two simulation examples are given to testify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, a command filter approximation-based finite-time fuzzy control scheme is proposed for the position tracking control of induction motor (IM) with full state constraints. Firstly, command filters and fuzzy logic systems are used to reconstruct the approximate value of unknown nonlinearities in IM drive systems, and convex optimization technology is applied to construct the update law of the weights of the fuzzy logic systems. Secondly, the barrier Lyapunov functions are introduced to ensure that the state of the motor is always in the given constraint space. Then, the finite-time control technology is utilized to accelerate the response speed of the system and realize the effective and fast tracking of the desired signal. Finally, the validity of the scheme proposed is verified by simulation. [ABSTRACT FROM AUTHOR]

In this study, a robust adaptive flight controller is developed to achieve anticipant trajectory tracking performance for a medium-scale unmanned autonomous helicopter (UAH) with external disturbances, actuator faults and full state constraints. The flapping dynamics of the main rotors is taken into consideration in the modeling process. The adaptive method is employed to handle the unknown bounded disturbances. A novel fault tolerant control (FTC) strategy is proposed to cope with actuator faults, which can effectively overcome the singularity problem in many adaptive estimation approaches by using projection function. Meanwhile, a suitable barrier Lyapunov function (BLF) is constructed to refrain from the violation of the state constraints. Combining with the backstepping technique, the four control inputs of the medium-scale UAH system are designed in sequence. Via the Lyapunov analysis, it is proved that all error signals in the closed-loop system converge to a small neighborhood around zero. Simulation studies are carried out to reveal the validity of the presented approach. • The flapping dynamics are introduced to embody the movement of the medium-scale UAH. • A novel FTC approach is proposed to deal with the actuator faults. • The singularity problem of direct adaptive estimation can be solved conveniently. • The developed FTC scheme can guarantee that all state constraints are not violated. [ABSTRACT FROM AUTHOR]

This paper investigates an adaptive 2-bits-triggered neural control for a class of uncertain nonlinear multi-agent systems (MASs) with full state constraints. Considering the limitations of practical physical devices and operating conditions, MASs may suffer performance degradation or even crash while the system states are not restricted. With this in mind, combined with barrier Lyapunov function (BLF), an adaptive neural consensus control is developed to guarantee that the state constraints of all followers are not violated. Further, the conversion relationship between the state constraints of MASs and the synchronization error constraints is clarified more precisely, which could improve the synchronization performance of MASs. In addition, considering both trigger threshold setting and control signal transmission bits issues, a 2-bit trigger strategy is proposed to maximize the utilization of MASs bandwidth resources. Theoretical analysis shows that all signals are uniformly ultimately bounded. And the simulation results demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Summary: In this article, an adaptive multi‐dimensional Taylor network (MTN) control approach is developed for nonlinear systems subject to input saturation and full state time‐varying constraints. Using the function approximation property of MTN, an adaptive tracking control method is developed by incorporating the concept of time‐varying state constraints into the construct of barrier Lyapunov functions. The proposed approach not only ensures all closed‐loop system signals are bounded but also that all states of the system never violate the given time‐varying constraints. The most important advantage of the proposed control strategy is that it can obtain satisfactory control effects with low computational cost. Finally, two examples are given to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Aiming at the research content of tracking control for a class of nonlinear uncertain switched systems including full state constraints, a novel event-triggered adaptive fuzzy output feedback control scheme is given. The systems studied need to use the approximation principle of fuzzy logic systems (FLSs) to solve the nonlinear smooth function with unknown terms. For ensuring that all states of the systems are within the time-varying constraint limits, the stability of the switched systems is verified by utilizing tangent barrier Lyapunov function (Tan-BLF). Based on the potential barrier Lyapunov function (BLF) and backstepping recursive construction method, the adaptive law, controller and event-triggered mechanism of the subsystem are designed. The proposed method will make that the signal is bounded. Moreover, the tracking error can be adjusted to the neighborhood closed to the origin. Simulation examples indicate the feasibility of the method. [ABSTRACT FROM AUTHOR]

Summary: This article focuses on the adaptive asymptotic learning tracking control problem of nonlinear systems with full state constraints. First, an adaptive neural network tracking algorithm is proposed which combines a time‐varying feedback element and robust compensation feedback element in the form of smooth function to guarantee that output signal tracks the reference signal asymptotically. Apart from this, a time‐varying integral barrier Lyapunov function is utilized to ensure that the system states are always kept in the constraint region. Furthermore, the event‐triggered mechanism is designed to efficiently reduce unnecessary transmissions. Compared with the other literatures, the original state constraint problem is directly solved by the proposed adaptive control algorithm. Finally, simulation is included to validate the built theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the problem of full state constraints-based adaptive fuzzy finite-time command filtered control (FTCFC) is investigated for permanent magnet synchronous motor (PMSM) stochastic systems. Firstly, the barrier Lyapunov functions are introduced to avoid the violation of the state constraints. Then, the unknown nonlinear functions are approximated by the fuzzy logic systems (FLSs) in PMSM stochastic systems. Furthermore, the finite-time control method is proposed by combing command filtered backstepping and adaptive fuzzy control, and the improved finite-time command filter can solve the problem of "explosion of complexity", and relax the condition of input signal. It is shown that the required tracking performance can be realized in finite time, and the boundedness of all signals in the closed-loop system is guaranteed. Finally, simulation results illustrate the validity of the studied method. [ABSTRACT FROM AUTHOR]

In the paper, a new control algorithm to realize the tracking control for a class of stochastic nonlinear systems with both full state constraints and input saturation is put forward using multi‐dimensional Taylor network (MTN) approach. By introducing a continuous function, the input saturation is converted into a linear model with bounded error. With the help of approximation properties of MTN, by proposing barrier Lyapunov functions (BLFs) and involving the idea of state‐constrained control into backstepping technique, a simple and efficient control scheme is developed. The proposed control scheme can guarantee the system states fall in the given constrained bounds as well as keeping the resulting control system stable. The effectiveness of the proposed control method is demonstrated through two examples. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered adaptive decentralised control strategy for a class of switched interconnected nonlinear systems is presented, which considers full-state constraints and unmodeled dynamics, simultaneously. In the controller design process, the approximation capability of radical basis function neural networks (RBF NNs) is used to estimate the unknown functions of the system. The interference caused by unmodeled dynamics is overcome by introducing a dynamic signal. In addition, the barrier Lyapunov function (BLF) is constructed for each subsystem to dispose the influence of state constraints. An adaptive control scheme with event-triggered mechanism is proposed to reduce communication burden. It is shown that the proposed event-triggered controller and an adaptive neural decentralised control strategy are designed such that all the signals in the closed-loop system are guaranteed to be bounded, the tracking errors of the system converge to a small neighbourhood of the origin and the full state constraints are not violated. Finally, a simulation result shows the effectiveness of the developed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive finite-time tracking control problem for a class of nonlinear full state constrained systems with time-varying delays and input saturation. Compared with the previously published work, the considered system involves unknown time-varying delays, asymmetric input saturation, and time-varying asymmetric full state constraints. To ensure the state constraint satisfaction, the appropriate time-varying asymmetric Barrier Lyapunov Functions and the backstepping technique are utilized. Meanwhile, the finite covering lemma and the radial basis function neural networks are employed to solve the unknown time-varying delays. The assumption that the time derivative of time-varying delay functions is required to be less than one in traditional Lyapunov–Krasovskii functionals is removed by the proposed method. Moreover, the asymmetric input saturation is handled by an auxiliary design system. Taking the norm of the neural network weight vector as an adaptive parameter, a novel adaptive finite-time tracking controller with minimal learning parameters is constructed. It is proved that the proposed controller can guarantee that all signals in the closed-loop system are bounded, all states are constrained within the predefined sets, and the tracking error converges to a small neighborhood of the origin in a finite time. Finally, a comparison study simulation is given to demonstrate the effectiveness of our proposed strategy. The simulation results show that our proposed strategy has good advantages of high tracking precision and disturbance rejection. [ABSTRACT FROM AUTHOR]

In this paper, a robust adaptive neural control scheme is proposed for a class of multi-input multi-output nonlinear systems in pure-feedback form with unmodeled dynamics and full state constraints. Radial basis function neural networks are employed to approximate and compensate for the unknown nonlinear continuous functions. By introducing nonlinear symmetric mapping, the full state-constrained tracking control problem of the multi-input multi-output pure-feedback system is transformed into a novel equivalent unconstrained one. For the transformed systems, a dynamic surface control method is applied to remove the difficulties for the multiple explosion of complexity problem. The use of Nussbaum-type function removes the need for any assumption on the function of control gain. By combining variable separation technique and the function's monotonously increasing property, the restrictive assumption of the dynamic disturbances caused by unmodeled dynamics is relaxed. One advantage is that only one adjustable parameter is used in the controller design. It is proved that all the closed-loop signals remain semi-globally uniformly ultimately bounded with good tracking performance, while the system states never violate the constraints. [ABSTRACT FROM AUTHOR]

In this article, the problem of adaptive finite-time control is studied for a category of nonstrict-feedback nonlinear time-delay systems with input saturation and full state constraints. The fuzzy logic systems are applied to model the unknown nonlinear terms in the systems. Then, a novel tan-type barrier Lyapunov function is adopted to overcome the problem of full state constraints. By utilizing the finite-time control theory and the backstepping technique, a finite-time fuzzy adaptive controller is designed. The controller can guarantee that the tracking error is adjusted around zero with a small neighborhood in a finite time and all the signals in the closed-loop system are bounded. Finally, two simulation examples are included to verify the validity and feasibility of the control scheme. [ABSTRACT FROM AUTHOR]

In this article, the finite-time adaptive fuzzy state-feedback tracking control problem for the pure-feedback system with full state constraints is studied. In order to transform the pure-feedback form into system strict-feedback case, the mean value theorem is introduced. By employing finite-time-stablelike function and state transformation for output tracking error, the output tracking error converges to a predefined set in a fixed finite interval. To tackle the problem of state constraints, integral barrier Lyapunov functions are utilized to guarantee that the state variables remain within the prescribed constraints with feasibility check. Fuzzy logic systems are utilized in this article to approximate nonlinear uncertainties. In addition, all the signals in the system are guaranteed to be semiglobal ultimately uniformly bounded. Finally, two simulation examples are given to show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive full state constrained consensus control strategy for a class of non-affine multi-agent systems with partially unknown control directions. Such non-affine systems commonly appear in practical applications and are difficult to control, especially with unknown control directions. Neural network is an excellent approximation tool to solve unknown parameters in the aforementioned consensus systems. The method of one-to-one mapping and mean value theorem can eliminate coupling terms and guarantee that the states of each agent are not violated the predetermined time-varying dynamic constraint boundary. In this way, the original systems can be transformed into an equivalent unconstrained systems, and the transformed systems obtained has the same consensus with the original systems. Considering that directions of control are partially unknown, the Nussbaum function can solve this problem in a novel way. Then the stability of the systems can be proved by Lyapunov function and all signals in the closed-loop systems are bounded. In the end, the simulation demonstrates the effectiveness of the proposed method.

This paper investigates the adaptive finite-time tracking control problem for a class of nonlinear time-varying delay systems subject to full state constraints and input saturation. The nonlinear state-dependent functions (NSDFs) are introduced to handle the asymmetric time-varying full state constraints without feasibility conditions. To cope with the unknown time-varying delays, the radial basis function neural networks (RBF NNs) and the finite covering lemma are utilized. Avoiding the use of the Lyapunov-Krasovskii functionals (LKFs), no restriction on the derivative of the time-varying delays is needed. Meanwhile, the effect of the input saturation is eliminated by the augmented function with an auxiliary control signal. The adaptive finite-time tracking controller with only one adaptive parameter is constructed by applying the command filter approach and backstepping technique. It is proved that the proposed controller can ensure that all signals in the closed-loop system are bounded and the tracking error converges to a small neighborhood of the origin in a finite time. Finally, the effectiveness of the proposed scheme is demonstrated by two simulation examples.