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This article concentrates on the problem of adaptive event-triggered control with double-side event-triggering mechanisms for a family of switched nonlinear systems with measurement sensitivity. In order to circumvent the negative effect of intermittent transmission and sensor measurement sensitivity on the output, an extended system is constructed by introducing a first-order output filter. Then, an input-driven neural switched observer is designed to estimate unmeasurable system states. To deal with the mutual interaction between the triggering and the switching mechanisms and their negative effect on the positive lower bound of inter-execution intervals, an incremental compensation is incorporated in the event-triggering mechanism (ETM) at the output sensor side. At last, a novel event-triggered control scheme built on both sides of the controller and output is proposed, which exhibits superior efficacy compared to the one-side event-triggered control scheme. By using the methods of the average dwell time (ADT) and the backstepping design, it is guaranteed that all signals of the switched closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the Zeno phenomenon is excluded. The efficacy of the proposed methodology is validated by using a numerical simulation and a one-link robot system. [ABSTRACT FROM AUTHOR]

This work developed an adaptive fuzzy tracking control strategy for pure-feedback nonlinear multi-agent systems via backstepping. The relevant unknown functions are addressed by fuzzy logic systems (FLSs), which take into account entirely unknown nonlinearities. The non-affine pure-feedback form is dealt with a Butterworth low-pass filter (BLF), and the converted systems can be used to construct a controller. The implementation of the first-order sliding-mode differentiator overcomes the difficulty of calculation explosion. The backstepping method is applied to design the virtual controller step by step, and the actual controller is designed successfully. Furthermore, through Lyapunov stability theory, the stability of systems is ensured, and that the tracking error is kept to a minimum. Finally, the simulation result shows the validity of the designed control strategy. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive optimal control strategy of finite-time control for high-order uncertain strict-feedback nonlinear systems. Firstly, a reinforcement learning (RL) based an optimal control scheme is employed to design a optimal controller, to achieve global optimisation. Additionally, considering the unmeasurable states, we construct a fuzzy observer and utilise fuzzy logic systems to approximate the unknown functions. Meanwhile, the inclusion of command filtering and time-based control simplifies the controller design and enhances the system's response rapidity. Finally, the effectiveness and feasibility of the proposed approach are validated through a numerical simulation and a single link-robot system simulation. [ABSTRACT FROM AUTHOR]

The issue of prescribed time adaptive fuzzy output-feedback event-triggered control strategy subject to bounded disturbances is considered in this paper. For the unmeasured states, an adaptive fuzzy observer is designed to estimate them. Then, a novel event-triggering condition involving a decreasing function containing the error between the system states is designed by synthesising the backstepping design framework and command filtered technique. The developed prescribed finite time control approach ensures that all signals of the closed-loop system are bounded and the tracking error remains within the predefined bound in the prescribed time. Meanwhile, Zeno phenomenon can be avoided. Finally, a mass-spring-damper system is taken as an example for simulation. [ABSTRACT FROM AUTHOR]

This paper discusses the fuzzy adaptive consensus secure control problem for a class of stochastic nonlinear multi-agent systems (MASs) with unknown false data injection attacks. Fuzzy logic systems (FLSs) are used to model unknown nonlinear functions. A distributed filter is proposed to obtain the information of the leader since leaders information is only available to partial agents. To deal with the unknown virtual time-varying gains caused by false data injection attacks, an adaptive compensation method is developed via re-codesigning the virtual controllers. Based on backstepping control methodology and bounded estimation algorithms, an adaptive fuzzy consensus secure controller is proposed. It is proved that all the signals in the closed-loop systems are bounded in probability, and all the output of followers can track the leader under false data injection attacks. Finally, simulation results and comparative results show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

In this paper, a predictive-based adaptive anti-saturation tracking control scheme is designed for uncertain multiple-input and multiple-output (MIMO) nonlinear systems subject to input constraints and external disturbance. Taking the safety of the MIMO nonlinear systems into account, a safety saturation threshold, which is less than the physical device saturation threshold, is given. In addition, since all signals of the auxiliary systems are not immediately employed to the systems when the input saturation happens, a new predictive-based auxiliary system is designed. Furthermore, to estimate the compound disturbance, with the help of the fuzzy control technology, the nonlinear disturbance observer (NDO) is used. Then, the stability of the closed-loop systems is proved under the designed controller. Finally, a simulation example is given to illustrate the feasibility of the designed anti-saturation controller. [ABSTRACT FROM AUTHOR]

Distributed switched large-scale systems are composed by dynamically coupled subsystems, in which interactions among subsystems vary over time according a switching signal. This paper presents a distributed robust switched model-based predictive control (DSwMPC) to control such systems. The proposed method guarantees stabilising the origin of the whole closed-loop system and ensures the constraints satisfaction in the presence of an unknown switching signal. In the distributed model-based predictive control (DMPC) used in this work, by considering the interactions among subsystems as an additive disturbance, the effect of the switch is reflected on the dynamic equation, local, and consistency constraint sets of the nominal subsystems. To compensate the effect of switching signal which creates a time-varying network topology, a robust tube-based switched model-based predictive control (RSwMPC) with switch–robust control invariant set as the target set robust to unknown mode switching is used as local controller. The scheme performance is assessed using three typical examples. The simulation results show that the input and state constraints are satisfied by the proposed DSwMPC at all times. They also validate that the closed-loop system converges to the origin. Also, a comparison of the DSwMPC with a centralised SwMPC (CSwMPC) and a decentralised SwMPC (DeSwMPC) are performed. [ABSTRACT FROM AUTHOR]

Fuzzy control has become one of the most effective tools for dealing with complex engineering processes. Over the years, research on fuzzy control systems has continuously evolved, witnessing numerous theoretical contributions and successful real-world achievements. The concept of model-free or data-driven fuzzy control was initially introduced with specific heuristics incorporated into the design. Due to the lack of a systematic framework for stability analysis in model-free fuzzy control, the significance of model-based fuzzy control has grown extensively. This approach ensures systematic design based on precise fuzzy models of the process. This survey focuses on the fundamental aspects of three prominent classes of fuzzy control. First, the paper commences with a review of Takagi–Sugeno fuzzy control systems. This includes discussions on stability analysis and controller design, exploring techniques to derive less conservative and/or complex results from a numerical burden perspective. Second, various aspects of data-driven fuzzy control are analysed in detail including a classification of the most popular data-driven control techniques and their combination with fuzzy control; a representative Iterative Feedback Tuning-based fuzzy controller is described. Third, this survey explores the fundamental aspects of evolving fuzzy control, with a particular emphasis on the significance of stability and control laws, which are not usually the primary focus of evolving intelligent systems research. For each discussed class of fuzzy control, the paper provides a selective list of mechatronics applications to illustrate their performance effectiveness, emphasising research papers published after 2011. Finally, drawing from recent advances in fuzzy control theory and mechatronics applications, future research directions and associated challenges are discussed. [ABSTRACT FROM AUTHOR]

The robust stabilisation for a general class of disturbed nonlinear systems subject to state-dependent uncertainties is addressed in this paper. Different from most existing results, the lumped uncertainty, including external disturbances and model uncertainties, is relaxed to be bounded by some nonlinear functions rather than constants in this paper. Based upon the classical design procedure of disturbance observer-based control (DOBC), a new continuous saturation-based dynamic controller is proposed to estimate the lumped uncertainty. By the delicate design of several compact sets, the qualitative robust stability of the closed-loop system is guaranteed in the sense that all the signals of the closed-loop system can converge to an arbitrarily small compact set in a finite time. Furthermore, when the external disturbances are constants, the semi-global asymptotic stability can also be guaranteed by the proposed controller. Finally, the relationship between system uncertainties and observer gain is shown by a numerical simulation example. [ABSTRACT FROM AUTHOR]

This paper delves into the fuzzy adaptive event-triggered tracking control for a certain class of strictly uncertain nonlinear systems subject to partial state constraints as well as output dead-zones. First of all, fuzzy logic systems (FLSs) are utilised to approximate the unpredictable items, a fuzzy adaptive observer is intended to enable state estimation. Subsequently, a type of smooth approximate model is introduced to settle the nondifferentiable phenomenon of output dead-zones. To reduce the computational overhead of the communication operation effectively, an improved event-triggered control strategy including a decreasing hyperbolic tangent function related to dead-zone, is applied to determine the actual control input. In addition, the barrier Lyapunov function (BLF) is fabricated to address the issue of partial state constraint. By theoretical analysis, the reference signal can be precisely tracked by the system output and all closed-loop signals remain uniformly ultimately bounded (UUB). Finally, the constructed controller's effectiveness is validated with two illustrative examples. [ABSTRACT FROM AUTHOR]

The predefined-time fuzzy adaptive output feedback decentralised control problem is considered for fractional-order nonlinear large-scale (FONLS) systems. Firstly, by using fuzzy logic systems (FLSs) to model unknown nonlinear dynamics, a fuzzy decentralised state observer is designed to solve the immeasurable state problem. Secondly, the predefined-time stability criterion of fractional-order nonlinear systems is proposed, and then, based on the established predefined-time stability criterion of the fractional-order nonlinear systems, a predefined-time fuzzy adaptive output feedback decentralised control method is developed by combining the backstepping design technique with the cyclic-small-gain approach, and the semi-global practically predefined-time stable (SGPPTS) of the control system is proved. Finally, the numerical simulation is given and shows the effectiveness of the presented predefined-time control method. [ABSTRACT FROM AUTHOR]

In this paper, an event-based adaptive fuzzy tracking control strategy for a class of uncertain nonlinear systems with limited control input and its rate of change is proposed. The unknown nonlinear function of the considered system is approximated by a fuzzy logic system (FLS). Furthermore, an event-triggered control method is developed to save the computing resources, in which the controller parameters are adaptively adjusted by a fuzzy controller. Moreover, it is proved by designing the Lyapunov function that the event-based controller can ensure that all signals of the controlled system are bounded. In addition, the tracking error can converge to a preset domain. Meanwhile, two practical simulations are given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a novel fractional-order adaptive controller is presented for a class of nonlinear systems with unknown dynamics. The dynamics of the system is considered to be fully unknown. The multi-layer perceptron (MLP) neural network using restricted Boltzmann machine (RBMs) is employed for online dynamic identification. A deep learning method on the basis of contrastive divergence (CD) algorithm combined with the extended Kalman filter (EKF) is proposed for online optimisation. The proposed controller has two parts. The first part is a simple error feedback controller and the second one is the suggested DT2-FLS. The parameters of DT2-FLS are tuned such that a cost function of tracking error to be minimised and the closed-loop system to be stable. For the best knowledge of the authors, for the first time the tuning rules for the membership function and rule parameters of DT2-FLS are derived by error feedback learning method. The closed-loop stability is demonstrated with Lyapunov method and the well performance of the schemed controller is shown by applying on the induction motor and brushless DC motors. In addition to unknown dynamics, some disturbances are also considered such as abruptly changes in load torque and time-varying rotor resistance. Furthermore, the performance of the suggested scheme is compared with some popular controllers and FLSs. [ABSTRACT FROM AUTHOR]

This paper studies disturbance estimation and compensation for discrete-time large-scale nonlinear descriptor systems with unknown measurement noises. Interval type-2 (IT-2) Takagi–Sugeno (T–S) fuzzy model is used to represent nonlinear dynamics, where fuzzy representation is assumed to be appearing not only in both the state and input matrices but also in the matrix of derivative state. First, by using a novel model transformation, the fuzzy representation in the derivative matrix is formulated into the linear one. Then, an augmented fuzzy observer is introduced to implement a synchronous estimation for the system state and the unknown measurement noise, which guarantees that reachable sets of the closed-loop system are bounded by a given ellipsoid. Moreover, a compensation-based controller is employed to remove or relieve the uncertainties induced by measurement noises. Finally, the validity of the obtained theories is testified by a numerical example. [ABSTRACT FROM AUTHOR]

This study presents a novel observer-based fuzzy control for chaotic systems (CSs). A class of CSs with unmeasurable states, unknown input constraints and unknown dynamics is taken to account. A generalised type-2 (GT2) fuzzy logic system (FLS) is formulated to approximate the uncertainties. The parameters of GT2-FLS are adjusted through the tuning rules that are extracted from robustness investigation. The proposed method is applied on CSs and it is demonstrated that the suggested observer and control system result in good performance in versus of disturbances, uncertainties, unknown and time-varying input nonlinearities, unmeasurable states and noisy faults. Comparison with other type of fuzzy systems demonstrates that the proposed GT2-FLS based controller is more effective. [ABSTRACT FROM AUTHOR]

An adaptive approximated inverse control scheme for a class of high-order nonlinear multi-agent systems with unknown control directions is proposed in this paper. The adaptive control problem under the case it is not required to be identical for the unknown control directions is solved by some novel Nussbaum functions and a monotonously increasing sequence, leading our multiple Nussbaum functions reinforce rather than counteract each other. Also, it is introduced in the design and analysis like some integrable auxiliary signals and a novel contradiction argument. Moreover, hysteresis nonlinearity is counteracted by constructing its approximated inverse compensator. With these efforts, stability analysis ensures that all the closed-loop system signals are uniformly bounded, and it is successfully achieved the asymptotic convergence of tracking error to zero, circumventing the obstacle caused by the unknown control directions. Experiment results illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper addresses a low-cost adaptive prescribed time tracking control strategy for switched nonlinear systems with input quantisation and unknown backlash-like hysteresis. Initially, a transformation function is introduced into the control design, which allows the tracking error of the controlled systems to a specified accuracy within a preassigned settling time. Unlike the existing prescribed time control scheme that only uses transformation function technique in the first step of the backstepping design, the transformation function is introduced to each step of the controlled systems, which makes only one adaptive parameter that needs to be updated online. Then, to synthesise a low-cost control scheme, a first-order sliding mode differentiator and a single-parameter estimation method are implemented based on the newly constructed coordinate transformations. Meanwhile, the Nussbaum technique successfully solves the effects caused by the unknown control direction, unknown backlash-like hysteresis and quantised input. All signals of the closed-loop systems are bounded under the proposed control scheme with arbitrary switching signal. Finally, the validity of the presented method is confirmed via two simulation examples. [ABSTRACT FROM AUTHOR]

In this article, the finite-time adaptive fuzzy output-feedback control strategy is presented for a class of multi-input and multi-output (MIMO) nonlinear systems with multiple actuator constraints and unmeasured states. Fuzzy logic systems and a fuzzy adaptive state observer are adopted to estimate the nonlinear uncertainties and the unmeasured states, respectively. The inherent 'explosion of complexity' problem is eliminated by adopting command filter technology and the corresponding error compensation mechanism is exploited to alleviate the influence of the errors generated by command filters. It is further proved that all the signals can be fast finite-time bounded in the closed-loop systems based on the Lyapunov stability theory. Besides, the observer and tracking errors can reach a small region around the origin in fast finite time. Finally, the practicability of the presented theoretic result can be demonstrated through the numerical and practical examples. [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]

A finite-time adaptive control is discussed in this paper for stochastic nontriangular nonlinear systems with unknown backlash-like hysteresis. The performance function is introduced to ensure that the tracking error of the system converges to the specified region in a finite time. Secondly, a controller is proposed using the fuzzy logic systems to approximate the unknown functions and backstepping technique. In addition, adding the dynamic event-triggered mechanism is able to reduce the network resources more effectively. At last, a few simulation examples are presented to demonstrate effective performance. [ABSTRACT FROM AUTHOR]

This article presents an event-based adaptive decentralised output feedback control scheme for interconnected systems with Bouc-Wen hysteresis and unmeasured system states. To reduce some unnecessary data transmissions, a novel dynamic threshold adjustable event-triggering mechanism is proposed. In contrast with the traditional static threshold event-triggering mechanism, communication efficiency is greatly enhanced. Then, a neural networks-based observer is constructed to address the problem of unmeasured states, and the dynamic surface control method is used to address the 'explosion of complexity' that comes up when traditional backstepping design processes are used. Meanwhile, the Nussbaum function is introduced to eliminate the effect of unknown hysteresis. By resorting to the Lyapunov stability theory, it can be verified that all signals in the close-loop system are uniformly ultimately bounded. Finally, a simulation example is given to verify the effectiveness of the developed control scheme. [ABSTRACT FROM AUTHOR]

Interval Type-2 (IT2) Fuzzy-Model-Based (FMB) control design has emerged as a prominent research area within the fuzzy control field, showcasing its capability to directly address uncertainties present in nonlinear control systems. The objective of this special issue is to showcase recent research findings in the IT2 FMB systems and foster the advancement of research activities in the field of IT2 FMB control systems. Dr. H.K. Lam made a significant contribution to the field by successfully conducting stability analysis of FMB control systems under parameter uncertainty in 2008 (Lam & Seneviratne, [1]). [Extracted from the article]

This paper investigates the parameter estimation of errors-in-variables Wiener (EIV-W) nonlinear systems. In such nonlinear systems, both input and output contain interference noises, and some intermediate processes are also interfered by noises. The hierarchical technology is applied to decompose the whole system into two subsystems firstly. For the linear subsystem, in order to obtain unbiased estimates of model parameters, a bias compensation method is introduced. Then, the bias-compensated least squares (BLS) algorithm is proposed. For the nonlinear subsystem, on the basis of particle swarm optimisation (PSO), the fuzzy control technology is added to improve the ability of jumping out of the local optimum. Thus, a bias-compensated least squares and fuzzy PSO based hierarchical (BLS-FPSO-H) method is derived at last. In simulation, a numerical example and a case study about the carbon fibre stretching process are implemented. Results indicate that the BLS-FPSO-H algorithm can effectively identify EIV-W nonlinear systems, the convergence speed and identification accuracy are greatly improved than the basic PSO method and some other PSO variants. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive output feedback control law for MIMO nonlinear systems with distinct unknown measurement errors in each output. The nonlinear terms have incremental rates which include nonlinear functions of output and polynomials of output multiplying unknown constants. By introducing an observer, and two classes of particular dual adaptive high-gains and a class of exclusive constant high-gains, an output feedback controller is developed to stabilise the MIMO nonlinear system. The designed controller is able to tolerate uncertain measurement errors with a relatively wide range. Finally, some simulations are provided to corroborate the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, the admissibility analysis and controller design issues for a class of nonlinear descriptor systems described by the T–S descriptor fuzzy model with partially unknown membership functions are discussed. New sufficient conditions are first given to ensure the admissibility of such fuzzy descriptor systems. To fully utilise the information of membership functions, the known and unknown parts of membership functions are handled by staircase functions and the bounds of the unknown part, respectively. Different from the traditional parallel distribution compensation (PDC) method, the state-feedback controller design condition is given under an imperfect premise matching method via linear matrix inequalities (LMIs). Some examples are given to show the validity of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper presents a novel event-triggered dynamic output feedback dissipative control of nonlinear systems under intermittent denial-of-service (DoS) attacks and actuator saturation. Firstly, based on attack information, a secure event-triggered mechanism (ETM) is introduced, which not only saves systems resources but also is Zeno-free and resilient to DoS attacks. Secondly, a switched T–S fuzzy closed-loop system model is built, which unifies the parameters of nonlinear plant, noises, ETM, DoS attacks, switched output feedback fuzzy controller, and actuator saturation all in one framework. Thirdly, low conservative exponential stability criteria are derived while guaranteeing strict (G , H , I) -dissipativity, and hence the relationships between system performance and factors such as DoS attacks, secure ETM, noises and actuator saturation are established. Further, sufficient conditions are given for the co-design of the switched output-based fuzzy controller and the secure ETM. Finally, the effectiveness of the proposed method is confirmed by numerical examples, achieving over 92% system resources. [ABSTRACT FROM AUTHOR]

In this article, we investigate the finite-time output feedback control issue for switched nonlinear systems. In the design procedure, fuzzy logic systems are used for identifying the unknown functions in switched nonlinear systems. A common state observer is constructed such that the unmeasured states can be measured. Based on the framework of the backstepping algorithm, an observer-based finite-time adaptive tracking control strategy is proposed via the common Lyapunov function approach. The stability analysis process confirms in detail that all the closed-loop signals are bounded under arbitrary switchings and the tracking error can converge to a small neighbourhood of the origin after a finite period of time. In order to verify the feasibility of the proposed control scheme, a numerical simulation example is shown in this paper. [ABSTRACT FROM AUTHOR]

A decentralised adaptive fuzzy event-triggered control problem of nonlinear switched large-scale systems with unknown hysteresis is investigated in this article, in which the unknown hysteresis is expressed by several nonlinear differential equations. The unknown nonlinearities in the systems are generally approximated by fuzzy logic systems due to their excellent function approximation abilities. Through employing dynamic surface control technology, the issue of complexity explosion resulting from repeated differentiation in the traditional backstepping method is overcome. A decentralised event-triggered controller is developed through adopting the common Lyapunov function approach. In the closed-loop system, the whole signals are semi-globally uniformly ultimately bounded which can be guaranteed through adopting the proposed scheme. Ultimately, the simulation results of two examples demonstrate the validity of the proposed strategy. [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]

In this paper, we solve the trajectory tracking control problem of autonomous underwater vehicles (AUVs) with actuator faults and model uncertainties. In order to achieve the output tracking with prescribed transient performance, we develop a novel tan-type barrier Lyapunov function (BLF)-based funnel tracking control strategy to constrain the position tracking error of the AUV within prespecified performance funnels. Furthermore, to maintain the predesigned transient tracking performance of the AUV when the actuator fault occurs, an event-triggered adaptive fuzzy fault-tolerant control (FTC) scheme is proposed to compensate actuator faults. Meanwhile, to handle the uncertainties caused by the system model, fuzzy logic systems (FLSs) are adopted to approximate the unknown hydrodynamic parameters and actuator fault parameters. Different from the existing results, the communication burden and the excess wear from the controller to the actuators are reduced utilising the event-triggered mechanism. By using the theory of Lyapunov stability, it is proven that all the signals of the closed-loop system are semiglobally uniformly bounded. Finally, the effectiveness of the proposed method is demonstrated by a simulation example. [ABSTRACT FROM AUTHOR]

This paper is concerned with the adaptive fuzzy control for uncertain switched discrete-time nonlinear systems in a pure-feedback form under arbitrary switchings. The design and the stability analysis of the adaptive fuzzy control with state constraints are based on the adaptive fuzzy backstepping control and the Lyapunov function. The feature of the proposed approach is as follows. (i) The proposed adaptive fuzzy control is designed by suppressing the explosion problem of the complexity in the backstepping control, (ii) In the fuzzy inference approach, the nonlinear uncertainties are approximated by using the fuzzy logic systems based on the simplified extended single-input rule modules, and (iii) The proposed estimator to take the estimates for the unmeasurable states, the adjustable parameters and the real control is in a simplified structure designed. The simulation experiment provides that the proposed approach improves the system performance. [ABSTRACT FROM AUTHOR]

In this paper, a finite-time adaptive fuzzy control approach is developed for asymptotic tracking control of permanent magnet synchronous motors (PMSMs) with full-state constraints. Multiple nonlinear state-dependent functions are introduced in the control design, such that the rotor position, the rotor angular velocity and the currents of the d-q axis are confined to effective ranges. By combining backstepping control design with the finite-time control technique, a novel adaptive fuzzy tracking control is developed, which can accelerate the convergence speed and improve the robustness of the PMSM servo systems. Moreover, the asymptotic tracking control can be achieved with the aid of a scaling function. It is proven that all signals of the closed-loop system are uniformly ultimately bounded, and the position tracking error can converge to zero asymptotically in finite time. Finally, the simulation results are provided to verify the effectiveness of the proposed control approach, and some comparisons are given to show the rapid and accurate position tracking performance. [ABSTRACT FROM AUTHOR]

This paper investigates an adaptive cooperative fault-tolerant supervisory control problem for nonlinear strict-feedback leader-following systems with unknown control coefficients and actuator faults under the fixed directed graph. Radial basis function neural networks are used to approximate system uncertainties. The Nussbaum gain technique is introduced to address unknown signs of control gains. Then, based on the dynamic surface control method, a distributed adaptive fault-tolerant control scheme is presented to compensate for the actuator faults of followers. For neural networks approximation theory, the unknown smooth function can only be approximated on a compact set due to the approximation errors. By theoretic analysis, the supervisory-based adaptive controllers are proposed to guarantee that system state signals can converge to compact sets and the leader-following systems can achieve the practical output consensus. Finally, the simulation results are provided to show the validity of the proposed consensus scheme. [ABSTRACT FROM AUTHOR]

This article presents an adaptive fuzzy asymptotic tracking control method for switched nonlinear systems with state constraints. The fuzzy logic systems are employed to dispose the lumped unknown dynamics. The state constraints are addressed with the aid of the barrier Lyapunov function. Moreover, drawing support from some well-defined smooth functions, the asymptotic tracking controller is recursively constructed based on backstepping technique. Through Lyapunov analysis and the common Lyapunov function method, it is shown that the system output tracking error asymptotically converges to zero and all system signals are bounded under the switching signal. Finally, the validity of the developed scheme is elucidated by simulation example. [ABSTRACT FROM AUTHOR]

In this study, a novel decentralised fault-tolerant control strategy is developed for a class of interconnected nonlinear time delay systems with unmeasurable state variables and actuator faults by using dynamic event triggering mechanism. The interconnected system model includes nonlinear time delay functions and unstructured uncertainties, which could be identified by the radial basis function neural networks approximation technique. The adaptive nonlinear observer is designed for each interconnected subsystem to obtain the estimated values of unmeasurable state variables and unknown system faults simultaneously. Then, a decentralised fault-tolerant tracking controller is designed for the considered interconnected time delay systems using both backstepping control procedures and a dynamic event triggering mechanism, which has the smaller event trigger frequency by comparing the traditional static event-triggered control scheme, such that the tracking error signals converge to a small neighbourhood near the origin and all the signals of the closed-loop system are globally bounded. Finally, a chemical reactor example is given to illustrate the feasibility and significance of the designed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, the problem of asynchronous sliding mode control (SMC) is addressed for discrete-time singular Markovian jump systems (DSMJSs) with external disturbances and randomly occurring nonlinearities. Moreover, the states of DSMJSs are unavailable for measurement, and the synchronisation between the controller and the system modes is not surely ensured. Our attention is mainly focused on solving the control problem by proposing an observer-based adaptive sliding mode controller for such a class of complex systems. First, a non-fragile observer is designed not only to estimate the system states but also to relax the multiplicative gain variations and to construct an appropriate mode-dependent sliding mode surface function. Then, by assuming that the bounds of nonlinear terms are unknown, an adaptive SMC control law is synthesised to drive the system trajectories onto the specified sliding surface and completely compensate for the influences of the external disturbances and fluctuations of the controller and observer gains. Sufficient conditions are established to ensure that the closed-loop system is stochastically admissible with a γ level of the mixed H ∞ /passive performance, and to provide the observer and controller gain matrices. Finally, to numerically substantiate the efficacy of the proposed control scheme, two examples are given. [ABSTRACT FROM AUTHOR]

In this article, state estimation-based adaptive control problem for a class of uncertain systems subject to time-delay and external disturbance is investigated within sliding mode framework. A simplified state observer without any inputs is designed to reconstruct the unmeasured state variables, from which a novel linear switching surface is provided. By applying a novel adaptive reaching motion controller, the existence and arrival of the defined switching surface are established. It is shown that in the framework of Lyapunov stability theory and linear matrix inequality technique, the proposed control scheme achieves the desirable performance for the closed-loop system. Two illustrative examples are given to substantiate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time adaptive fuzzy control method is proposed for nonlinear nonstrict-feedback multi-agent systems (MASs) with unknown control directions. The Nussbaum function is introduced to compensate the influence of unknown control directions. By combining the finite-time stability criterion, an adaptive fuzzy controller is constructed to ensure that systems reach a steady state in a finite time, and the tracking errors of all output signals converge to a small neighbourhood of the origin. By using the Lyapunov finite-time stability theorem, all signals are proved to be semi-global practically finite-time stable. Finally, the effectiveness of the proposed control method is proved through some results of simulation experiments. [ABSTRACT FROM AUTHOR]

In this paper, the finite time tracking control issue of the TCP/AQM system with input saturation and non-response flows such as UDP is studied. A novel AQM control algorithm is proposed by combining finite time control, prescribed performance control and fuzzy logic system. In order to obtain better disturbance suppression performance, the fuzzy logic system is adopted to deal with the external disturbance and the error disturbance generated by approximate input saturation function. The controller is designed to ensure that the queue tracking error converges to a specific region and achieve the practical finite time stability of all signals in the closed-loop system. The simulation results demonstrate the feasibility and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This article studies a tracking control problem for a class of stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation. Firstly, the Gauss Error Function is introduced to solve the difficulty arising from the saturation nonlinearity. Meanwhile, to overcome the problem of calculating explosion caused by the repeated differentiation of the virtual control signals, a finite-time command filter with a compensation mechanism is developed. Combining the neural networks' approximation ability and the backstepping technique, an adaptive neural finite-time control strategy is proposed for the considered system by constructing the time-varying barrier Lyapunov function. Under the proposed control strategy, it is guaranteed that all signals are bounded in a sense of mean square, the output of the system can track the reference signal within a finite time and all states will not violate the constraints. Furthermore, the stability of the closed-loop system is analysed based on the stochastic finite-time stability theory. Finally, a simulation example verifies the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper studies the issue of event-triggered adaptive fixed-time control for uncertain large-scale nonlinear systems. A state observer is employed to estimate unknown states. By combining the technologies of command filter and backstepping control, a command filtered-based fixed-time adaptive control approach is proposed, which can cope with the issue of 'complexity explosion' in the backstepping design scheme. Meanwhile, a novel continuous hyperbolic tangent function is designed to handle the difficulty of fixed-time stability analysis with output-feedback control form, where two types of observer errors are needed in the designed Lyapunov function to achieve the fixed-time stability. Further, based on the event-triggered strategy, an event-triggered fixed-time adaptive controller is designed. Based on Lyapunov stability theory, it is proven that all signals of the closed-loop system are bounded. Finally, the effectiveness of the presented approach is validated by a simulation. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive fuzzy event-triggered output feedback control scheme is studied for a class of non-strict feedback systems with tracking error constrained and unknown dead-zone. Fuzzy logical systems (FLSs) are employed to solve the uncertainties of the controlled plant, and the immeasurable states are estimated by employing the fuzzy observer. In the designed observer, to compensate for the effect of unknown dead-zone by invoking the known designed event-triggered input signal, an adaptive parameter is designed. Furthermore, due to event-triggered control (ETC) places a great burden on the computing space, the dynamic surface control (DSC) technique is used to solve the problem of 'explosion of complexity' in each step, and the simplified barrier Lyapunov function (SBLF) is utilised to restrict the tracking error within the prescribed boundaries. Consequently, the designed parameter adaptive laws and event-triggered controller can ensure that all signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Ultimately, two numerical simulation examples are provided to illustrate the effectiveness of the proposed event-triggered control theory. [ABSTRACT FROM AUTHOR]

Due to the frequent occurrence of random disturbances in practical physical systems, the control design of stochastic systems has a significant application background. This article presents an event-triggered adaptive fuzzy control scheme for nonstrict-feedback switched stochastic nonlinear systems with state constraints. The barrier Lyapunov functions are deployed to make all states maintain the prescribed regions. In addition, the event-triggered mechanism is incorporated into the backstepping framework to mitigate the data transmission. The fuzzy logic systems are exploited to cope with the system uncertainties, and then the adaptive fuzzy control strategy is recursively constructed. The devised event-triggered adaptive fuzzy controller can not only surmount the influence of state constraints but also decrease unnecessary resource consumption. In virtue of common Lyapunov function method, it is shown that all system signals are bounded under switching signals and the predefined constraints are not violated. Finally, the validity of the presented scheme is elucidated by simulation results. [ABSTRACT FROM AUTHOR]

This paper studies the fuzzy decentralised control issue for the fractional-order interconnected nonlinear systems with input saturation. The fuzzy logic systems (FLSs) and piecewise functions are used to approximate unknown nonlinear continuous functions and the input saturation of the considered systems. Under the frameworks of the backstepping control design technique and adaptive fuzzy decentralised control theory, an adaptive fuzzy decentralised control strategy is proposed. In view of fractional-order Lyapunov stability theory, it confirms the stability of the control plants and the convergence of the tracking errors. Finally, two simulation examples are applied to confirm the practicability and validity of the presented control strategy, in which, one is the two-order numerical fractional-order large-scale systems, the other one is the fractional-order permanent magnet synchronous motor (PMSM) model. [ABSTRACT FROM AUTHOR]

This brief addresses a distributed adaptive asymptotically synchronous tracking problem based on guaranteed connectivity for networked uncertain nonholonomic mobile robots (NMRs) with actuator failures and unknown control directions. First, a radial basis function (RBF) neural network is used to approximate the unknown nonlinear functions, and a distributed nonlinear error surface is introduced to achieve synchronous tracking between NMRs and maintain the initial connectivity patterns. Then, a conditional inequality that allows multiple piecewise Nussbaum functions to achieve robust control is proposed to solve the problem of unknown actuator failures and unknown control directions. Moreover, the proposed protocol ensures that all signals in the closed-loop system are globally bounded and the tracking errors converge asymptotically to zero. Finally, a simulation example verifies the effectiveness of the proposed adaptive laws. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive robust boundary controller is designed for an unstable heat equation with external disturbance flowing into the control end. The disturbance and uncertainty effect is cancelled out in the closed-loop system by using on-line approximation of disturbance upper-bound provided as an output of adaptive robust update law. The asymptotic stability of the controlled system in the presence of unknown disturbance and/or parametric uncertainties is proved utilising the Lyapunov theorem. Unlike previous researches, the control implementation does not require prior knowledge of unknown disturbance. Moreover, the controller does not require measuring the system states or estimating the system states by an observer. The adaptive robust controller can be regarded as a combination of the best qualities of the adaptive controller and the robust controller with no prior knowledge of system uncertainty, and asymptotic tracking error performance. Numerical simulations and comparisons are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies the problem of adaptive fuzzy tracking control for a class of pure-feedback switched nonlinear systems with unknown gain. First, the studied system is handled by using the mean value theorem, the initial pure-feedback nonlinear systems become affine nonlinear systems. Then, the unknown signal is handled through a linear transformation, the Nussbaum-type functions are used to design an effective controller for the processed system. To avoid the issue of 'explosion of complexity' caused by mean value theorem and backstepping procedure, a first-order sliding-mode differentiator is employed to simplify the calculation. Combined with the average dwell time (ADT) method, a set of switching signals are given to sure the stability of the system. The Lyapunov theorem is used to verify that all signals are semiglobal uniformly ultimately boundness (SGUUB), and the errors can be regulated arbitrarily small. Finally, simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a computationally efficient robust predictive control method is proposed for continuous-time under-actuated SISO systems in the presence of actuator saturation and state-dependent uncertainties. The proposition of this research is to employ the idea of model prediction together with the Adaptive Fuzzy Sliding-Mode Control (AFSMC) for tuning the sliding surface parameters by predicting the anticipated effects of uncertainties. In the proposed scheme, only after the trigger conditions are met, the coefficients of the sliding surface are updated and the AFSMC is applied. Hence, computational complexity can be controlled by adjusting the switching rule. In the AFSMC, a fuzzy system is used to approximate a nonlinear function, and a robust term to compensate for any possible mismatches. An adaptively tuned gain is also applied to the control signal to prevent instability caused by the actuator saturation. Based on the updating sliding surface, fuzzy singletons, the upper bound of the fuzzy approximation error, and the saturation gain are adaptively tuned. Closed-loop stability is shown to be guaranteed using the multiple Lyapunov functions theorem and the Barbalat's lemma. Finally, the method is applied for the depth control of an Autonomous Underwater Vehicle (AUV), depicting the excellent performance of the proposed method. [ABSTRACT FROM AUTHOR]

Based on Lyapunov finite-time stability theory and backstepping control strategy, we put forward a novel fast finite-time dynamic surface tracking control scheme for a single-joint manipulator system with prescribed performance. Compared with most of the traditional prescribed performance finite-time control methods, this work uses a new universal asymmetric barrier function and has a faster convergence speed. In addition, our proposed method eliminates the problem of explosion of complexity based on the dynamic surface. The proposed controller ensures that the closed-loop system is finite-time stable, and the output variable tracks the desired output signal at finite time. Meanwhile, the tracking error converges to the prescribed boundary constraints, and all the signals of the closed-loop system are bounded. In the end, a simulation example is presented to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper aims at putting forward an adaptive fuzzy hierarchical sliding mode control method to deal with the control problem for uncertain under-actuated switched nonlinear systems with actuator faults. First, both the bias fault and the loss of effectiveness are considered for the actuator faults. Then, the unknown uncertain functions of the under-actuated switched nonlinear system are approximated by utilising the approximation ability of fuzzy logic systems (FLSs). The error terms caused by FLSs and the upper bound of the bias signal of the actuator faults are integrated into a function where the upper bound of the function is on-line estimated. Furthermore, the singular problem of denominators can be well solved by introducing the projection algorithm. Based on our proposed control scheme, the boundedness of all the signals in the closed-loop system can be verified by resorting to the Lyapunov stability theory. Finally, a simulation example is given to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]