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This paper mainly investigates the fixed-time almost disturbance decoupling (ADD) stabilisation problem for a class of nonlinear systems in the p-normal form. Under suitable assumptions, a novel fixed-time ADD feedback controller is designed to ensure all states in the underlying system are not affected by disturbances. By constructing an appropriate Lyapunov function, it is shown that the corresponding closed-loop system is fixed-time stable which means the upper bound of the settling time depends on the control design parameters only. To testify the effectiveness of the proposal, two simulation examples are presented. [ABSTRACT FROM AUTHOR]

This paper investigates the stochastic path following control of underactuated marine vehicles (UMVs) subject to multiple disturbances and constraints. Firstly, the complex marine environment in which UMVs navigate typically contains stochastic components, thus the multiple disturbances are categorized as slow-varying deterministic disturbances and stochastic disturbances. Secondly, a position-constrained line-of-sight (PCLOS) based fractional-order sliding mode stochastic (FSMS) control strategy is established to achieve path following control of UMVs. A PCLOS guidance law based on universal barrier Lyapunov function is proposed to ensure that the position errors remain within the constraint ranges, which is versatile for systems with symmetric constraints or without constraints. An FSMS controller based on fractional-order theory and sliding mode control is designed to improve the dynamic response speed of the system and effectively attenuate chattering phenomenon. A stochastic disturbance observer is developed to estimate the slow-varying deterministic disturbances in the stochastic system, and auxiliary dynamic compensators are used to mitigate the impact of input constraints. Lastly, theoretical analysis indicates that the closed-loop system is stable and the position constraint requirements are satisfied. Comparative simulations illustrate the effectiveness of the proposed control strategy. • The multiple disturbances are categorized as slow-varying deterministic disturbances, and stochastic disturbances. • A novel PCLOS guidance law is designed to ensure that the position errors do not exceed the given constraint ranges. • The fractional-order sliding mode stochastic control method exhibits improved robustness and flexibility compared to traditional integer-order sliding mode control method. [ABSTRACT FROM AUTHOR]

This manuscript discuss the issue of stabilization for positive 2D (two dimensional) fractional order continuous Takagi-Sugeno Fuzzy Systems represented by the Roesser model with fixed delays. Employing linear programming approach while establishing positivity constraint on the 2D fractional system in closed-loop, delay-independent stabilization conditions are introduced by using Lyapunov-Krasovskii functions. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive dynamic surface (DSC) guidance law for missile is designed to intercept the maneuvering target with field-of-view (FOV) and terminal angle constraints in three-dimensional(3D) space, and the missile autopilot dynamics is considered. Firstly, the time-varying transformation function related to line of sight (LOS) is used to replace the FOV constraints, transforming the process-constrained control problem into the output-constrained control problem. Meanwhile, the 3D coupled relative kinematics model considering missile autopilot dynamics and maneuvering target acceleration is established. Secondly, a novel time-varying asymmetric barrier Lyapunov function (TABLF) with dead-zone characteristics is introduced to the adaptive dynamic surface guidance law design process to improve the robustness of parameter debugging. Thirdly, with the help of a nonlinear adaptive filter, the 'explosion of complexity' problem can be avoided effectively, which is caused by analytic computation of virtual signal derivatives. Furthermore, aiming at the problem of autopilot dynamic errors, target acceleration disturbances, and unmeasurable parameters in the model, a novel adaptive law is used to evaluate online. Then, the stability of the closed-loop system is rigorously proven using Lyapunov criteria. Ultimately, Numerical simulations with various constraints and comparison studies have been considered to show the feasibility and effectiveness of the proposed missile guidance law. • The terminal angle constraint guidance (TACG) law within field of view (FOV) constraints is developed in the three-dimensional(3D) space while considering the autopilot dynamics and the maneuvering target acceleration simultaneously, which can promote the guidance accuracy. • The utilization of the novel FOV constraint transformation can transform the FOV constraints into the output constraints, which facilitates the derivation of adaptive dynamic surface control (DSC) scheme. • The time-varying asymmetric barrier Lyapunov function (TABLF) with the dead zone characteristic is designed to derive the 3D TACG law with FOV constraints, which can enhance the accuracy of convergence and improve the robustness of parameter debugging. • The 'explosion of complexity' is avoided by using the novel nonlinear adaptive filter. In addition, a novel adaptive law is designed to adapt the unknown parameters automatically containing autopilot modeling error, target acceleration disturbance, and unmeasurable parameters. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of event-triggered stabilization for neural networks vulnerable to replay attacks. According to the proposed event triggering mechanism and controller, the neural network is represented as a switched closed-loop system. Through the selection of a suitable Lyapunov function and applying Jensen's inequality, a criterion to ensure the mean square exponential stability of the system is established in the form of linear matrix inequalities. Building on this, a co-design method for the event trigger matrix and controller gain is given. Finally, a numerical example is used to verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the optimal tracking control problem of flexible-joint robotic manipulators in order to achieve trajectory tracking, and at the same time reduced the energy consumption of the feedback controller. Technically, optimization strategies were well-integrated into backstepping recursive design so that a series of optimized controllers for each subsystem could be constructed to improve the closed-loop system performance, and, additionally, a reinforcement learning method strategy based on neural network actor-critic architecture was adopted to approximate unknown terms in control design, making that the Hamilton-Jacobi-Bellman equation solvable in the sense of optimal control. With our scheme, the closed-loop stability, the convergence of output tracking error can be proved rigorously. Besides theoretical analysis, the effectiveness of our scheme was also illustrated by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, a gradient dynamics-based control method is proposed to directly tackle the singularity problem in the backstepping control design of the TORA system. This method is founded upon the construction of an energy-like positive function, which includes an auxiliary variable in terms of the intermediate virtual control law. On this basis, a gradient dynamics is created to obtain a new virtual control command, which is capable of making the auxiliary variable gradually approach zero, thereby mitigating the issue of division by zero. The core innovation is the integration of the gradient dynamics into the recursive backstepping design to overcome the singularity problem and stabilize the system at the equilibrium quickly. In addition, it rigorously proves that all the signals in the closed-loop control system are uniformly ultimately bounded, and the tracking errors converge to a small neighborhood around zero through a Lyapunov-based stability analysis. Comparative simulations demonstrate that the proposed approach not only avoids the singularity issue, but also achieves a better transient performance over other methods. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of trajectory tracking for wheeled mobile robots subject to time-varying input delay and bounded external disturbances. First, we establish a dynamic model for a wheeled mobile robot under sliding and skidding conditions, and determine the maximum allowable input delay that maintains system stability using Razumikhin-type stability analysis without prior knowledge of the variation in delay. The proposed adaptive robust controller combined with super-twisting sliding mode control is resilient to disturbances such as input delay, system uncertainty, and parameter variation. The proposed adaptive law enables real-time modification of switching gain based on tracking error without predefined knowledge of uncertainty bounds. Compared with traditional sliding mode control strategies, the super-twisting algorithm can eliminate chattering phenomenon while combined robust methods further reduce modeling uncertainties' influence on system performance. Finally, we select an appropriate Lyapunov function to analyze and prove uniformly ultimately bounded of the closed-loop system. MATLAB simulation comparison results demonstrate that this approach achieves high tracking accuracy, faster response speed, and robustness. [ABSTRACT FROM AUTHOR]

This paper studies the event-triggered control for uncertain switched systems under injection attacks. An adaptive event-triggered control method for neural network–approximated switched systems (NNA-SSs) is proposed. The main works are as follows: First, a neural network is introduced to approximate the uncertain nonlinear item of the systems. Second, the observer-based adaptive event-triggering (OB-AET) strategy is designed to efficiently utilize communication and computing resources. Furthermore, the closed-loop switched systems considering injection attacks are established. By utilizing the Lyapunov function method and average dwell time technique, sufficient conditions for the exponential stability of the closed-loop switched systems are given. Accordingly, the gains of the state feedback controllers and observers are solved. Finally, simulation examples are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The paper studies the design of interval observer–based event-triggered H ∞ controller for switched systems with frequent asynchronism. We first propose an interval observer–based event-triggered mechanism, which is used to transmit the observer states and the activated mode information for controller updating. Then, we design an interval observer–based event-triggered H ∞ controller for the considered systems. By using the controller mode–dependent Lyapunov function method, sufficient conditions for the globally exponential stability of the closed-loop switched systems are obtained under average dwell time switching law. Moreover, we prove that the interevent interval length is a positive constant, which can exclude the Zeno behavior. Compared with the literature, the paper removes the strict requirement of at most one switch between two event-triggered instants, that is, frequent switches within an interevent interval are allowed. Finally, a numerical example is given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive stabilisation of output-constrained high-order nonlinear systems with more general high-order and low-order nonlinearities. By skilfully introducing nonlinear mappings, a key coordinate transformation, integral Lyapunov functions and the sign function into the adding a power integrator technique, and combining a novel analysis method, a continuous adaptive state-feedback controller is constructed. When the initial value of system states lies in the constrained set, it is rigorously proved that all the closed-loop signals are uniformly bounded, the asymmetric output constraint isn't transgressed, and the equilibrium point of the closed-loop system is uniformly asymptotically stable. A simulation example shows the effectiveness of this control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the non-singular practical fixed-time neural adaptive control issues for multi-input and multi-output (MIMO) nonlinear systems with non-strict feedback form. Neural networks (NN) are used to estimate the unknown nonlinearities and deal with the problem of an algebraic loop. Under the framework of the backstepping control design, a practical fixed-time adaptive NN control method is developed by using the adding power integration technology. According to the Lyapunov function theory, it is proved that the closed-loop system is practical fixed-time stable, and the system can track the desired reference signal within a fixed time. Finally, the proposed practical fixed-time control method is applied to a multi-motor control platform, which proves the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

This article investigates the fixed-time stabilization (FTS) problem for lower-triangular nonlinear systems under the p -normal form. Under suitable assumptions condition of the system's nonlinearities, a novel fixed-time partial-state feedback (PSF) control law is designed using backstepping design method and adding a power integrator (API) technique. With the help of one appropriate Lyapunov function, it is shown that the partial-state of the corresponding closed-loop system is fixed-time stable. Numerical simulation is given to illustrate the potency of the suggested continuous partial-sate feedback controller. [ABSTRACT FROM AUTHOR]

This paper is devoted to adaptive trajectory tracking of quadrotor systems with full-state constraints and random disturbances. The command-filtering technology combined with error compensation mechanisms is proposed to deal with the "explosion of complexity" problem in traditional backstepping design and reduction of the filtering errors. The Barrier Lyapunov functions (BLFs) are constructed to ensure that all system states do not violate the boundary of its constraints. The command-filtered adaptive fuzzy 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 by parameters tuning. Finally, the effectiveness of the proposed control method is illustrated by simulation results. [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]

The missile integrated guidance and control (IGC) problem with seeker's asymmetric field-of-view (FOV) angle constraints is addressed. In the introduced model, the fin deflections controller is used to drive the body line-of-sight angle rate, which avoids the solving and tracking of aerodynamic angles in traditional IGC method. A novel fixed-time convergence virtual input based on the integral barrier Lyapunov function is designed to ensure the asymmetric FOV angle constraints are never violated. The virtual input is tracked by a new proposed pre-defined fixed time controller with adjustable initial convergence speed. The lumped uncertainty including aerodynamic coefficient and target maneuvering is coped by the fixed-time disturbance observer. It is proved that the closed-loop system states are converged to the bounded region in a fixed-time and the asymmetric FOV angle constraints are satisfied. The 6-degree of freedom flight simulations and comparisons verified the advantages of the proposed algorithm. • The asymmetric FOV constrained IGC model is formulated as a dual integration system. • The IBLF guarantees the output states satisfying the asymmetric constraints. • The pre-defined time controller ensures the states converge in the pre-defined time. [ABSTRACT FROM AUTHOR]

This paper addressed the adaptive control problem for non-strict-feedback nonlinear systems with dead-zone and external disturbances. The design methodology integrated the backstepping technique with the approximation of unknown functions using an echo state network (ESN), enabling real-time adjustments. A comprehensive Lyapunov stability study was conducted to confirm the semi-globally uniformly ultimately boundedness (SGUUB) of all signals in the closed-loop system, ensuring that the tracking error converged to a small neighborhood of the origin. The effectiveness of the proposed method was further demonstrated through two examples, and error assessment criteria were utilized for comparisons with existing controllers. [ABSTRACT FROM AUTHOR]

In this paper, a composite finite-time prescribed performance tracking control scheme is presented for an unmanned helicopter (UH) system subject to performance constraints, model uncertainties and external perturbations. A new finite-time neural network disturbance observer (FTNNDO) with adaptive laws is designed to deal with the external disturbances and model uncertainties, which not only accelerate the convergence rate in finite time but also eliminate the complicated differential calculation in the traditional backstepping scheme. Using the continuous adaptive law, the neural network (NN) approximate errors can be effectively estimated and compensated online without the chattering and gain overestimation caused by traditional methods, thus further enhancing the robustness of the system. To constrain the tracking performance of the transient process and steady-state accuracy, a novel prescribed performance function is designed to preset the tracking errors within prescribed boundaries. Based on the FTNNDO and barrier Lyapunov function (BLF), an improved finite-time tracking controller is designed to achieve fast convergence with prescribed performance. By using Lyapunov synthesis, it is strictly proven that the finite-time convergence of the closed-loop control system can be achieved and tracking errors are always within the prescribed performance bounds. In the end, simulation results for the UH tracking control system are given to demonstrate the effectiveness of developed control scheme. [ABSTRACT FROM AUTHOR]

This paper focuses on the finite-time tracking control problem of shield tunneling systems in the presence of constraints on the states and control input. By modeling the system based on the LuGre friction model, an effective method of tracking control in finite time is designed to overcome these actual constraints at the same time. First, the constraint on the system state is transformed into a symmetric constraint on the tracking error, and the constraint on control input is handled by designing an auxiliary differential equation. Then, radial basis function (RBF) neural networks are introduced to approximate the uncertainties. Next, using an adaptive finite-time backstepping method and choosing a logarithmic barrier Lyapunov function (BLF), a finite-time controller is designed to realize the finite-time stability of the closed-loop system. Finally, a simulation example is given to verify the correctness and validity of the theoretical results. [ABSTRACT FROM AUTHOR]

The problem of stabilizing a chain of three integrators subject to a phase constraint is studied. Continuous constrained control in the form of nested sigmoids, which guarantees the fulfillment of the phase constraint, is synthesized. A Lyapunov function is constructed, and necessary and sufficient conditions of global stability of the closed-loop system are established. The discussion is illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of spacing control between adjacent trains in train formation and proposes a distributed train-formation speed-convergence cooperative-control algorithm based on barrier Lyapunov function. Considering practical limitations such as communication distance and bandwidth constraints during operation, not all trains can directly communicate with the leader and obtain the expected trajectory it sends, making it difficult to maintain formation consistency as per the predetermined ideal state. Furthermore, to address the challenge of unknown external disturbances encountered by trains during operation, this paper designs a distributed observer deployed on each train in the formation. This observer can estimate and dynamically compensate for unknown reference trajectories and disturbances solely based on the states of adjacent trains. Additionally, to ensure that the spacing between adjacent trains remains within a predefined range, a safety hard constraint, this paper encodes the spacing hard constraint using barrier Lyapunov function. By integrating nonlinear adaptive control theory to handle model parameter uncertainties, a barrier Lyapunov function-based adaptive control method is proposed, which enables all trains to track the reference trajectory while ensuring that the spacing between them remains within the preset interval, therefore guaranteeing the asymptotic stability of the closed-loop system. Finally, a practical example using data from the Guangzhou Metro Line 22, specifically the route from Shiguang Road Station to Chentougang Station over three stations and two sections, is utilized to validate the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The paper focuses on the issue of predefined‐time adaptive fuzzy tracking control for switched nonlinear systems. The complicated unknown nonlinear function is approximated by utilizing the fuzzy logic system. A novel adaptive fuzzy control scheme is proposed by using the common Lyapunov function and backstepping technology on the basis of the definition of predefined‐time stability, which can preset the setting time of tracking errors. The findings indicate that the presented controller can ensure that all signals remain bounded in the closed‐loop system under arbitrary switching conditions, and the tracking error can converge to the neighborhood near the origin within a predefined time. The feasibility of the developed control approach is further proved through an example of numerical simulation. [ABSTRACT FROM AUTHOR]

This paper proposes the finite-time state-constrained adaptive fault-tolerant control method for a class of heavy launch vehicles subject to quantized input signals and actuator faults. Firstly, the attitude dynamic model of the heavy launch vehicles suffering from the actuator faults and disturbances is formulated. Secondly, to deal with the completely unknown nonlinear functions, radial basis function neural networks (RBFNNs) are introduced for approximation and compensation. Meanwhile, the barrier Lyapunov function (BLF) is introduced to ensure that all the states in the closed-loop system are bounded and the constraints of the system states are satisfied. As a result, the finite-time state-constrained adaptive fault-tolerant control structure is constructed for the heavy launch vehicles and the semi-globally practical finite-time stability (SGPFS) of the closed-loop control system is proved. Finally, numerical simulation results show the effectiveness and the satisfactory performance of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the bumpless transfer control of linear switched positive systems based on state and disturbance observers. First, state and disturbance observers are designed for linear switched positive systems to estimate the state and the disturbance. By combining the designed state observer, the disturbance observer, and the output, a new controller is constructed for the systems. All gain matrices are described in the form of linear programming. By using co-positive Lyapunov functions, the positivity and stability of the closed-loop system can be ensured. In order to achieve the bumpless transfer property, some additional sufficient conditions are imposed on the control conditions. The novelties of this paper lie in that (i) a novel framework is presented for positive disturbance observer, (ii) double observers are constructed for linear switched positive systems, and (iii) a bumpless transfer controller is proposed in terms of linear programming. Finally, two examples are given to illustrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

Unknown actuator failures are inevitable in practical systems. At the same time, time delay exists in many physical actuators, and the system performance will be affected by such actuator delay and faults. However, the results of studies that attempt to compensate for unknown failures of actuators with time delay are still very limited. In this paper, such a problem is studied, and an adaptive control scheme is proposed based on backstepping approaches. First, the input delay of actuator faults and output disturbances are transformed into unknown effects on the output signal. In the backstepping recursive design, these unknown effects will accumulate to the last step of the controller design. Then, a new Lyapunov function is constructed by introducing auxiliary signals to prove the stability of the system. It is shown that the proposed control scheme can compensate for the effects caused by unknown actuator failures and input delays. The stability of the closed-loop system can be guaranteed by this adaptive controller. Finally, simulation studies are used to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper is concerned with the event-triggered control of switched two-dimensional (2D) continuous-discrete systems in Roesser model. A more general event-triggered scheme is proposed to reduce unnecessary resource waste and data redundancy, where a weighing coefficient and multiple parameter matrices are used. Based on the proposed event-triggered mechanism, a state feedback controller and a state-dependent switching signal are proposed. By using the multiple Lyapunov function method, sufficient conditions for the exponential stability of the closed-loop system are derived in terms of linear matrix inequalities. Finally, two examples are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of bumpless transfer control for switched impulsive positive systems with L 1 -gain performance. For switched impulsive positive systems, the control bump is brought from both switching behavior and impulsive effects. First, the concept of bumpless transfer performance is proposed by imposing magnitude constraints on the control signal to measure the level of bumpless transfer in the switched impulsive positive system. Then, based on a new class of multiple piecewise-continuous copositive Lyapunov function, a tighter mode-dependent average dwell time switching signal bound and bumpless transfer controller are designed to realize positive property, bumpless transfer and L 1 -gain performance of the closed-loop systems. Finally, the feasibility and superiority of the proposed control strategy are illustrated by simulations. • A novel bumpless transfer performance definition is proposed for SIPSs. • The multiple piecewise-continuous copositive Lyapunov functions are constructed. • A mode-dependent and quasi-time dependent controller is designed. [ABSTRACT FROM AUTHOR]

Friction nonlinearity and uncertainty are the main factors affecting the highly performance control of electromagnetic actuators. In this paper, a nonlinear adaptive robust control strategy is proposed of electromagnetic actuators with friction nonlinearity and uncertainty compensation. First, the dynamical model of the electromagnetic actuator is established considering nonlinearity and uncertainty. Then, an adaptive robust controller is designed based on the continuously differentiable friction model to ensure that the control input is continuously and bounded. In the design of the controller, the unfavorable effects of unknown parameters in the electromagnetic actuator are eliminated by constructing a parameter adaptive law. Meanwhile, in order to improve the tracking accuracy of the electromagnetic actuator, a nonlinear robust control law is designed to ensure the robustness of the controller. The stability analysis by Lyapunov function shows that the asymptotic tracking effect can be obtained when only parameter uncertainty exists in the closed-loop system of the electromagnetic actuator, and the consistent bounded stability can be ensured when the system also exists uncertain nonlinearity. Extensive comparative results verify the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This article realizes an adaptive finite‐time sampled‐data output‐feedback stabilization for a class of fractional‐order nonlinear systems with unmodeled dynamics and unavailable states. K‐filters are constructed to estimate unavailable states, a dynamic signal is introduced to handle unmodeled dynamics and neural networks were used to approximate uncertain nonlinearities existed in stabilizer construction. With the help of backstepping technique, an adaptive sampled‐data output‐feedback stabilizer is exported, and such stabilizer with allowable design parameters and sampling period can render the corresponding closed‐loop system reaches practically finite‐time stable, which can be demonstrated by means of selected Lyapunov function candidates. In the end, two simulations with a numerical and an engineering examples are presented to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time adaptive event-triggered control problem for a class of nonlinear multiagent systems with full-state constraints. The problem of full-state constraints is solved for multiagent systems by utilising a one-to-one nonlinear mapping. The design conditions of the controllers are more easily satisfied than the existing barrier Lyapunov functions strategy. A fixed-time adaptive control scheme is proposed for unconstrained systems that are converted from constrained systems. In contract with the existing finite-time control schemes that rely on the initial states, this restriction condition can be removed in this paper. An improved event-triggered mechanism with an adaptable switching threshold is designed to provide flexible switching between fixed threshold and variable threshold policies, resulting in reduced execution and sampling. It is proved via the Lyapunov stability method that all signals of the closed-loop systems are semi-global practical fixed-time stable. Finally, the effectiveness of the proposed control strategy is verified by some simulation results. [ABSTRACT FROM AUTHOR]

In this research, the adaptive event-triggered neural network controller design problem is investigated for a class of state-constrained pure-feedback fractional-order nonlinear systems (FONSs) with external disturbances, unknown actuator saturation, and input delay. An auxiliary compensation function based on the integral function of the input signal is presented to handle input delay. The barrier Lyapunov function (BLF) is utilized to deal with state constraints, and the event-triggered strategy is applied to overcome the communication burden from the limited communication resources. By the utilization of a backstepping scheme and radial basis function neural network, an adaptive event-triggered neural state-feedback stabilization controller is constructed, in which the fractional-order dynamic surface filters are employed to reduce the computational burden from the recursive procedure. It is proven that with the fractional-order Lyapunov analysis, all the solutions of the closed-loop system are bounded, and the tracking error can converge to a small interval around the zero, while the state constraint is satisfied and the Zeno behavior can be strictly ruled out. Two examples are finally given to show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this paper, a Lyapunov-based design method for event-triggered control with self-triggered sampling is proposed. In the proposed method, update of the control input and sampling of the state are individually determined based on two kinds of upper bounds of the Lyapunov function. By the proposed method, a non-monotonic Lyapunov function can be constructed. By non-monotonic decrease of a Lyapunov function, the number of communications can be reduced. In the neighborhood of the origin, we consider a control method such that the state stays in a certain set. As a result, it is guaranteed that the closed-loop system is uniformly ultimately bounded. Through a numerical example, we demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This article investigates an adaptive fast finite‐time tracking control problem for a class of uncertain nonlinear systems with output hysteresis. The idea of output hysteresis compensation is skillfully extended to adaptive design by employing a hysteresis inverse transformation and barrier Lyapunov function. The backstepping technique is adopted to establish the fast finite‐time control strategy, which can realize finite‐time convergence of the closed‐loop system. A rigorous theoretical analysis is performed to illustrate that the signals in the closed‐loop systems are bounded in finite time, and the tracking error can converge into a compact set with sufficient accuracy. Finally, a numerical simulation is given to confirm the effectiveness of the presented strategy. [ABSTRACT FROM AUTHOR]

In this paper, stability analysis and stabilization control of discrete-time impulsive switched time-delay systems with all unstable subsystems are discussed. By utilizing a switching time-varying Lyapunov-Krasovskii functional and the mode-dependent interval dwell-time switching rule, we derive some more general stability theorems for the considered time-delay system with all subsystems being unstable. Moreover, we design a time-varying state feedback controller to ensure the stabilization of the resulting closed-loop system. Eventually, the theoretical findings are demonstrated utilizing numerical examples. [ABSTRACT FROM AUTHOR]

This paper addresses the $ H_{\infty } $ H ∞ static output feedback control of discrete-time semi-Markov jump systems (S-MJSs) with mismatched modes in finite-time horizon. Different from the existing relevant work, transient dynamics in short-time is focused instead of the Lyapunov stability over infinite-time horizon. Considering the immeasurable system states, output control strategy is developed subject to mismatched modes. Firstly, S-MJSs in closed-loop form are established by designing a hidden mode-dependent output feedback controller. Based on the closed-loop system (CLS), new Lyapunov function relies on both system mode and elapsed-time are created, according to which fresh analysis criteria ensuring FTB and $ H_{\infty } $ H ∞ FTB are derived. To circumvent the non-convex difficulty arising in controller design, a separation technique is introduced such that the design prerequisites are obtained from the above performance analysis conditions. Finally, the boost converter circuit and a numerical example are borrowed to test the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

This study presents a systematic methodology for developing a stabilizing controller for a general hybrid systems model. The approach is based on utilizing the small‐gain theorem as a means of constructing the Lyapunov function and analyzing the input–output stability of the subsystems in the feedback loop. By considering the control system in a closed‐loop configuration with the hybrid system, the small‐gain theorem can be applied. In this scheme, a dynamic control system is proposed that satisfies the closed‐loop stability conditions. This method applies to various hybrid systems' applications due to its generality. To demonstrate the effectiveness and performance of the proposed control approach, two simulation examples, including a linear hybrid system and a bipedal walking robot, are examined. [ABSTRACT FROM AUTHOR]

Stability of a switching system, which comes to existence when stabilizing a chain of two integrators by a feedback in the form of nested saturators, is studied. The use of the feedback in the form of nested saturators makes it possible to take into account boundedness of the control resource and to ensure the fulfillment of the phase constraint on the velocity of approaching the equilibrium state, which is especially important in the case of large initial deviations. A Lyapunov function is constructed by means of which global asymptotic stability of the closed-loop system is proved for any positive feedback coefficients. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the adaptive tracking control in a prescribed‐time for a class of uncertain nonlinear systems with a full state constraint. Firstly, in order to restrict the state constraints, the barrier Lyapunov function is employed and parameter uncertainty is handled based on linear‐in‐the‐parameters (LIP). A prescribed‐time adaptive controller is then designed by backstepping to ensure that the closed‐loop system can reach zero in a prescribed finite time under any given initial conditions. The prescribed‐time is independent of the design of the parameters. Finally, two simulation examples verify the effectiveness and usability 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 paper is concerned with developing an adaptive method on the basis of a nonlinear disturbance observer (NDO) in order to control a switched nonlinear system in the presence of unknown functions and external disturbances, and under arbitrary switching signals. The proposed approach employs an adaptive backstepping technique, NDO, a fuzzy logic system (FLS), and the particle swarm optimization (PSO) algorithm. Based on a common Lyapunov function (CLF), the adaptive backstepping technique is used to design a nonlinear state-feedback controller. Also, NDO and FLS are stated to estimate the disturbances and the unknown nonlinear functions, respectively. In addition, to improve the performance of the closed-loop system, the PSO algorithm is used to optimize the controller parameters. Finally, simulation examples are taken into account to demonstrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive output feedback aperiodic sampling control for a class of switched nonlinear systems under asynchronous switching. A novel model-dependent dynamic aperiodic sampling rule is proposed to solve problems of potential asynchronous lengthening and transmission overburdening. Blending with the sampling information, the time-schedule Lyapunov functions (TSLFs) are recreated to analyze the stability in different control situations. Meanwhile, the switched time-schedule observers and adaptive laws are designed to deal with the estimation problem of unknown system states and nonlinear function. The obtained switched sampling controllers and switching rule guarantee the closed-loop system to be uniformly ultimately bounded. Compared with the existing results, the asynchronous time in this paper does not change with the increasing sampling period. Depending on the sampled data, the time-varying observers and adaptive laws make the application of the given dynamic output feedback control design more extensive and flexible. Finally, the effectiveness of the proposed method is demonstrated by a ship steering system and a mass-spring-damper system. [ABSTRACT FROM AUTHOR]

This paper investigates the output-feedback control for discrete-time switched linear systems. By extending the methodology of switched Lyapunov functions (SLF), a non-monotonic SLF approach is proposed to analyse the stability of the resulting closed-loop system under arbitrary switching. This approach relaxes the requirement of monotonicity to conventional SLF approach, and hence it will lead to less conservative results. First, the stability of discrete-time switched systems is analysed, and then an output-feedback controller design method is proposed based on the obtained stability results. The controller design problem is formulated in term of LMIs, and relaxed variables are introduced in the synthesis conditions to improve the design freedom. Finally, three examples are used to verify the effectiveness and application potential of the proposed methods. [ABSTRACT FROM AUTHOR]

This article presents an observer‐controller structure for nonlinear systems with external disturbances, uncertainties and performance constraints. Firstly, considering the unmeasurable state issue, a sliding mode observer is designed to estimate the unknown state. Then an integral barrier Lyapunov function (iBLF) is established to ensure that the observer estimation errors are held within the performance constraints. With the objective of utilizing iBLF, an equivalent output injection technique is adopted. Combining with backstepping technique, an observer‐controller scheme is given. The controller can hold all signals in the closed‐loop system bounded with the help of iBLF, and the tracking error will converge to a bounded tight set without violating the corresponding constraints. The effectiveness of the proposed strategy is clarified via a vessel heading control example. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of neuroadaptive fixed-time control for full-state constrained strict-feedback nonlinear systems subject to actuator faults. A fixed-time control strategy combined with barrier Lyapunov functions and neuroadaptive backstepping is proposed, and a neural network is employed to approximate the packaged unknown nonlinear terms and nonlinear actuator faults. By constructing barrier Lyapunov functions, it can be ensured that none of the strict-feedback systems' states will transgress their constraint bounds. Additionally, a fixed-time controller is designed such that all the signals in the closed-loop system are bounded, and the output is driven to track the reference signal to a small neighborhood within a fixed time. The benefits and feasibility of the proposed control method are also confirmed by simulations. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network controller is proposed for vibration suppression of a multisectional riser system with unknown boundary disturbance, time-varying asymmetric output constraint, and input nonlinearity. The considered riser system is composed of a continuous connection of several different pipes, and its dynamic models are represented by a set of multiple continuously connected partial differential equations (PDEs) and an ordinary differential equation (ODE) at the top boundary. Considering input nonlinearity, external disturbance, and system-uncertainty, radial basis function (RBF) neural networks are adopted to eliminate the effect of these uncertain terms. Besides, a barrier Lyapunov function is employed to guarantee the restrictions. With the proposed boundary control, the stability of the closed-loop system is proved and simulations are given to illustrate the well performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]

For the nonlinear continuous‐time switched system with input saturation under asynchronous switching action, the problem of actuator failure is investigated in the presence of external disturbances. Firstly, a sufficient condition for the robust exponential stabilization of system is obtained by using the minimum dwell‐time method combining the piecewise Lyapunov function method. Secondly, a sufficient condition for the tolerance disturbances of the system is obtained by using the similar approach. Thirdly, the weighted L2$$ {L}_2 $$‐gain is analysed. Then, we maximize the estimation of the domain of attraction for the closed‐loop system by taking the shape of a set into consideration. Furthermore, the fault‐tolerant state feedback controllers are designed, which aim at enlarging the estimation of domain of attraction for the closed‐loop system and obtaining the maximum value of tolerance disturbance and the minimum upper bound on the weighted L2$$ {L}_2 $$‐gain. Last but not least, we give a numerical example to verify the effectiveness of the proposed approach and compare the state responses curve under the action of the fault‐tolerant and the standard controller. [ABSTRACT FROM AUTHOR]

This paper is concerned with practical fixed-time (FT) stabilization problem of discretetime impulsive switched port-controlled Hamiltonian systems (DISPCH). First, starting with discrete-time port-controlled Hamiltonian systems, a novel controller is presented to achieve practical FT stability of the obtained closed-loop system. Moreover, in order to well handle the abrupt changes at switch moments in practical switched systems, another novel controller is presented in terms of positive-order Lyapunov functions approach and range dwell time method to make discrete-time impulsive switched port-controlled Hamiltonian system practical FT stable. Ultimately, the validity of proposed methods is illustrated by simulations. [ABSTRACT FROM AUTHOR]

In this paper, the tracking control problem of the state constrained stochastic nonlinear systems with unknown control direction is studied, and a novel adaptive prescribed performance control (PPC) approach is developed with the help of the multi-dimensional Taylor network (MTN). Firstly, a performance function is introduced into the first step of backstepping to ensure transient performance under state constraints. Secondly, the tangent time-varying barrier Lyapunov functions (tan-TVBLFs) are constructed to prevent all states from violating the given time-varying boundary. Thirdly, the MTNs are employed to estimate the unknown nonlinearity in the process of controller design, and a new adaptive PPC strategy is designed. Then, the Lyapunov stability theorem is used to prove that the closed-loop system is semi-global uniformly ultimately bounded (SGUUB) in probability, and the tracking error can be kept in an adjustable small neighborhood of the origin. Finally, the effectiveness of the proposed scheme is verified by the simulation of a numerical example and an actual control system. [ABSTRACT FROM AUTHOR]

In this study, a new robust predictive control technique is investigated for uncertain fractional-order descriptor systems. Using the properties of fractional calculus and the construction of an appropriate Lyapunov function, the sufficient conditions to guarantee the existence of a robust predictive controller are given by minimizing the worst-case optimization problem. The new robust predictive controller is presented by using Shur complement, linear matrix inequality toolbox and cone-complement linear algorithm, and it is proved that the feasible solution of the optimization problem at the initial time can guarantee the admissibility of fractional-order descriptor closed-loop system. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a high-order adaptive dynamic surface control (HOADSC) method by using the fully actuated system-based approach for uncertain high-order strict-feedback systems (SFSs) with an output constraint. Each subsystem of the SFSs is a high-order system with a full actuation structure. In contrast to the traditional first-order state space method, the proposed control method directly treats each high-order subsystem as a whole without transforming it into a first-order system, which is a concise and efficient treatment. By introducing a series of first-order low-pass filters in each step of the design, the high-order derivatives of the virtual control law are obtained, and the complex and multiple derivation operations are transformed into simple algebraic operations. The Barrier Lyapunov function is combined to ensure that the output of the system satisfies the given constraints. And the Lyapunov theory is used to prove that all signals in the closed-loop system are ultimately uniformly bounded, and its output can accurately track the desired reference signal without violating the given constraints. The effectiveness of the proposed control method is verified by simulations of an actual manipulator system. [ABSTRACT FROM AUTHOR]