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This article addresses the attitude manoeuvre control without unwinding based on variable gain (i.e. adaptive) super-twisting algorithm (STA) and the modified Rodrigues parameters (MRPs). First, a selection logic for the MRP set and its shadow set is designed in order to construct an attitude control system model for rigid spacecraft. Second, a nonlinear sliding function is presented, and on the sliding phase, finite-time convergence and anti-unwinding performance are guaranteed. As a result, a first-order sliding mode controller is created to ensure finite-time convergence and anti-unwinding performance on the reaching phase. To alleviate the chattering and improve convergence speed, an adaptive STA is presented, and the finite-time convergence and anti-unwinding performance are guaranteed on the reaching phase. Furthermore, the adaptive gains are designed based on dual-layer adaptive law, which allows the gains to be increased and decreased as appropriate. It is further demonstrated that when the controller gains are time-varying according to adaptive law, the convergence property and anti-unwinding performance are guaranteed. Numerical simulations are carried out to illustrate the closed-loop system's performance under the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper provides a new sliding mode control (SMC) approach, by which both the nominal and robust stability associated with a trajectory tracking problem for an uncertain robot manipulator are achieved. More precisely, the new control law consists of linear and nonlinear functions of tracking errors, in which the former is for the nominal stability and the latter is to ensure the robust stability of the resulting closed‐loop systems. The nonlinear function can be interpreted as an extended version of conventional SMC approach, and the reaching phase corresponding to a pregiven sliding surface is shown to be completed in a finite time; the tracking errors arrive at the sliding surface in a finite time and do not deviate from it after the arrival. In the sliding phase, the tracking errors are also ensured to converge to the origin. Finally, some simulation results are given to demonstrate both the theoretical validity and practical effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

It is well known that unknown modeling errors cannot be avoided in practice. Such unmeasured uncertainties are usually denoted as unknown nonlinear functions that exist in every channel of the system equation. This paper aims to develop an output feedback adaptive control scheme by constructing state estimation filters to address such unknown modeling errors. In the controller design, these uncertainties caused by modeling errors will be accumulated to the last step for compensation. Unknown parameters existing in the upper bound functions of unknown nonlinear functions and system parameters are estimated synchronously based on tuning function approaches. It is shown that the designed output feedback controller can ensure the stability and tracking performance of the closed‐loop system, and the transient performance in terms of a truncated norm is also established. [ABSTRACT FROM AUTHOR]

The paper expounds the question of echo state network‐based fixed‐time adaptive tracking control for a type of multi‐input multi‐output nonlinear strict‐feedback system with input delayed saturation. Based on the feature that echo state network can obtain better estimation performance at lower computational cost, it is utilized to approximate the unknown nonlinear function in the controller design course. By constructing an auxiliary system, a time‐delay system with input saturation is eliminated. Moreover, command filtering techniques can be applied to avoid the "complexity explosion" problem in the backstepping method. Then, the closed‐loop system is attested to be semi‐global practical fixed‐time stable. In the end, a simulation shows the valid scheme. [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]

Summary: In this paper, the adaptive control is investigated by switching linear controllers for a class of nonlinear parameterized systems with multiple unknown control directions. Parametric uncertainties entering the state equations nonlinearly can be fast time‐varying or jumping at unknown time instants, the bounds of the parametric uncertainties are not required to know a priori and the multiple control directions are unknown. Our proposed adaptive controller is a switching type controller. First, sufficient conditions for designing an adaptive stabilizer are derived. Then, a switching‐type adaptive controller is designed, in which a linear controller with two undetermined design parameters to be tuned is recursively designed by adding a power integrator, and a switching mechanism is proposed to tune these parameters online for compensating the multiple unknown control directions and the unknown bounds of the parametric uncertainties. The undetermined design parameters are based on the upper bound estimation of the unknown parameters existing not only in the nonlinear functions but also in the control directions functions. The proposed adaptive controller globally asymptotically stabilizes the system in the sense that, for any initial conditions, the state converges to the origin while all the signals of the closed‐loop system are bounded. Finally, an example is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article studies the adaptive state‐feedback control problem of output‐constrained stochastic high‐order nonlinear systems with stochastic integral input‐to‐state stability (SiISS) inverse dynamics. A key nonlinear transformation function is constructed to convert the original output‐constrained stochastic nonlinear system into an equivalent form without any output constraint. By subtly using the SiISS small‐gain condition and fully extracting the characteristics of system nonlinearities, two new control design and analysis methods are developed to guarantee that the closed‐loop system has an almost surely unique solution, all the closed‐loop signals are bounded almost surely, and the equilibrium point is stable in probability without the violation of output constraint. A simulation result is provided to show the effectiveness of this control method. [ABSTRACT FROM AUTHOR]

This article investigates the adaptive fault‐tolerant tracking control problem for nonlinear systems with prescribed performance and input dead‐zone. First, a new composite variable is constructed by using the characteristics of actuator fault and input dead‐zone for the modeling purpose. Second, an adaptive neural network observer is designed to estimate the system states in the presence of inaccurate feedback information. Third, the proposed control strategy effectively counteracts the effects of sensor failure and unknown nonlinear functions, which makes the tracking error confined within the performance boundary and all the signals of the closed‐loop system semi‐globally uniformly ultimately bounded. Finally, an application oriented example is provided to demonstrate the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

This paper focuses on addressing the leaderless consensus problem in general nonlinear multi-agent systems (MAS) with bounded disturbances and directed topology. Neural networks are utilized for estimating unknown nonlinear functions. By designing an adaptive observer, under the presence of bounded disturbances and unknown nonlinearities, a fully distributed leaderless consensus protocol is presented to make the closed-loop systems achieve consensus. By using sigmoid basis functions for disturbance compensation, the sensitivity of the controller to disturbances can be reduced, enabling the system to work more stably and robustly in the presence of bounded disturbances. Finally, the results are verified through a numerical example. [ABSTRACT FROM AUTHOR]

This paper proposes a finite-time dynamic surface control (FTDSC) scheme using a novel nonlinear filter for nonlinear systems with input saturation and output constraint. The FTDSC scheme featured by nonlinear filter is presented to overcome the drawback of the traditional DSC scheme using first-order linear filter. The barrier Lyapunov function (BLF) is used to handling output constraint and the disadvantage of conventional input saturation problem is solved through introducing a nonlinear smooth function. In order to deal with the impact of external disturbance, the nonlinear disturbance observer (NDO) is introduced to estimate the disturbance. It is proved that All signals are semi-globally uniformly ultimately bounded (SGUUB) in the closed loop system. Moreover, the tracking error can convergences to a neighborhood of zero through the proper designed parameters. Comparison studies are conducted to verify that the proposed FTDSC scheme comparing with the traditional DSC scheme has the more accurate tracking capability. [ABSTRACT FROM AUTHOR]

This paper investigates a parameter-dependent (PD) functional observer-based finite-time adaptive memory integral sliding mode controller (ISMC) scheme for continuous linear parameter varying (LPV) systems with unknown nonlinear functions. First, a novel functional observer including parameters and parameter rates is proposed, and the observer gains can be directly designed. Different from the previous studies, a memory ISMC scheme with a time-varying delay through the functional observer is used for the first time in LPV systems to improve the performance. Moreover, to fully adopt the model characteristics of LPV systems and the functional observer, an integral PD surface function that contains PD input matrices and a memory parameter is established simultaneously. And then, an adaptive compensator is deduced for eliminating the influence of unknown nonlinear functions, so that the designed memory ISMC can make closed-loop LPV systems stable within a preset finite time. Furthermore, stability criteria with less conservatism for the LPV systems are proposed by memory Lyapunov–Krasovskii functional with measurable parameters. These criteria can guarantee LPV systems are finite-time boundedness with H ∞ performance (FTB- H ∞ ) on the sliding stage, arriving stage and entire finite-time region. Finally, a riderless bicycle is given to display the validity and superiority of the developed approach. [ABSTRACT FROM AUTHOR]

This article studies the fault‐tolerant control for linear systems with disturbances through sliding mode scheme. Compared with the existing results, by means of a nonlinear sliding function with an exponential term, a reduced‐order sliding motion can be obtained while the reaching phase of conventional sliding mode control is eliminated. On the other side, with the help of an exponentially decaying barrier Lyapunov functions, a continuous sliding mode algorithm is established. Within the proposed framework, the trajectories of closed‐loop systems can be forced to a prescribed practical sliding surface, and then, both the transient as well as steady‐state performances are guaranteed despite the occurrence of abrupt actuator faults. Under the Lyapunov direct method, a sufficient condition is derived such that the sliding motion is uniformly bounded with time‐varying performance constraints. Finally, an electric amplidyne system is considered to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

This article investigates the robust adaptive guaranteed cost tracking (RAGCT) control problems for two class of high‐order nonlinear systems with time‐varying uncertainties. A special nonlinear function, named as the local smooth nonlinear function, is introduced, which is crucial for our controller design. Then, using the function and the recently proposed high‐order fully actuated (HOFA) system approaches, a novel RAGCT controller for HOFA system with time‐varying uncertainty is designed, which guarantees the global boundedness of the closed‐loop system within finite time. Then, based on the result about the above HOFA system, the RAGCT controller for a class of strict‐feedback systems with high‐order form and time‐varying uncertainties is obtained, which also guarantees that the closed‐loop system is globally bounded within finite time. Most importantly, two designed controllers can preset arbitrarily the upper bounds of cost functions characterizing the tracking performance independent of the system initial values and the time‐varying uncertainties, which optimizes greatly the system tracking performance, and is difficult to achieve by using existing guaranteed cost control methods. Finally, two simulation examples including one‐link manipulator are provided to show the effect and the practicability of our proposed control method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stabilization of a geometric nonlinear beam with a nonlinear delay term in boundary control. The well-posedness of the closed-loop system where a nonlinear damping and a nonlinear delay damping are applied at the boundary is examined using the Faedo–Galerkin approximation method. Constructing a novel energy-like function to handle the nonlinear delay, the explicit exponential decay rate of the closed-loop system is established with a generalized Gronwall-type integral inequality and the integral-type multiplier method. [ABSTRACT FROM AUTHOR]

This article investigates the tracking control problem of adaptive fault‐tolerant control (AFTC) for a class of strict‐feedback nonlinear systems with actuator/sensor faults and unmatched disturbances. The sensor/actuator faults include bias, drift, loss of accuracy, and loss of effectiveness. Our main control objective is to investigate an adaptive fault‐tolerant control algorithm with the help of tuning functions, adaptive control technology, fuzzy logic systems (FLSs) and so on. For unknown nonlinear functions and fault items, FLSs and adaptive control technology are utilized to approximate them. Furthermore, over‐parameterization problem existed in the backstepping‐based controller design is solved by introducing the tuning functions. Theoretical analysis proves that our proposed AFTC scheme can ensure that all the signals in the closed‐loop system are bounded and the tracking error remains within a small range of zero even if a series of faults mentioned above occur. In the end, the simulation results show that the presented AFTC strategy is feasible and effective. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the output-feedback tracking problem of random high-order nonlinear systems. Compared with most of the existing results where only a wiener process is considered, we consider systems with a second-order moment process. A new high-gain homogeneous domination design technique is developed to deal with the coupling terms between nonlinear functions and the second-order moment process. A new output tracking controller is designed to ensure that the mean-square of the tracking error can be made arbitrarily small while all the states of the closed-loop system are bounded in probability. Finally, a simulation example is given to illustrate the effectiveness of our control scheme. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of fixed-time consensus tracking for a class of nonlinear multi-agent systems subject to unknown disturbances. Firstly, a modified fixed-time disturbance observer is devised to estimate the unknown mismatched disturbance. Secondly, a distributed fixed-time neural network control protocol is designed, in which neural network is employed to approximate the uncertain nonlinear function. Simultaneously, the technique of command filter is applied to fixed-time control, which circumvents the "explosion of complexity" problem. Under the proposed control strategy, all agents are enable to track the desired trajectory in fixed-time, and the consensus tracking error and disturbance estimation error converge to an arbitrarily small neighborhood of the origin, meanwhile, all signals in the closed-loop system remain bounded. Finally, a simulation example is provided to validate the effectiveness of the presented design method. [ABSTRACT FROM AUTHOR]

This article considers the problem of finite-time command-filtered control for switched nonlinear systems with input quantization and output constraints. The unmeasurable state is estimated by designing a switched state observer. During the design process, to overcome the chattering problem effectively, the hysteresis quantization is designed as two bounded nonlinear functions. Furthermore, in order to restrict the output to an expected range, the barrier Lyapunov function approach is introduced. The "explosion of complexity" and the error compensation problems in the backstepping design are solved by using a finite-time command-filtered approach. A first-order Levant differentiator is used instead of the general command filter in this paper, which can not only filter the intermediate signals accurately to get the differential signals, but also ensure the finite-time stability of the filter. Stability of the closed-loop system in the sense of semi-global practical finite-time stability (SGPFS) is proved by exploring a multiple Lyapunov functions approach. Finally, a simulation example is provided to verify the validity of the presented control method. [ABSTRACT FROM AUTHOR]

Summary: In this article, the issues of asymmetric input saturation and asymmetric time‐varying state constraints are integrated into nonlinear systems for the first time, and a new adaptive tracking control strategy is proposed based on the multi‐dimensional Taylor network (MTN) method. Firstly, by introducing a continuous auxiliary function, the input saturation is transformed into a smooth model with bounded error. Secondly, MTNs are employed to estimate nonlinear functions, asymmetric barrier Lyapunov functions (ABLFs) are constructed to make all state variables meet asymmetric constraints, and then a novel control scheme with simple structure is developed via backstepping. Thirdly, according to the Lyapunov stability theory, it is proved that all signals in the closed‐loop systems are bounded, and all state variables do not violate the constraints. Finally, three examples show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to design an adaptive controller for uncertain strict-feedback stochastic nonlinear systems with dead zone and input saturation. A barrier Lyapunov function in asymmetric forms is constructed which can relax the requirements for initial conditions. To simplify the design process of the controller, a dead zone-based model of saturation is implemented and neural networks are used to approximate uncertain nonlinear functions and to construct an observer to cope with difficulties raised by the presence of immeasurable state variables. By applying the backstepping technique, a smooth tracking controller with adaptive law is proposed to ensure all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Furthermore, the tracking error can converge to a small neighbourhood of the origin. The effectiveness of the proposed scheme is presented by simulations on a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network (NN) finite-time command filter controller for a class of non-strict feedback stochastic non-linear systems has been investigated. Using error compensation signals and NNs, a command filter controller is presented which guarantees that all the signals of the closed-loop system are practical finite-time stable and the output signal tracks the given reference signal under the bounded error. In the design procedure, NNs are employed to approximate unknown non-linear functions, which contain all the state variables of the whole system, in order to avoid excessive and burdensome computations and ensure that the backstepping method works normally for non-strict feedback systems. Meanwhile, the ''explosion of complexity'' problem caused by the backstepping method is avoided using the command filter approach. The control scheme not only resolves the ''explosion of complexity'' problem but also eliminates the filtering error in finite-time. Finally, the simulation results are given to prove the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper investigates the trajectory tracking problem of uncertain robot manipulators with input saturation and output constraints. Uncertainty and disturbance estimator (UDE) is used to tackle the model uncertainties and external disturbances. Different from most existing methods, UDE only needs the bandwidth of the unknown plant model for design, which makes it easy to be implemented. Nonlinear state-dependent function is employed to cope with output constraints and a second order auxiliary system is constructed to solve the input saturation. Finally, an UDE-based tracking controller is proposed based on the backstepping method. With the proposed control scheme, the input saturation and the output constraints are not violated, and all signals in the closed-loop system are bounded. The comparative simulation results of a two-link robot manipulator are utilized to validate the effectiveness and superiority of the proposed control method. [ABSTRACT FROM AUTHOR]

In this article, an adaptive neural fault-tolerant control (FTC) scheme is proposed for the medium-scale unmanned autonomous helicopter subject to external disturbance, actuator fault, and input saturation. Multiple approximators are constructed to handle the unknown terms and promote the control design. The nonlinear coupled function terms are approximated by virtue of the radial basis function neural networks. The unknown disturbance is tackled by the developed disturbance observer. Meanwhile, two auxiliary systems are introduced to handle the actuator fault and input saturation, respectively. In the framework of the backstepping method, a multiapproximator-based adaptive FTC strategy is presented, which assures the boundedness of all closed-loop system signals. Simulation results are presented to validate the availability of the designed controller. [ABSTRACT FROM AUTHOR]

In this study, we study the performance guaranteed tracking problem for the uncertain MEMS gyroscope dynamics. A fixed-time nonlinear function and the transformed error are introduced to establish the preset time control property for the closed-loop system. Then, a funnel sliding mode controller is presented to stabilize the system. To compensate for the uncertainty and external disturbance, a novel lumped disturbance observer is proposed. Different from most existing compensation control results for the MEMS gyroscope with antidisturbance that can only achieve uniformly ultimately bounded, the proposed control scheme is able to ensure that the system errors converge to the preassigned compact sets within a given time rather than infinite time and the system outputs track the desired command asymptotically. Simulation verification also confirms the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This article focuses on an adaptive neural network (NN) finite-time prescribed performance control problem for nonstrict-feedback nonlinear systems subject to full-state constraints. Specifically, a finite-time performance function is employed, which can guarantee that the tracking error converges to a prescribed region within a finite-time. Neural networks (NNs) are used to approximate the unknown nonlinear function. The unmeasurable states are estimated via constructing a state observer. By using the dynamic surface control (DSC) technique, the complexity problem has been avoided in traditional backstepping control. In order to satisfy the state constraint condition, the barrier Lyapunov function (BLF) is incorporated in the process of backstepping. The developed adaptive finite-time NN backstepping control strategy can make that the closed-loop system is semiglobally practical finite-time stability (SGPFS). Meanwhile, all states can be guaranteed to remain in the constrained space. Simulation results demonstrate the validity of the control method. [ABSTRACT FROM AUTHOR]

A magnetic screw motor integrated by a magnetic screw and a permanent magnet synchronous motor offers high thrust density without friction and loss. However, its internal structure is complicated, resulting in difficulty to control its linear displacement to track the given signal in time and even getting out of control due to interference. In this article, to improve the tracking performance and anti-interference performance of the system, an active disturbance rejection controller (ADRC) is adopted to improve the current loop and makes up for nonlinear regional control in the closed-loop control system. To ensure the stability of the proposed scheme in the entire operation range, the design and the stability analysis are carried out in the continuous-time domain. The novel control algorithm enhances the flexibility of the magnetic screw motor by optimizing the nonlinear function in ADRC. In addition, to highlight the high-performance control of the ADRC for the magnetic screw motor, sliding mode control and PI control are added to compare with it. Finally, the reciprocating motion control is added to show the diversity of the functions of the magnetic screw motor, which verifies the effectiveness and feasibility of the control algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the adaptive fixed-time neural tracking dynamic surface control (DSC) for high-order nonlinear switched systems. The uncertain nonlinear functions are approximated by neural networks (NNs). And the obstacles of high-order terms are eliminated by the adding a power integrator. A novel fixed-time convergence filter is proposed to refrain from the problem of repeated differentiation of virtual controllers. On the basis of the backstepping control algorithm and the common Lyapunov function (CLF) method, a novel adaptive fixed-time neural DSC strategy is developed. It is proven that the states in the closed-loop system are bounded and the tracking error converges to an area of zero in fixed time. The simulation experiments further verify the feasibility of the mentioned method. [ABSTRACT FROM AUTHOR]

In this article, a command filtering‐based adaptive event‐triggered neural network control scheme is proposed for a class of uncertain switched nonlinear systems with unknown control coefficient and input saturation. First, radial basis function neural networks are used as function approximators to estimate unknown nonlinear functions. Then, an event‐triggering mechanism based on the tracking error is introduced to avoid the over‐consumption of communication resources. Furthermore, command filters are employed to solve the problem of complexity explosion that exists in conventional backstepping control design, and the error compensation signals are designed to reduce the errors caused by the filters. Considering that the unknown control gain and input saturation exist in many practical applications, a Nussbaum‐type function is thus introduced into the controller design to address these challenging issues. Finally, stability of the closed‐loop system is strictly proven under a standard Lyapunov stability analysis framework. The effectiveness of the proposed control scheme is illustrated by a simulation example. [ABSTRACT FROM AUTHOR]

Summary: This article studies the bipartite containment control problem for a class of fractional order nonlinear multi‐agent systems in the presence of arbitrary switchings and unmeasured states. Under the framework of Lyapunov function theory, this article proposes an adaptive neural network dynamic surface controller, in which dynamic surface control technology can avoid "explosion of complexity" and obtain fractional derivatives for virtual control functions continuously. Radial basis function neural networks are used to approximate the unknown nonlinear functions and an observer is designed to obtain the unmeasured states. The proposed distributed protocol can ensure all the signals remain semi‐global uniformly ultimately bounded in the closed‐loop system and all followers can converge to the convex hull containing each leader's trajectory as well as its opposite trajectory different in sign. Example and simulation results confirm the feasibility of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper considers the issue of adaptive neural decentralised tracking control for a class of output-constraint switched interconnected nonlinear systems with unknown backlash-like hysteresis control input. First, neural networks (NNs) are applied to approximate unknown nonlinear functions, and an NNs switched state observer is designed to estimate unmeasured system states. Then, the dynamic surface control technique is used to avoid the influence of explosion of complexity. In addition, the problem of output constraints is solved by introducing the barrier Lyapunov functions. Based on the Lyapunov stability theory, all signals in the switched closed-loop system can be verified to be uniformly ultimately bounded under the proposed control method. Moreover, the system output can track the target trajectory well within a small bounded error. Finally, a numerical simulation result is given to illustrate the effectiveness of the adaptive decentralised control scheme. [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 article discusses adaptive preassigned finite-time stability of nonlinear systems with unknown time-varying powers and full-state constraints. By introducing a concept of practical preassigned finite-time stable, constructing finite-time performance functions and nonlinear transformed functions, and integrating high-power and low-power terms into dynamic surface control design, an adaptive state-feedback controller is designed to eliminate the effects of unknown powers and serious unknown parameters in system dynamics, and decrease the computational burden in previous design methods. It is proved that all the signals of the closed-loop system are bounded, asymmetric time-varying full-state constraints are not transgressed, and systemstates converge to a prescribed arbitrarily small region around zero after a preassigned settling time. Two simulation examples show the effectiveness and advantages of this control scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of output feedback neural network (NN) learning tracking control for nonlinear strict feedback systems subject to prescribed performance and input dead-zone constraints. First, an NN is utilized to approximate the unknown nonlinear functions, then a state observer is developed to estimate the unmeasurable states. Second, based on the command filter method, an output feedback NN learning backstepping control algorithm is established. Third, a prescribed performance function is employed to ensure the transient performance of the closed-loop systems and forces the tracking error to fall within the prescribed performance boundary. It is rigorously proved mathematically that all the signals in the closed-loop systems are semi-globally uniformly ultimately bounded and the tracking error can converge to an arbitrarily small neighborhood of the origin. Finally, a numerical example and an application example of the electromechanical system are given to show effectiveness of the acquired control algorithm. • Constructing neural network-based state observer for nonlinear strict feedback systems with unmeasurable states. • The desired transient and steady-state tracking control performance are fulfilled, simultaneously. • There is no need to have the known parameter bound of dead-zone or the linear functions outside the deadband. • The controller can overcome the "explosion of complexity" problem and alleviate the online calculation burden. [ABSTRACT FROM AUTHOR]

In this article, a study of the appointed fixed time control problem is presented for the quadrotor unmanned aerial vehicle attitude control system subject to external disturbances. Based on the appointed fixed time sliding mode variable and the adaptive technique, a novel sliding mode observer is first established to estimate the external disturbances within the appointed settling time. Subsequently, an appointed fixed time controller is proposed to track the desired attitude regardless of the initial states. To circumvent the singularity problem of the above controller, a novel improved adaptive sliding mode control law is designed by employing a nonlinear function and a modified sliding mode observer. Moreover, the chattering phenomenon is attenuated by replacing the sign function with hyperbolic tangent function. The appointed practical fixed time stability of the closed-loop attitude control system is proved and analyzed rigorously utilizing the Lyapunov theory. Finally, the effectiveness and fine performance of the proposed schemes are illustrated by numerical simulation results. [ABSTRACT FROM AUTHOR]

In this article, the global stabilization issue is discussed for a class of stochastic continuous time‐delay nonlinear systems suffered from unknown control directions and disturbance. Instead of designing the adaptive law online, a control gain is invoked to adapt for the formidable scene of merely continuous but nonsmooth caused by the low‐order, and to deal with the negative effects of disturbance and multiple unknown nonlinear functions where both input and state time delays are taken into account. Then, by utilizing the homogenous domination approach, a delay‐independent controller is designed with a sign function to compensate for the negative impact generated from disturbance and maintain the global asymptotic stability of the closed‐loop system. Finally, a stochastic simulation is supplied to show the validity of the control scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose the twisting observer for robustly estimating the states of a second-order uncertain system. It is well-known that the twisting algorithm achieves the second-order sliding mode condition using the sign information of both the plant states. When all the states are not available, the twisting algorithm cannot be extended to the estimation problem directly. Hence, our proposed observer approximates this unknown sign term for the non-measurable state in the twisting observer with a delayed output-based switching function. It is shown that under some suitable gain conditions, the observer can guarantee any arbitrary accuracy of estimation error for some appropriate delay parameter which can be varied online. The advantage of this observer is that one needs to control only the delay parameter to achieve the desired steady-state accuracy, unlike the additional nonlinear functions found in the existing observers. The application of this observer to output feedback stabilization is also discussed. The separation principle for the closed-loop system is shown by designing the gains of controller and observer independently. Finally, we illustrate the design and performance of the proposed state observation scheme through a numerical example. [ABSTRACT FROM AUTHOR]

In this study, a prescribed performance adaptive fault tolerant tracking control scheme is presented for a class of nonlinear large‐scale systems with time delay interconnection, dead zone input, and actuator fault. The radial basis function neural networks are used to approximate unknown nonlinear functions. Different from the barrier Lyapunov functions used to achieve the symmetrical prescribed performance, a new error transformation is introduced in this study to achieve the desired asymmetrical prescribed performance. In addition, Nussbaum function is introduced to solve the difficulties caused by dead zone input and actuator fault. Based on the appropriate Lyapunov–Krasovskii functions, the effect of time delay interconnection could be compensated. By using backstepping procedures, an adaptive fault tolerant tracking control approach is developed for the considered large‐scale systems, and the stability of the closed‐loop systems is analyzed by Lyapunov theory. Meanwhile, the prescribed performance of the tracking error could be guaranteed. Finally, the effectiveness of the proposed control approach is illustrated by two simulation examples. [ABSTRACT FROM AUTHOR]

Summary: This article investigates an adaptive neural network (NN) output‐feedback optimal control design problem for active suspension systems (ASSs) with stochastic disturbance. The ASSs under consideration contain the characteristics of spring nonlinear dynamics, unmeasured states, and state constraints. The NNs are developed to approximate the unknown nonlinear functions. Meanwhile, observer‐based output feedback control design method is proposed based on the adaptive backstepping technique. Furthermore, the stability of the closed‐loop system is demonstrated by constructing the barrier Lyapunov function, thus ensuring that the full‐state constraints are not exceeded. In particular, the simulation validations are given for the cases of bump, C‐class, and D‐class road displacements inputs. Finally, the simulation results verify the effectiveness of the studied control strategy. [ABSTRACT FROM AUTHOR]

This paper focuses on the passive fault-tolerant control for an uncertain switched nonlinear random system with multiple interval time-varying delays and intermittent faults in sensors and actuators. There are also nonlinear functions, exogenous disturbances and measurement noise in the system. There are few tries to realize noise-to-state exponentially mean-square stability for switched random nonlinear systems subject to multiple interval time-varying delays allowing various time delay cases. Random differential equations are more common than stochastic differential equations in practice. Thus, the suggested method is more practicable. First, a nonlinear dynamic output feedback fault-tolerant controller is constructed. Next, an augmented closed-loop system can be obtained in the framework of multiple interval time-varying delays. Then piecewise Lyapunov function is utilized to realize stability analysis for the closed-loop system. At last, this feasibility of this approach presented in this study is verified through two examples. [ABSTRACT FROM AUTHOR]

Summary: This article investigated the adaptive backstepping tracking control for a class of pure‐feedback systems with input delay and full‐state constraints. With the help of mean value theorem, the system is transformed into strict‐feedback one. By introducing the Pade approximation method, the effect of input delay was compensated. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. Furthermore, in order to reduce the computational burden by introducing backstepping design technique, dynamic surface control technique was employed. In addition, the number of the adaptive parameters that should be updated online was also reduced. By utilizing the barrier Lyapunov function, the closed‐loop nonlinear system is guaranteed to be semi‐globally ultimately uniformly bounded. Finally, a numerical simulation example is given to show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The topic of adaptive event‐triggered control for multi‐input‐multi‐output nonlinear systems with asymmetric state constraints is considered in this article. First, by introducing unified barrier functions method, the initial system is transformed to a non‐constraints system, which brings that the requirement of feasibility conditions could be eliminated and the constraint functions could be relaxed effectively. Then, an adaptive tracking controller is designed by combining some excellent technology, where command filter is utilized to overcome the explosion of complexity, neural network is introduced to approximate unknown nonlinear function, and event‐triggered mechanism is proposed to save communication resources. The designed control scheme can make the outputs of the system to track the target trajectories within a small bounded error range, all signals in the closed‐loop system are bounded, and all states do not escape the state constraints. Finally, practical and comparative examples are given to verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

This article discusses the problem of event-triggered fixed-time control for a class of stochastic non-triangular structure nonlinear systems. The Mean Value Theorem is employed to transform the stochastic non-triangular structure nonlinear systems into the equivalent systems with affine structure. The unknown nonlinear functions are approximated by the radial basis function neural networks. Combining with the fixed-time stable theory, backstepping technique and event-triggered control technique, a novel event-triggered fixed-time adaptive controller is designed. The results manifest that the whole signals in the closed-loop system are bounded in probability. Meanwhile, the tracking error converges to a small residual set within a fixed-time interval. Two simulations are included to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]