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Summary: This paper investigates the finite‐time robust simultaneous stabilization problem of a set of nonlinear time‐delay systems with general forms and proposes some new simultaneous stabilization results. First, by developing an equivalent form and applying augmented technique, this paper obtains an augmented equivalent form of the original systems. Secondly, based on the equivalent form, we study finite‐time simultaneous stabilization problem and present some new stabilization results by constructing some suitable Lyapunov functionals. Thirdly, using the simultaneous stabilization results obtained, this paper investigates the finite‐time robust simultaneous stabilization problem for the set systems and proposes a delay‐dependent robust simultaneous stabilization result. Finally, the study of an illustrative example shows that the results obtained by this paper work well in the finite‐time robust simultaneous stabilization the set systems. It is shown that, by using the method in this paper, the developed conditions do not contain delay terms, which can avoid solving nonlinear mixed matrix inequalities and reduce effectively computational burden in studying nonlinear time‐delay systems. [ABSTRACT FROM AUTHOR]

This paper considers the equilibrium‐free stability and performance analysis of discrete‐time nonlinear systems. Two types of equilibrium‐free notions are considered. Namely, the universal shifted concept, which considers stability and performance w.r.t. all equilibrium points of the system, and the incremental concept, which considers stability and performance between trajectories of the system. This paper shows how universal shifted stability and performance of discrete‐time systems can be analysed by making use of the time‐difference dynamics. Moreover, the existing results are extended for incremental dissipativity for discrete‐time systems based on dissipativity analysis of the differential dynamics to more general state‐dependent storage functions for less conservative results. Finally, it is shown how both these equilibrium‐free notions can be cast as a convex analysis problem by making use of the linear parameter‐varying framework, which is also demonstrated by means of an example. [ABSTRACT FROM AUTHOR]

This paper proposes a novel solution to the problem of robust ripple‐free hybrid robust regulator ensuring the elimination of intersample ripples. The paper considers a structurally stable regulation approach in the hybrid framework. A regulator is designed using this technique under periodic sampling and is applied to the nonlinear model of a quadrotor. The control of unmanned autonomous vehicles has become a research topic that is important by itself, particularly in the case of multi‐rotor systems due to their versatility in various applications. Tracking a reference signal for such vehicles can be challenging, particularly when the frequency of communication is limited, or the control station is located far away. The proposed controller is hence tested for this challenging application. [ABSTRACT FROM AUTHOR]

SUMMARY This paper presents a sufficient condition for observability of continuous-time switched nonlinear systems that also involve state jumps. Without the assumption of observability of individual modes, the sufficient condition is based on gathering partial information from each mode so that the state is completely recovered after several switchings. Based on the sufficient condition, a hybrid-type observer is designed, which comprises a copy of the actual plant and an error correction scheme at discrete time instants. In order to execute the proposed design, the observable component of the state at each mode needs to be estimated without transients or 'peaking' (caused by high-gain observers), and this motivates us to introduce a back-and-forth estimation technique. Under the assumption of persistent switching, analysis shows that the estimate thus generated converges asymptotically to the actual state of the system. Simulation results validate the utility of the proposed algorithm. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a novel input channel gain adaptive active disturbance rejection control (ICGA‐ADRC) framework for the nonlinear control system with large load variation. This paper first introduces the design of ICGA‐ADRC and proves its stability through the rigorous Lyapunov approach. Subsequently, the proposed controller is simulated by subjecting it to a nonlinear mass‐spring‐damping system characterized by significant load variations. Furthermore, the position‐tracking performance of the controller is tested experimentally by using a motor‐driven pendulum system with various loads. Finally, the results of both simulations and experiments show that the ICGA‐ADRC is capable of compensating for system model uncertainty and external perturbation while accurately estimating the system input channel gain in real‐time. The innovation of this paper is that the robustness of active disturbance rejection control (ADRC) to the system load variation is significantly strengthened by integrating robust adaptive finite‐time parameter estimation method into the conventional ADRC control architecture. [ABSTRACT FROM AUTHOR]

Motivated by the fully actuated system (FAS) approach, this paper proposes a direct robust control method for a type of uncertain multi‐order sub‐fully actuated systems (MOSFASs). Unlike numerous previous results, this paper broadens to a more general multi‐order model and the more challenging sub‐fully actuated case with the presence of singular problems. Firstly, a general uncertain MOSFAS model is presented, where the uncertainty merely needs to satisfy a common assumption. Secondly, a direct robust control method is proposed, giving a robust controller which only needs to satisfy very loose and basic requirements to ensure that the states ultimately converge into an arbitrarily small region. Simultaneously, by constraining the initial values of the states, it is perfectly guaranteed that the states always remain in a "safe" zone free of singular points, thus preserving the realizability of the controller. Finally, under certain conditions, a less conservative robust control scheme is introduced, which can relax the constraint of initial values easily. A simulation for a coarse‐fine angle system demonstrates the validity and value of the proposed robust control method. [ABSTRACT FROM AUTHOR]

This paper investigates the control problem of spacecraft rendezvous with obstacle constraint, considering the external disturbance forces caused by orbit perturbation. Firstly, the translational dynamic model of spacecraft rendezvous is given and then rewritten into a second‐order fully‐actuated system form. Then, by employing the prescribed performance control method, the performance function and error transformation are determined, pre‐defining the prescribed performance bounds. Moreover, the fully‐actuated system approach is used to linearize the original nonlinear system, which simplifies the processes of control law design and ensures model accuracy. After that, to ensure that the spacecraft could avoid the dangerous zone during its manoeuvre, the artificial potential function is introduced, based on which a sliding mode surface is designed. Finally, the prescribed performance control–artificial potential function‐based control law is derived, further adopting the neuro‐adaptive method to deal with external interferences. The stability of the close‐loop control system is analysed through the Lyapunov approach and the effectiveness of the proposed control scheme is verified by carrying out a numerical simulation. [ABSTRACT FROM AUTHOR]

Due to variable and complex work environments, nonlinearities, uncertainty and disturbances are inevitable in multi‐agent systems. Approximation‐free control can address these issues without involving approximators, such as fuzzy logic systems and neural networks. However, some issues like the singularity problem caused by the signals exceeding the preset boundary in changing work conditions still remain. This paper proposes an adaptive and reliable approximation‐free control, which comprises a novel singularity compensator and a modified transforming function. The proposed control scheme performs better in terms of convergence rate and overshoot, avoids issues relating to singularity, and has added flexibility in terms of parameter choice. The proposed control law adapts to changes in operating conditions and nonlinearities—the efficacy of which is demonstrated using simulations. [ABSTRACT FROM AUTHOR]

This paper presents the decentralized sampled‐data fuzzy observer design technique for nonlinear interconnected systems based on the Takagi–Sugeno fuzzy model. The interconnected system is considered that the subsystems include uncertain interconnections and are not needed to satisfy stable conditions. Based on the continuous‐time Lyapunov functional approach, the observer design technique is proposed to minimize the interconnection bound to attenuation degree ratio and formulated into the linear matrix inequality formats. Finally, by simulation example, it shows the validity and superiority of the proposed observer design technique. [ABSTRACT FROM AUTHOR]

Nonlinear control hypothesis is the region of control hypothesis which manages frameworks that are nonlinear, time‐variant, or both. Nonlinear control structures have extended high prevalence fundamentally because of the wide use of hypothetical intends to deal with credible issues in different zones of engineering and medical aspects. Sliding mode control (SMC) is a nonlinear control strategy applied when the deterministic model of the nonlinear system is discrepant with actual plant in terms of plant parameters and undetermined external disturbances. SMC is a sort of nonlinear control that accentuates zero error convergence modeling error, demonstrating nonlinearities and outside aggravations. Adaptive neuro‐fuzzy inference system (ANFIS) captures neural associations and soft reason guidelines in a single framework. ANFIS causes fewer errors and is more obvious to the customer isolated from the ANN. The paper centers that neural organizations accept a critical capacity in controlling nonlinear frameworks. This review aims an overview of existing methods for nonlinear control and application of SMC for nonlinear system based on ANFIS. [ABSTRACT FROM AUTHOR]

In this paper, the prescribed tracking performance control problem is addressed for uncertain nonlinear systems with unknown periodically time‐varying parameters and arbitrary switching signal. By utilizing radial basis function neural network and fourier series expansion, an approximator is developed to overcome the difficulty of identifying unknown periodically time‐varying and nonlinearly parameterized functions. To achieve the ideal tracking control performance and eliminate the influence of filtering error, a novel command filter‐based adaptive neural network prescribed tracking performance controller is designed by introducing a filtering compensation mechanism. Differently from the standard Backstepping technique, the proposed control scheme eliminates the "explosion of complexity" problem and relaxes the constraint condition on the reference signal. And then, it is warranted that the closed‐loop system is semi‐globally ultimately uniformly bounded and the tracking error is always limited to the specified region bounded by the performance functions. Three simulation examples are used to demonstrate the feasibility of the developed technique in this paper. [ABSTRACT FROM AUTHOR]

Summary: Motivated by existing works on the ℒ1$$ {\mathcal{L}}_1 $$ state‐feedback controller for nonlinear systems, in which the ℒ∞$$ {\mathcal{L}}_{\infty } $$ norm of the output for the worst disturbance with a unit magnitude is required to be bounded by 1, this paper considers an extension of those works to an output‐feedback form. More precisely, the existence of an ℒ1$$ {\mathcal{L}}_1 $$ output‐feedback controller for nonlinear systems is characterized by developing output regulation map and output controlled invariance domain, which are extended versions of the conventional regulation map and controlled invariance domain in the previous works. We first lead to a sufficient condition for the existence of an ℒ1$$ {\mathcal{L}}_1 $$ output‐feedback controller by ensuring the lower‐semicontinuity of the corresponding output regulation map. It is also shown in this paper that there exists an ℒ1$$ {\mathcal{L}}_1 $$ output‐feedback controller only if there exists an output controlled invariance domain. Based on these conditions, we further introduce algorithmic guidelines for verifying the existence of ℒ1$$ {\mathcal{L}}_1 $$ output‐feedback controller. Finally, a numerical example is provided to verify the validity of the overall arguments developed in this paper. [ABSTRACT FROM AUTHOR]

Summary: The Special Issue presents results of current research on learning‐based adaptive methods, merging together model‐based and data‐driven adaptive approaches. The special issue contains two main types of contributions. The first type of papers presents new theoretical developments for learning‐based adaptive algorithms, while the second type focuses on challenging practical applications ranging from UAVs, and autonomous vehicles, to heating and ventilation systems. These papers are compiled in a special issue of the journal. To access all of the papers please follow the following link (https://onlinelibrary.wiley.com/toc/10991115/2019/33/2). [ABSTRACT FROM AUTHOR]

Damage assessment of tall buildings after an earthquake is important for efficient postearthquake management due to social and economic reasons. Structural health monitoring (SHM) system enables rapid, remote, and objective condition assessment for tall buildings by controlling dynamic properties of structures. However, tracking only the changes in dynamic properties of tall buildings may not be sufficient for damage assessment. In this paper, changes in modal frequencies and maximum interstory drift ratio are investigated as damage assessment indicators because they can be obtained by analyzing vibration data recorded by SHM system. On the other hand, limited number of sensors are used due to economic reasons. Therefore, in this paper, firstly, a unique methodology on development and optimization of nonlinear simplified model for tall buildings is presented to estimate responses of noninstrumented floors from instrumented floors. After that, new threshold values are suggested for changes in dynamic properties and interstory drift ratios to reliably decide performance level of structures after earthquakes. Finally, the proposed method was validated with vibration record of a real damaged building. [ABSTRACT FROM AUTHOR]

This study addresses the state estimation problem of discrete‐time non‐linear stochastic systems with non‐Gaussian noises, particularly impulsive noises. Instead of minimizing the mean square error of the state estimate, which tends to excessively focus on outliers caused by non‐Gaussian noises, the ℓ1${\ell }_1$ norm‐based non‐linear recursive filter (L1KF) is put forward in this paper. Here, minimizing the ℓ1${\ell }_1$ norm of model errors is actually to pursue the minimum sum of absolute values of all errors, which is equitable to all model errors rather than paying much attention on outliers. To further improve estimation accuracy, a recursive nonlinear smoother (L1KS) is proposed, based on minimizing the ℓ1${\ell }_1$ norm of model errors. The proposed ℓ1${\ell }_1$ norm‐based filter and smoother are implemented using unscented transformation for statistical linear regression applied to nonlinear models. Additionally, the computational complexity of the proposed method is analysed. Simulation results of tracking a radar target with impulsive noises demonstrate the effectiveness and robustness of the proposed estimator. [ABSTRACT FROM AUTHOR]

The concurrent existence of model uncertainties, external disturbances, and actuator faults in a nonlinear system such as a robotic manipulator can significantly affect the trajectory tracking performance of these systems. Therefore this study is devoted to proposing a control approach based on fixed‐time control theory to improve the trajectory tracking performance of the robotic manipulator in the presence of lumped disturbance, including uncertainties, external disturbances, and actuator faults. The control approach in this paper is designed based on the integration of a fixed‐time adaptive sliding mode observer and a fixed‐time non‐singular fast terminal sliding mode control design strategy. Firstly, a new fixed‐time adaptive sliding mode observer is designed based on fixed‐time theory and adaptive control theory to estimate the lumped disturbance present in the system. Then, using the information from the disturbance observer, the fixed‐time non‐singular fast terminal sliding mode control is devised based on a non‐singular fixed‐time sliding surface and a fixed‐time reaching approach. Furthermore, in the sense of the Lyapunov theorem, through rigorous analysis, it is demonstrated that the tracking errors of the closed‐loop system converge to a small neighbourhood within a fixed time, regardless of the information about the initial conditions of the states of the system. Finally, extensive comparative simulations are performed using the PUMA560 robot to manifest the feasibility and validity of the proposed control strategy in terms of trajectory tracking accuracy and fast convergence in the presence of uncertainties, disturbances, and actuator faults. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear control law with a dynamic controller based on high gain observer (HGO) for a co‐axial double rotor magnetic gear (CADRMG) based on Halbach array used in wind turbines. The generalized canonical form (GCF) is used to normalize the nonlinear augmented system to achieve the dynamic control signal with a chain of integrator of system output. In addition, a tracking problem is defined to track high speed rotor (HSR) with respect to GCF. On the other hand, the HGO is used to estimate the error tracking at the expense of nonlinear terms. Furthermore, the nonlinear system observability of the augmented system is evaluated. A dynamic control law is used to solve the tracking problem based on HGO. This controller can eliminate the effect of load torque as a constant and unknown disturbance in the closed‐loop system. Moreover, the closed‐loop stability of the system under dynamic signal control is guaranteed. The system states estimation is calculated by recursive equations from tracking error estimations. Finally, numerical simulations are given to illustrate the theoretical results of proposed system. [ABSTRACT FROM AUTHOR]

This paper presents an analytical method to solve the optimal control problem for affine nonlinear systems with unknown drift dynamics. A new non‐quadratic cost function over an infinite horizon is presented that considers input constraints and includes the cost of the feed‐forward component of the control law. The mean value theorem for vector‐valued functions has been used to derive an integral form of this theorem. Based on this theorem, a rigorous proof is provided demonstrating that the cost function can be converted into another form. In the presence of input constraints, this converted form enables extracting the optimal control solution without solving the HJB equation. Additionally, unknown nonlinearity effects in drift dynamics are compensated in the control input. This is accomplished by estimating the unknown drift dynamics via an adaptive neural network (NN) approach. It is proven that the states and weights of NN are uniformly ultimately bounded based on a Lyapunov technique. The necessary and sufficient conditions are provided that ensure the optimality of the infinite horizon optimal control problem with a discount factor. As a result, it is demonstrated that the proposed approach satisfies the optimality criteria. To evaluate the effectiveness of the proposed approach, simulation examples are provided. [ABSTRACT FROM AUTHOR]

This paper studies the finite time stabilization control (FTSC) problem of nonlinear systems with unknown functions and full‐state constraints (FSCs). A lemma dealing with unknown functions is put forward so that the assumptions and the "explosion of terms" (EOT) of backstepping are avoided. Compared with approximation algorithms, this algorithm can make the system states converge to the origin in a finite time. On the other hand, a new logarithmic constraint function is utilized to solve the drawback of barrier Lyapunov functions (BLFs). Then, combined with finite time control (FTC), an FTSC algorithm with FSCs is proposed. Finally, simulations of Chua's circuit system are given to verify the effectiveness and superiority of this algorithm. Compared with the approximation algorithm, this algorithm can theoretically make the states of Chua's circuit system to the origin in a finite time. [ABSTRACT FROM AUTHOR]

Wind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card. [ABSTRACT FROM AUTHOR]

This paper investigates the prescribed‐time robust control problem of a class of nonlinear time‐delay systems, and proposes some new prescribed‐time robust stabilization results. First, by applying the full decomposition method based on the delay term, the paper obtains an equivalent model of original system and promotes existing prescribed‐time stability criterion of delay‐free system to delay one. Based on these, when the bound of the disturbance is known, we design the corresponding prescribed‐time stabilization and robust stabilization controller. On the basis, we further give several robust stabilization results for the unknown disturbance by designing a new prescribed‐time controller. Finally, the study of two illustrative examples shows that the results obtained of the paper work well in the prescribed‐time robust stabilization the nonlinear delay systems. It is well worth pointing out that, different from existing infinite‐time and finite‐time asymptotical results of nonlinear delay system, the closed‐loop system under the prescribed‐time controller can reach the desired position within the specified time, and has stronger anti‐interference ability. [ABSTRACT FROM AUTHOR]

Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under‐ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV‐MPC) is derived from the nonlinear model to enable real‐time control. In simulations, NMPC and LTV‐MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise‐time, overshoot, steady‐state error, and robustness. The LTV‐MPC shows satisfactory real‐time performance in experimental validation. The paper further also demonstrates experimentally that LTV‐MPC can be run real‐time on the AUV in performing hydrobatic maneouvers. [ABSTRACT FROM AUTHOR]

This paper proposes a near‐optimal controller design for the constrained nonlinear affine systems based on a Recurrent Neural Network (RNN) and Extended State Observers (ESOs). For this purpose, after defining a finite‐horizon integral‐type performance index, the prediction over the horizon is performed using the Taylor expansion that converts the primary problem into a finite‐dimensional optimization. In comparison with other controllers of the similar structure, the proposed method is capable of dealing with output constraints by employing the Control Barrier Function (CBF). The class of the output and input constraints are of the box‐type. Moreover, whereas several safe control approaches are proposed regardless of the performance of the closed‐loop system, this paper aims at achieving a near‐optimal performance as far as the constraints permit. As a result, a constrained optimization problem is achieved, where the online solution is obtained using a rapidly convergent RNN. Stability and the ease of implementation are some of the advantages of this network making the algorithm more reliable. Moreover, integrated stability analysis of the closed‐loop system that includes the dynamic RNN reveals that the closed‐loop system is stable in the sense of the Lyapunov stability theory. The effectiveness of the proposed control method in terms of the tracking performance and constraint satisfaction is illustrated through a simulating example of two‐inverted pendulums system. The results indicated advantages of the proposed method as compared with the recently published methods in well‐known literature. [ABSTRACT FROM AUTHOR]

Summary: Solar photovoltaic (SPV) modules are frequently utilised to produce electricity. The output voltage and load power in SPV‐based applications are variable and strongly reliant on the operating conditions of solar energy, such as irradiance and ambient temperature. They offer additional challenges for the robust design of power converters, and DC–DC power converters provide the power bus with a constant voltage. Properties of a nonlinear system and load patterns necessitate a more robust control design for the power converter. This paper gives robust control of high‐order sliding mode (HOSM) control in isolated flyback converters for solar photovoltaic applications. The proposed HOSM technique is contrasted with the conventional PI control technique. The proposed HOSM control approach outperforms traditional PI control strategies under uncertainty and disturbance settings. This paper also presents a method for preventing chattering in flyback converters (FCs) using modified super twisting sliding modes. [ABSTRACT FROM AUTHOR]

As a nonlinear control approach, a database‐driven PID (DD‐PID) controller has been proposed that uses distance calculations of an initial database to tune PID parameters. However, sorting operations are required in extracting neighborhood data from the database, which imposes a large computational load. Therefore, it is impossible to finish this computation within a restricted sampling period in control systems with fast response time and insufficient memory capacity. On the other hand, the cerebellar model articulation controller (CMAC) has advantages for these problems and has similar constructs to the DD approach. Therefore, this paper combines advantages of these two approaches, and proposes a CMAC‐PID controller design based on off‐line optimization of a database. Finally, a comparison with conventional off‐line CMAC‐PID controller and DD‐PID controller through simulations are considered. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the first model reference adaptive control system for nonlinear, time‐varying, hybrid dynamical plants affected by matched and parametric uncertainties, whose resetting events are unknown functions of time and the plant's state. In addition to a control law and an adaptive law, which resemble those of the classical model reference adaptive control framework for continuous‐time dynamical systems, the proposed framework allows imposing instantaneous variations in the reference model's trajectory to rapidly steer the trajectory tracking error to zero, while retaining the closed‐loop system's ability to follow a user‐defined signal. These results are enabled by the first extension of the classical LaSalle–Yoshizawa theorem to time‐varying hybrid dynamical systems, which is presented in this paper as well. A numerical simulation shows the key features of the proposed adaptive control system and highlights its ability to reduce both the control effort and the trajectory tracking error over a classical model reference adaptive control system applied to the same problem. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of robust H∞$$ {H}_{\infty } $$ memory observer design problem for discrete‐time switched nonlinear systems by using past output measurements. Different from traditional design strategies of Luenberger‐type observers using the current output measurements, the memory Luenberger‐type observer design strategy introduces the past output measurements of switched systems and observers simultaneously. In addition, in order to better describe the observer error systems, a novel augmented state variable framework of the observer error systems is developed. The highlight of this paper lies that the memory observer design strategy is developed by considering past output measurements and state error values of memory observers, whose performance can be improved by increasing the number of past output measurements used in the observer design, which is an important discovery and significant novelty of this work. By combining the multiple Lyapunov functions approach and average dwell time technique, and introducing matrix variables with free structure, new sufficient conditions for weighted H∞$$ {H}_{\infty } $$ performance analysis of observer error systems are derived. Then, the memory observer synthesis conditions are obtained in the form of linear matrix inequality (LMI), and the memory observers can be obtained by solving these LMIs. Lastly, two examples are presented to show the advantages and effectiveness of the derived results. [ABSTRACT FROM AUTHOR]

This paper introduces a novel data‐driven approach to develop a fault‐tolerant model predictive controller (MPC) for non‐linear systems. By adopting a Koopman operator‐theoretic perspective, the proposed method leverages historical data from the system to construct a data‐driven model that captures the non‐linear behaviour and fault characteristics. The fault influence is addressed through an online estimation of a time‐varying Koopman predictor, which allows for adjusting the MPC control law to counteract the fault effects. This estimation is performed in a higher dimensional Koopman feature space, where the dynamics behave linearly. As a result, the non‐linear fault‐tolerant MPC optimization problem can be replaced with a more practical and feasible linear time‐varying one using the approximated Koopman predictor. Moreover, by incorporating the online update procedure, the time‐varying Koopman predictor can represent the dynamics of the faulty system. Hence, the controller can adapt and compensate for the faults in real‐time, integrating the fault diagnosis module in the MPC framework and eliminating the need for a separate fault detection unit. Finally, the efficacy of the proposed approach is demonstrated through case study results, which highlight the ability of the controller to mitigate faults and maintain desired system behaviour. [ABSTRACT FROM AUTHOR]

This paper investigates the design of a model‐free adaptive iterative learning controller(MFAILC) based on sampled‐data under the presence of denial‐of‐service(DoS) attacks in nonlinear networked control systems. First, the MFAILC is presented only using I/O data, where a compensation mechanism for DoS attacks is proposed. With dynamic linearization techniques, the nonlinear system is transformed into a linear system in the iteration domain. Then an improved MFAILC is designed to actively compensate the lost data caused by DoS attacks, where the estimation of pseudo‐partial derivative (PPD) is improved by establishing the AR model. The proposed algorithm can weaken the adverse effects of the DoS attacks and ensure the excellent tracking performance of the system. Finally, the stability of the method is proved, and the effectiveness of the proposed algorithm is demonstrated by a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, a novel nonlinear unknown input observer is proposed in order to fault estimation for nonlinear uncertain systems with time delays. By the estimation of the faults, the features are detected such as shape, size occurrence time etc. The time delay is considered a constant and known parameter in the states. The disturbances are investigated in the states and outputs and also, and sensor and actuator faults are considered. The stability of the closed‐loop system is guaranteed by Lyapunov–Krasovskii theory and some feasible Linear matrix inequalities (LMI). The proposed method is simulated on a continuous‐stirred tank reactor (CSTR) with uncertainties and time delay. Simulation results show the appropriate efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates decentralised adaptive control of discrete‐time systems consisting of linear time‐invariant systems and a class of nonlinear systems. The parameters of the subsystems and the interconnection strengths between the subsystems are assumed unknown. Multiple adaptive prediction models with switching are used in this paper to address the relatively poor transient performance that typically results from a single prediction model. However, the application of this methodology requires indirect model reference discrete‐time adaptive control, and there appears to be very little focus in the literature on this. The principal contribution of this paper is to fill this void by arriving at proof of global stability of such decentralised adaptive systems using the theory of Lyapunov and properties of square‐summable sequences. The chosen representation of the system enables the same results to be directly applicable to both linear time‐invariant and linear‐in‐the‐parameters nonlinear subsystems. We consider two parametric update algorithms, one of which has a normalisation factor. Simulation studies included in this paper demonstrate the improvement in transient performance. [ABSTRACT FROM AUTHOR]

In this paper, the tracking problem is concerned for a class of nonlinear system. The primary objective is to achieve fixed‐time control performance in the presence of nonvanishing mismatched uncertainties and nonparametric external disturbance. Firstly, in order to estimate disturbance, a fixed‐time extended state observer with fractional power term is developed, the order of which is reduced to alleviate peaking value phenomenon. By reasonably switching among different estimation patterns, both transient and steady‐state performances are balanced. Secondly, by means of fuzzy logic system theory, an ESO‐based adaptive fuzzy control strategy with time‐varying gains is presented, and thus the mismatched nonlinearities are approximated, and the external disturbance is compensated in real time simultaneously. Moreover, by constructing a mixed barrier and quadratic type of Lyapunov function, the fixed‐time stability analysis is put forward for tracking error dynamics based on backstepping methodology. Finally, the advantages and effectiveness are illustrated by a numerical example on wheeled mobile robot. The main characteristic of this paper is to integrate ESO methodology into fuzzy control architecture in fixed‐time sense. As a consequence, not only the output tracking purpose, but also desirable transient performance can be achieved. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a backstepping model‐free adaptive control (BS‐MFAC) algorithm for the trajectory tracking control problem of a class of second‐order nonlinear systems. Firstly, the model‐free adaptive control (MFAC) is employed to track the virtual desired velocity. Then, based on the measured state data at the current moment, a virtual desired velocity is designed, combining MFAC with backstepping control in a clever manner. By designing a discrete form of Lyapunov function, it is proven that the system velocity error under this algorithm converges asymptotically and stably. Furthermore, a nonhomogeneous difference equation solution related to the system position is obtained through calculations. The analysis of the solution indicates that the system position output error will slide into a neighborhood around zero. This algorithm possesses advantages such as simple structure, model independence, and strong robustness. Additionally, a planar two‐link space robot arm model is constructed using the Mbdyn platform, a multibody dynamics simulation software. Trajectory tracking control of joint angles is achieved under the proposed control algorithm in this paper. The simulation outcomes unequivocally establish the efficacy of the algorithm, which adeptly exploits the pseudo partial derivatives (PPD) estimation for second‐order nonlinear systems in MFAC. The algorithm yields expedited and superior trajectory tracking control of said systems. [ABSTRACT FROM AUTHOR]

The shaking table test has been widely recognized as one of the most reliable methods for assessing the dynamic response of structures and systems when exposed to a range of vibrations. Replicating these vibrations, recorded as acceleration signals, necessitates precise controller designs. However, traditional controller designs primarily rely on linear system assumptions, leading to complications in incorporating parametric uncertainty and uncertain non‐linearity. This paper presents a novel approach to the acceleration trajectory control problem of single‐axis hydraulic shaking table systems from a non‐linear perspective. This strategy employs a controller combined with an extended state observer (ESO) and adaptive control, offering a design that more closely reflects real‐world scenarios and requires less parameter adjustment. The ESO estimates the uncertain non‐linearity using the displacement signal, while the parametric uncertainty is tackled through adaptive control. The proposed controller's effectiveness is validated through theoretical proof using Lyapunov analysis. The analysis demonstrates that asymptotic tracking performance is guaranteed when the uncertain non‐linearity is time‐invariant. Moreover, with time‐variant uncertain non‐linearity, the controller can also ensure both prescribed transient tracking performance and final tracking accuracy. Comparative experiment results underscore the superior performance of the proposed controller. [ABSTRACT FROM AUTHOR]

Controlling the Permanent Magnet Synchronous Motor (PMSM) can be challenging due to the nonlinearity of its dynamics, which makes it difficult to design control strategies that are both robust and effective. To address this challenge, this paper presents a novel control strategy rooted in the concept of passivity that combines field‐oriented control (FOC). This strategy compels the PMSM to accurately follow velocity and electrical torque trajectories. The approach, known as passivity‐based control (PBC), entails reshaping the inherent system energy while introducing the necessary damping to attain the desired objectives. A crucial aspect involves identifying workless force terms within the process model. Despite their presence, these terms do not impact the energy balance and stability properties. As a result, eliminating these terms is unnecessary. This simplicity in control architecture not only preserves system stability but also bolsters overall robustness. The system's overall stability and the current tracking error's exponential convergence have both been demonstrated analytically. In order to maintain stability, the controller accounts for the nonlinearities of the plant and approximates the unstructured dynamics of the PMSM. The proposed control is designed using the dq model of the PMSM, which avoids the model's structure destruction due to singularities, since the dq model does not depend explicitly on the rotor angular position. Experimental results shown further, illustrate speed and position control with a desired pair calculated by a filter or a proportional‐integral (PI) controller for speed control and a proportional‐integral‐derivative (PID) controller for position control. Also the correlation between practical and theoretical results is given as well as the robustness test in relation to the uncertainties of the PMSM's inertia moment. The results demonstrates the effectiveness of the proposed strategy in controlling the PMSM under different operating conditions, highlighting its potential for industrial applications. [ABSTRACT FROM AUTHOR]

In this paper, the event‐triggered distributed consensus tracking control problem for a class of multi‐agent systems (MASs) is studied, where denial‐of‐service (DoS) attacks and Bouc‐Wen hysteresis inputs are considered in the communication channel and the actuator, respectively. First, when communication networks are subject to malicious DoS attacks, the connection weights of the communication topology graphs are affected. To address this problem, this paper establishes topological models of the communication network using a switching topology method, and gives connection recovery strategies for the communication network. Second, in order to save communication resources, a distributed control protocol based on an event‐triggered mechanism and locally deployed estimator is designed for DoS signals occurring in the communication network of states and control inputs. In addition, the effect of unknown Bouc–Wen hysteresis input is eliminated using the Nussbaum‐type function. Then, based on Lyapunov stability theory, the boundedness of all signals in the closed‐loop system is verified, and the objective of consensus control is achieved. Finally, the effectiveness of the proposed control scheme is demonstrated by numerical and practical simulation examples. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an adaptive output feedback dynamic surface control (DSC) strategy is proposed for strict‐feedback stochastic nonlinear systems with input quantization, prescribed performance and dynamic uncertainties. A new quantizer is used to process the input signal, which can avoid the chattering of the quantization signal and keep the upper bound of the quantization error constant. Radial basis functions are used to approximate unknown smooth functions, unmodeled dynamics are processed by dynamic signals, and unmeasurable states are estimated by high gain observer. Hyperbolic tangent functions are employed to handle prescribed performance. The second order command filter is used to replace the first order filter used in general DSC, and the compensation term is added in each step of DSC. By the Lyapunov stability analysis, all signals in the controlled system are semi‐globally uniformly ultimately bounded (SGUUB) in probability. Two examples further prove that the control scheme designed in this paper is reasonable and effective. [ABSTRACT FROM AUTHOR]

This paper addresses the fixed‐time adaptive neural distributed control problem of uncertain nonlinear multiagent systems (MASs) with unknown communication link faults. To handle the unknown terms caused by communication link faults and the unknown functions in each agent, a radial basis function neural network (RBF NN) is employed. In contrast to existing methods that focus solely on practical finite‐/ fixed‐time stability of nonlinear systems, the control scheme developed in this paper goes beyond by not only mitigating the effects of unknown communication link faults in nonlinear MASs but also addressing the singularity problem of fixed‐time controllers. These advancements are realized through the design of new power exponent functions and the introduction of new lemmas. This way, the tracking error of MASs converges to a preset range within a fixed time and achieves better control performance. Finally, the feasibility of the proposed scheme is verified through two simulations. [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]

This paper proposes, a novel decentralized fuzzy filter design for nonlinear large‐scale systems with uncertain interconnections. Based on Takagi–Sugeno (T–S) fuzzy model, the error system is represented by filter error and filter state variables, and the H∞$H_{\infty }$ filtering problem is defined. By using the Lyapunov functional, the stability condition of the error system with H∞$H_{\infty }$ performance is obtained to solve the H∞$H_{\infty }$ filtering problem, and its sufficient condition is converted into the linear matrix inequality (LMI) formats. Furthermore, the decentralized fuzzy filter design technique is also presented for the non‐measurable premise variable case. Finally, an example is provided to verify the effectiveness of the proposed decentralized fuzzy filtering techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a non‐singular fixed‐time fault‐tolerant adaptive tracking control method for a kind of multi‐input and multi‐output nonlinear system, which is in form of non‐strict feedback and is subject to actuator faults. The major contribution, compared to other practical fixed‐time fault‐tolerant control methods, is that the fixed time can be calculated precisely. By applying fuzzy logic systems, the identification of unknown nonlinearities is achieved, and by the boundness of fuzzy basis functions, the algebraic loop problem is avoided. Based on the fixed‐time stability theory, the practical fixed‐time fault‐tolerant adaptive tracking control method is proposed with adaptive back‐stepping control technology, in which the control singular problem is overcome by applying the adding power integration method. By the Lyapunov function theory, the practical fixed‐time stability of the closed‐loop system is theoretically proved. And even in the case of unknown actuator faults, the tracking error of the controlled system for the given command signal can still converge to a small neighbourhood of zero within a fixed time, regardless of the system initial state. Finally, the proposed control method is successfully implemented on a multi‐motor control platform, proving its effectiveness, and multiple comparative simulations are carried out, demonstrating its superiority. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new state and parameter cooperative estimation design method for the nonlinear mechanical systems that can measure the configurations. The paper is based on a preceding state observer that is intrinsic to nonlinear mechanical systems and considers the case where a design parameter differs by some constant error from the real parameter. In the paper, we first confirmed through theory and simulation that the configurations estimated by the observer differ from those of the real dynamics. Then we confirm that modifying the parameter in a direction reducing the difference between the measured and estimated configurations can sufficiently estimate the real parameter, and we derive a parameter estimation equation based on it. Furthermore, we designed a new cooperative estimation method that estimates the system states and a parameter simultaneously in association with each other by adding this estimation equation to an intrinsic observer designed for mechanical systems. Numerical simulations show that it estimates an exact parameter value while ensuring convergence rates as much as the preceding observer. [ABSTRACT FROM AUTHOR]

This paper is concerned with the effects of nonlinear sensitivity on boundedness of solutions for the following chemotaxis‐Stokes system nt+u·∇n=∇·(∇n−S(n)∇c)−nm,(x,t)∈Ω×(0,∞),ct+u·∇c=Δc−c+m,(x,t)∈Ω×(0,∞),mt+u·∇m=Δm−mn,(x,t)∈Ω×(0,∞),ut=Δu+∇P+(n+m)∇ϕ,(x,t)∈Ω×(0,∞),∇·u=0,(x,t)∈Ω×(0,∞),$$\begin{eqnarray*} \qquad\qquad\qquad\qquad\qquad{\left\lbrace \begin{aligned} &n_{t}+{\bf u}\cdot \nabla n=\nabla \cdot (\nabla n-S(n)\nabla c)-nm, &(x,t)\in \Omega \times (0,\infty), \\ &c_{t}+{\bf u}\cdot \nabla c=\Delta c-c+m, &(x,t)\in \Omega \times (0,\infty),\\ &m_{t}+{\bf u}\cdot \nabla m=\Delta m-mn, &(x,t)\in \Omega \times (0,\infty), \\ &{\bf u}_{t}=\Delta {\bf u}+\nabla P+(n+m)\nabla \phi , &(x,t)\in \Omega \times (0,\infty),\\ &\nabla \cdot {\bf u}=0, &(x,t)\in \Omega \times (0,\infty), \end{aligned} \right.} \end{eqnarray*}$$in a smoothly bounded domain Ω⊂R3$\Omega \subset \mathbb {R}^{3}$ under no‐flux boundary conditions for n,c,m$n, c, m$ and no‐slip boundary conditions for u, where n and m denote the densities of unfertilized sperms and eggs, respectively, c stands for the concentration of the signal, u represents the velocity of fluid, P is the pressure within the fluid and ϕ is the gravitational potential. This system describes the process of coral fertilization occurring in ocean flow. Based on the novel conditional estimates for c and u, it is proved that for all appropriately regular nonnegative initial data, this system possesses a unique globally bounded solution provided that S∈C2([0,∞))$S\in C^{2}([0,\infty))$ satisfies S(n)≤χn(n+1)α−1$S(n)\le \chi n(n+1)^{\alpha -1}$ with χ>0andα<1$\chi >0\;\text{and}\;\alpha <1$, which improves the known subcritical exponent α<23$\alpha <\frac{2}{3}$ under fluid‐free case. This paper is concerned with the effects of nonlinear sensitivity on boundedness of solutions for the following chemotaxis‐Stokes system nt+u·∇n=∇·(∇n−S(n)∇c)−nm,(x,t)∈Ω×(0,∞),ct+u·∇c=Δc−c+m,(x,t)∈Ω×(0,∞),mt+u·∇m=Δm−mn,(x,t)∈Ω×(0,∞),ut=Δu+∇P+(n+m)∇ϕ,(x,t)∈Ω×(0,∞),∇·u=0,(x,t)∈Ω×(0,∞),$$\begin{eqnarray*} \qquad\qquad\qquad\qquad\qquad{\left\lbrace \begin{aligned} &n_{t}+{\bf u}\cdot \nabla n=\nabla \cdot (\nabla n-S(n)\nabla c)-nm, &(x,t)\in \Omega \times (0,\infty), \\ &c_{t}+{\bf u}\cdot \nabla c=\Delta c-c+m, &(x,t)\in \Omega \times (0,\infty),\\ &m_{t}+{\bf u}\cdot \nabla m=\Delta m-mn, &(x,t)\in \Omega \times (0,\infty), \\ &{\bf u}_{t}=\Delta {\bf u}+\nabla P+(n+m)\nabla \phi , &(x,t)\in \Omega \times (0,\infty),\\ &\nabla \cdot {\bf u}=0, &(x,t)\in \Omega \times (0,\infty), \end{aligned} \right.} \end{eqnarray*}$$in a smoothly bounded domain Ω⊂R3$\Omega \subset \mathbb {R}^{3}$ under no‐flux boundary conditions for n,c,m$n, c, m$ and no‐slip boundary conditions for u, where n and m denote the densities of unfertilized sperms and eggs, respectively, c stands for the concentration of the signal, u represents the velocity of fluid, P is the pressure within the fluid and ϕ is the gravitational potential.... [ABSTRACT FROM AUTHOR]

This paper focuses on the global prespecifiable fixed‐time control problem of uncertain strict‐feedback nonlinear system with input quantization. In contrast to the existing fixed‐time control results, the signs of the control gains can be unknown in our paper. Unlike the traditional fixed‐time control schemes, some exponential functions are added into the virtual controllers to accelerate the convergence rate. Besides, some dynamic switching functions are also introduced to compensate the unknown control signs. Based on the novel regulate law, the switching functions can be regulated online to ensure the fixed‐time stability for the considered system. Furthermore, the settling time can be easily adjusted by selecting the controller parameters. Simulation results are provided to illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the iterative learning control (ILC) for a class of second‐order nonlinear hyperbolic impulsive partial differential systems. Firstly, to follow the discontinuous desired output, a P‐type learning law is adopted, and sufficient conditions for the convergence of the tracking error is established under identified initial state value. The rigorous analysis is also given using the impulsive Gronwall inequality. Secondly, the tracking error of output trajectory is considered in systems with state initial values shifting based on an initial learning algorithm. These results of this paper show that the tracking error on the finite time interval can uniform converge to 0 as the iteration index goes to infinity if impulse number of the systems is only a finite numbers. Finally, two numerical simulation examples are given to verify the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Disturbances and uncertainties can produce unsatisfactory responses in many industrial and engineering systems. Besides, the practical systems and processes are multiple‐input multiple‐output (MIMO). Hence, achieving a good control performance with adequate output responses is not simple. Many different methods were provided for control of industrial processes in some references. However, in this paper, the primary goal is to design an appropriate tracking controller for alleviating the destructive effects of uncertainties in output channels of MIMO nonlinear systems. For this purpose, a robust mechanism has been introduced according to the optimal design of centralized extended proportional‐derivative (CEPD) and disturbance observer (DOB). By designing the derivative part Kd$$ {K}_d $$ based on famous Vandermonde matrix and DOB gain Γ$$ \Gamma $$, the robust criterion R=I+CGKd−1$$ R&amp;#x0003D;{\left(I&amp;#x0002B; CG{K}_d\right)}&amp;#x0005E;{-1} $$ is obtained to tackle the undesirable factors such as nonlinear functions and uncertainties in error dynamics. The closed‐loop stability is guaranteed by tuning the proportional part Kp$$ {K}_p $$ under linear matrix inequality. The proposed scheme in this paper can be used for a wide range of MIMO nonlinear systems in practical situations. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive compensation control algorithm for solving the actuator failures problem of nonlinear stochastic multi‐agent systems (MASs) under the directed communication topology. It should be emphasized that the coexistence of unknown nonlinearities, stochastic perturbations and actuator failures makes the implementation of control protocol very difficult and extremely challenging. To achieve the control objective, fuzzy logic systems (FLSs) are first employed to deal with the unknown nonlinearities of each agent. Then, the threshold‐based event‐triggered mechanism is further considered to reduce the communication burden of the system in the case of limited communication resources. Moreover, the issue of "explosion of complexity" is solved by using dynamic surface control (DSC) technique in the process of backstepping design. With these efforts, the actuator failures are circumvented and the outputs of the followers converge to the convex hull spanned by the multiple leaders' outputs. Finally, the simulation results of multiple single‐link robots show the validity of the proposed design scheme. First, the studied stochastic nonlinear MASs consisting of N leaders is considered, in which the dynamics of the leaders can be completely unknowable. Therefore, the leader models can be more general compared to the previous control results [42–46]. Moreover, we cleverly apply a novel coordinate transformation to deal with the consensus problem of multi‐leader. Second, in this paper, the adaptive compensation controller is constructed to effectively compensate the uncertainties of the actuator failure models. In addition, event‐triggered control mechanism for the input of the actuator failure model is used to address the problem of resources transmission limitation. Third, the proposed communication protocol can ensure that the outputs of the followers converge to the convex hull spanned by the outputs of the multiple leaders. Furthermore, the problem of "explosion of complexity" in the derivation process is solved by fusing the backstepping and DSC technology. [ABSTRACT FROM AUTHOR]

This paper focuses on the control problem of time‐delay nonlinear high‐order fully actuated (HOFA) systems with unmodeled dynamics and unknown dead‐zone input. The primary objective of this paper is to design an adaptive controller by using the HOFA systems approach. To do so, we need to tackle some technical obstacles. Firstly, in order to deal with unmodeled dynamics in the HOFA systems, the technique of changing the supply rate is combined with the HOFA systems approach. Secondly, an adaptive dead‐zone inverse is constructed to compensate for the influence of the unknown asymmetrical dead‐zone input nonlinearity. Then, the controller is designed by the HOFA systems approach, and it is proved that all states of the systems converge to a bounded region based on the Lyapunov‐Krasovskii functions. Finally, the simulation results demonstrate the effectiveness of the designed adaptive controller. [ABSTRACT FROM AUTHOR]

The security control problem for a class of unknown nonlinear systems is considered in this paper. For the nonlinear system running in the network environment, the measurement channel is subjected to hybrid attacks. Intermittent denial of service attacks and false data injection attacks are modeled as the hybrid attacks. According to the characteristics of the repetitive system, a resilient iterative learning control (ILC) algorithm under hybrid attacks is devised. Subsequently, the stability of the system is proved by mathematical derivation and theoretical analysis in the sense of mathematical expectation. The theoretical analysis results indicate that the resilient ILC algorithm can ensure the stability of the system, and the tracking error converges with the increased number of iterations. Finally, the validity of the algorithm is illustrated by numerical simulation and mobile robot simulation. [ABSTRACT FROM AUTHOR]

The problem of the system robustness subject to physical constraints and mismatched fault reconstruction is discussed in this paper. In order to facilitate the design, a four‐rotor unmanned aerial vehicle (UAV) system model was selected for research. First, the control allocation model of the nonlinear UAV system with disturbances is shown in the paper. Secondly, a weighted pseudo‐inverse method based on adaptive weights is proposed, which reduces the impact of physical constraints on the system. After that, a dynamic weight control allocation method based on the fault efficiency matrix is designed. The weight matrix can dynamically adjust the control distribution law according to the fault estimation value provided by the observer. Then, a dynamic adaptive control allocation method for faults and physical constraints is carried out by combining adaptive weights and dynamic weights. Finally, a simulation example is presented to further illustrate the effectiveness of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]