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Due to the rapid and large amplitude changes of the current and voltage on the load, the controller will generate oscillations around the sliding mode surfaces (chattering) damaging the actuators, thus in order to reduce the load. For this phenomenon, the Relay function has been suggested to be used instead of the classical sign function. On the other hand, when the parameter of the nonlinear object changes with time, the problem of keeping the speed constant when the load changes is difficult, so an adaptive observer using fuzzy logic control is designed to estimate motor speed which is necessary to increase the stability of the controller. From MATLAB/Simulink simulation and experimentation by OPAL_RT device (OPAL-RT has delivered eFPGASIM, the industry's most powerful and intuitive FPGA-based real-time solution) for the three-phase asynchronous induction motor 1-hp, 150-rad/s with a three-phase three-level cascaded inverter, the results show the estimated speed and the good tracking speed according to the value set at a frequency varying from the lowest 10 rad/s to the highest 150 rad/s, and the system remains stable when the stator and rotor resistance changes up to 1.5 times the initial value in the presence of noise. The stability of the suggested algorithm, which requires little memory and produces low output voltages and currents (total harmonic distortion) with high harmonic component. The SMC laws are obtained in the sense of the Lyapunov stability theorem. Therefore, the stability, robustness and tracking control features are guaranteed, and the system is stable even when noise enters in the system. [ABSTRACT FROM AUTHOR]

The fixed-time asymptotic stability and energy consumption estimation problems of nonlinear systems were discussed. First, a novel dynamic model with nonlinearity and time-varying delays was proposed. Second, to effectively improve the convergence rate of the system, a fixed-time control strategy was adopted. Through construction of the Lyapunov functional and with the inequality analysis method, sufficient conditions for the error system were obtained to achieve the fixed-time asymptotic stability. Moreover, to predict the energy consumption during the operation of the system, the upper bound of the energy consumption for the system was estimated, which is helpful to evaluate the operation time of the system. Finally, a numerical example was given to verify the feasibility and effectiveness of the results. [ABSTRACT FROM AUTHOR]

In this article, a continuous-discrete high gain observer with a proportional integral (PI) output predictor structure is designed for a class of nonlinear systems subject to sampled-delayed data measurements. To counteract the effect of the well-known sensitivity of the high gain observer to the noise measurements, a novel PI output predictor structure is proposed. The uniform ultimate boundedness (UUB) of the proposed observer is demonstrated by means of Lyapunov function and small gain method. The effectiveness of our proposed observer is demonstrated through numerical simulations and performances comparison with previous observer design methods in the literature. [ABSTRACT FROM AUTHOR]

Rapid faults diagnosis and isolation in flight control systems is vital to evade undesirable effects on the environment, humans as well as on the system itself. In this research, a new strategy based on an online dynamical system is proposed for sensors fault diagnosis and isolation of quadrotor unmanned aerial vehicle (UAV). In the presented strategy, the multi-layer neural network (MLNN) is adopted as an observer for sensor faults diagnosis of quadrotor unmanned aerial vehicle (UAV). For the improvement of faults detection accuracy and effectiveness, two new adaptive weight-updating approaches are used in this study. Lyapunov function theory-based approaches are proposed for updating the learning rate parameters of a multi-layer neural network (MLNN). The key purpose of utilizing these approaches is to attain the global minima for nonlinear functions without enhancement of computational power to enhance the accuracy of faults diagnosis and isolation. The proposed fault detection (FD) approaches are applied to quadrotor unmanned aerial vehicle (UAV), the simulated results demonstrate that the proposed approaches are having the capability of rapid faults diagnosis and isolation of sensors compared to the conventional neural network and the techniques used in the literature (Chen et al. in IEEE Trans Syst Man Cybern Syst Hum 46:260–270, 2016a, b, c, d; Payam et al. in J Intell Robot Syst 90:473–484, 2018). [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the stochastic stability of discrete time positive Markov jump nonlinear systems (PMJNS). Some definitions on stochastic stability for discrete time PMJNS are introduced first. Then, using the multiply max-separable Lyapunov function method, some stochastic stability criterions of discrete time PMJNS are provided, and some corresponding criterions are also provided for discrete time positive Markov jump linear systems (PMJLS). Different from previous conclusions that require subsystems to be stable or marginally stable, the obtained results allow some subsystems to be unstable. Based on the proposed criterions, the stochastic stability behavior of discrete time positive Markov jump systems can be obtained just from the algebraic properties of the system function and the probability characteristics of the Markov chain. To illustrate the main results, two simulation examples are provided at the end. [ABSTRACT FROM AUTHOR]

In many emerging scientific applications, the input of random initial time is often required for predicting and controlling the dynamics of real-order systems that include or does not include time-delays. In this work, we provide a new design for a class of random initial-time non-autonomous linear Caputo-type real-order time-delay systems that involves constant discrete delays. We first introduce a new comparison principle and a linear comparison lemma for Caputo derivative. By using the idea of comparison methodology and generalized Laplace transform, we develop some new elementary asymptotic theories and establish order-dependent and delay-independent conditions that give convergence of nontrivial solutions to such a class of system. One of the major challenges encountered in the investigation is discovering some simpler classes of autonomous linear real-order systems in bounding the coefficient matrices of a designed class of systems above by constant matrices in our assumptions. These assumptions including newly introduced matrices Λ , Θ k and G provide some fundamental key tools for an effective asymptotic analysis of newly designed class of systems. A typical theorem that we develop says that if all the diagonal entries of Λ become negative and every root of the characteristic equation associated with the matrix Λ + ∑ k = 1 m Θ k G lies in the open left-half complex plane, then it is possible to predict the limiting behavior of the designed class of systems. For a practical application of interest, we show how to synchronize the possible chaotic dynamics of the coupled nonlinear Duffing oscillators if the system starts at a negative initial time through some proposed results. [ABSTRACT FROM AUTHOR]

In a general domain, we discuss an initial value problem for the symmetry generalized M$$ M $$‐coupled system of fourth‐order parabolic equations with space–time‐dependent diffusion coefficients and mixed nonlinearities. For the power‐type nonlinearities containing the sub‐critical Sobolev exponents, we achieve the local existence, a unique continuation, global existence, or finite‐time blow‐up of solutions to the system in time‐weighted Sobolev space. For the logarithmic nonlinearities containing the critical Sobolev exponents, we investigate the local existence in time‐weighted Sobolev space. [ABSTRACT FROM AUTHOR]

In this paper, an output feedback scheme is proposed for a class of nonlinear systems with hysteresis input and sensor uncertainties. A hysteresis inverse function is applied to counteract the effect of the hysteresis input. The system nonlinearities and sensor uncertainties are dealt with by a dynamic gain, based on which a reduced-order observer and an output feedback controller are developed. It shows that the proposed scheme can globally stabilize the system. Finally, the effectiveness of the proposed scheme is verified by simulation. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the parameter estimation problems of feedback nonlinear systems. Combining the multi‐innovation identification theory with the negative gradient search, we derive a multi‐innovation gradient‐based iterative algorithm. In order to reduce the computational burden and further improve the parameter estimation accuracy, a decomposition multi‐innovation gradient‐based iterative algorithm is proposed by using the decomposition technique. The key is to transform an original system into two subsystems and to estimate the parameters of each subsystem, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we establish a result of unique continuation for a special two-dimensional nonlinear system that models the evolution of long water waves with small amplitude in the presence of surface tension. More precisely, we will show that if (η , Φ) = (η (x , y , t) , Φ (x , y , t)) is a solution of the nonlinear system, in a suitable function space, and (η , Φ) vanishes on an open subset Ω of R 2 × [ - T , T ] , then (η , Φ) ≡ 0 in the horizontal component of Ω. To state such property, we use a Carleman-type estimate for a differential operator L related to the system. We prove the Carleman estimate using a particular version of the well known Treves’ inequality. [ABSTRACT FROM AUTHOR]

This study conducts a comprehensive examination of the nonlinear propeller pendulum system's angular position control, utilizing three distinct control strategies: Proportional-Integral-Derivative (PID) controller, State Feedback (SF) controller, and Sliding Mode Control (SMC). In order to optimize the performance of each controller, Gorilla Troops Optimization (GTO) is employed to identify the optimal value of the controllers' design parameters. The dynamics of the system under each controller are simulated via MATLAB software, and the performance of the controlled system is quantitatively assessed utilizing the Integral Time of Absolute Error (ITAE). The resilience of the controllers under uncertainties is evaluated by introducing an external disturbance to the system. Simulation results indicate that the SMC, tuned by GTO, exceeds the performance of the other controllers in reducing the settling time, eliminating maximum overshoot, and minimizing the ITAE index. Moreover, under external disturbance, the SMC tuned by GTO demonstrates superior robustness compared to other controllers. [ABSTRACT FROM AUTHOR]

This article studies the data filtering‐based identification algorithms for a class of nonlinear system with autoregressive noise. By means of the data filtering technique and the hierarchical identification principle, the identification model is transformed into two sub‐identification models, and a filtering hierarchical gradient‐based iterative algorithm is proposed for improving parameter estimation accuracy and reducing computational burden. Meanwhile, to further improve the identification performance, the multi‐innovation identification theory is used to derived the filtering hierarchical multi‐innovation gradient‐based iterative algorithm. The gradient‐based iterative algorithm is given for comparison. The analysis shows that the filtering hierarchical gradient‐based iterative algorithm has smaller computational burden and can give more accurate parameter estimates than the gradient‐based iterative algorithm, and the filtering hierarchical multi‐innovation gradient‐based iterative algorithm can track time‐varying parameters based on the dynamical window data. Finally, the example part is provided to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Fractional terminal and super-twisting as two types of fractional sliding mode controller are addressed in the present paper. The proposed methodologies are planned for both the nonlinear fractional-order chaotic systems and the nonlinear factional model of Hovercraft. The suggested procedure guarantees the asymptotic stability of fractional-order chaotic systems based on Lyapunov stability theorem, by presenting a set of fractional-order laws. Compared to the previous studies that concentrate on sliding mode controllers with unwanted chattering phenomena, the proposed methodologies deal with chattering reduction of terminal sliding mode controller/super twisting to converge to desired value in finite time, consequently. The main advantages of the offered controllers are 1) closed-loop system stability, 2) robustness against external disturbances and uncertainties, 3) finite time zero-convergence of the output tracking error, and 4) chattering phenomena reduction. Finally, the simulation results show the performance of the approaches both on the chaotic and Hovercraft models. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The main aim of this work is to use the XOR-operation technique to find the common solutions for a new class of extended system of fuzzy ordered variational inclusions with its corresponding system of fuzzy ordered resolvent equations involving the $ \oplus $ operation and fixed point problems, which are slightly different from corresponding problems considered in several recent papers in the literature and are more advantageous. We establish that the system of fuzzy ordered variational inclusions is equivalent to a fixed point problem and a relationship between a system of fuzzy ordered variational inclusions and a system of fuzzy ordered resolvent equations is shown. We prove the existence of a common solution and discuss the convergence of the sequence of iterates generated by the algorithm for a considered problem. The iterative algorithm and results demonstrated in this article have witnessed, a significant improvement for many previously known results of this domain. Some examples are constructed in support of the main results.

Abstract The stochastic response of a multi‐degree‐of‐freedom nonlinear dynamical system is determined based on the recently developed Wiener path integral (WPI) technique. The system can be construed as a representative model of electrostatically coupled arrays of micromechanical oscillators, and relates to an experiment performed by Buks and Roukes. Compared to alternative modeling and solution treatments in the literature, the paper exhibits the following novelties. First, typically adopted linear, or higher‐order polynomial, approximations of the nonlinear electrostatic forces are circumvented. Second, for the first time, stochastic modeling is employed by considering a random excitation component representing the effect of diverse noise sources on the system dynamics. Third, the resulting high‐dimensional, nonlinear system of coupled stochastic differential equations governing the dynamics of the micromechanical array is solved based on the WPI technique for determining the response joint probability density function. Comparisons with pertinent Monte Carlo simulation data demonstrate a quite high degree of accuracy and computational efficiency exhibited by the WPI technique. Further, it is shown that the proposed model can capture, at least in a qualitative manner, the salient aspects of the frequency domain response of the associated experimental setup.

Considering the situation of limited resources in practical applications, it is significant to design control algorithms with high resource utilization rates for a class of nonlinear systems subject to mismatched disturbances. In contrast to common recursive methods, this paper proposes a novel event-triggered tracking control approach by co-designing the triggering event and the controller within a non-recursive design framework that combines disturbance estimation techniques and feedforward compensation strategies. Through rigorous Lyapunov stability analysis, the global boundedness of each state in the closed-loop system is demonstrated, and the absence of the Zeno phenomenon is further verified. A representative numerical simulation and a practical implementation for speed regulation of permanent magnet synchronous motor (PMSM) system confirm the effectiveness and simplicity of the proposed control strategy.

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

In this paper, a holistic nonlinear state-space model of a system with multiple converters is developed, where the converters correspond to the wind turbines in a wind farm and are equipped with grid-following control. A novel generalized methodology is developed, based on the number of the system’s converters, to compute the equilibrium points around which the model is linearized. This is a more solid approach compared with selecting operating points for linearizing the model or utilizing EMT simulation tools to estimate the system’s steady state. The dynamics of both the inner and outer control loops of the power converters are included, as well as the dynamics of the electrical elements of the system and the digital time delay, in order to study the dynamic issues in both high- and low-frequency ranges. The system’s stability is assessed through an eigenvalue-based stability analysis. A participation factor analysis is also used to give an insight into the interactions caused by the control topology of the converters. Time domain simulations and the corresponding frequency analysis are performed in order to validate the model for all the control interactions under study.

This paper deals with the problem of the estimation of non-uniformly nonlinear systems with time-varying delays in the state and input. In addition, the problem of the sampled output measurement is also been addressed. Thus, an observer design for a class of uncertain, non-uniformly nonlinear systems in the presence of time-varying delay is proposed. A continuous–discrete observer based on a high-gain approach is designed to achieve undelayed estimation. Thus, sufficient conditions to ensure the convergence of the observer are obtained. The analysis is based on a Lyapunov–Krasovskii functional, which shows that the bounded observation error depends on the sizes of the known upper delay and the upper sampling rate. The performance of the proposed algorithm is evaluated by considering a control-based observer for a two-degrees-of-freedom helicopter system with a known time-varying delay and sampled output measurements.

The application of the dynamical dampers in the mechanical systems, when the sources of stimulation are impossible to abolish, is one of the ways to fight against the harmful vibrations. The linear dynamical damper of nonlinear systems can compensate the force of stimulation in wide diapason of frequency. The parameters of dynamical system where dynamical damper exists more effectively are determined.

The rate of profit is a key element for understanding the movement of capitalism such as technological progress and economic crisis. Even though capitalists seek larger profit rates, there exists a tendency of stagnating or falling profit rates. According to Marx, the rate of profit would tend to decline in the long run as a result of technological progress, termed the law of tendential fall in the profit rate. This article introduces a novel approach based on the discrete-time feedback control mechanism to elucidate Marx’s theory. Specifically, this study presents a mechanism and conditions to show how the effort to maximize the profit rate, implemented by designing an “appropriate” control law under the sampling of fiscal years, eventually leads to the gradual decrease in the rate of profit in the long run.

This paper presents a comprehensive investigation of the generalized Schrödinger’s equation through the utilization of an efficient analytical method, enabling the derivation of a wide range of exact solutions. The novelty of this research lies in the development of a novel approach that combines simplicity and power to produce diverse categories of solutions within a unified framework. By employing this innovative methodology, the study contributes new insights into the properties of the model, expanding upon the existing literature. Additionally, the effectiveness of the proposed method is demonstrated through the presentation of graphical representations that enhance the understanding of the dynamic characteristics of the obtained results. Furthermore, the significance of this research extends beyond its theoretical contributions. The findings of this study have practical implications in various fields, including fluid mechanics, nonlinear optics, and plasma physics. The ability to determine soliton solutions for other partial equations using this methodology opens up opportunities for further advancements in these domains. Moreover, the presented results provide valuable insights into the behavior and properties of systems governed by generalized Schrödinger’s equation, offering potential applications in diverse areas of science and engineering.

A nonlinear filter is presented by using a formal linearization method and the extended Kalman filter (EKF) approach in this paper. A given nonlinear system is transformed into an augmented nonlinear system by considering the higher‐order polynomial terms of state as new additional states. And then, the EKF theory is applied to it well. Numerical experiments show the effectiveness of this new filter. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this article, an original event‐triggered model‐free adaptive control is presented for nonlinear systems with output constraints. Firstly, a compact form dynamic linear model for this nonlinear system is established. Based on the output saturated data, the pseudo partial derivative (PPD) parameter is designed to identify the linear model. Then, a novel event‐triggered mechanism is inserted into the controller. The mechanism will be activated only if the event‐triggered error satisfies the predefined condition; Otherwise, it keeps the last triggering state. The purpose is to alleviate the transmission burden and save the communication resources. Varying from the existing studies, the main innovation of this article is that the controller is reconstructed in virtue of saturated data and event‐triggered condition. The present algorithm convergence is proved. Finally, the simulation study illustrates the feasibility of the plan. [ABSTRACT FROM AUTHOR]

In this work, we investigate the problem of state estimation for a class of nonlinear systems subjected to randomly occurring measurement anomalies (ROMAs) without a priori statistic. To address the problem, first, a novel measurement model is constructed, in which the anomalous measurements and anomaly probability are modeled as Gaussian mixture distribution (GMD) and Beta distribution, respectively. Different from the existing researches assuming that the statistical information of anomalous measurements is known in advance, the model does not require a priori statistical knowledge of anomalous measurements. Moreover, by adaptive learning of the anomaly probability, the measurement model is identical with the classical cubature Kalman filter (CKF) in the absence of measurement anomalies. Then, the variational Bayesian inference (VBI) is employed to approximately calculate the joint posterior distribution of the system state and unknown parameters, and a robust filter is derived. Finally, the effectiveness of our filter is demonstrated by the numerical simulation. • To resist the ROMAs without a priori statistic, a novel measurement model is designed. Utilizing the VBI method, a new RCKF is designed. • In the absence of measurement anomalies, the proposed filter is consistent with standard CKF by adaptive learning of the anomaly probability. • Different from the existing results, the proposed filter is more accordant with the practical situation where the measurement anomaly is unknown. [ABSTRACT FROM AUTHOR]

The paper studies relationships (inclusions, coincidences, and noncoincidences) between classes of linear approximations that provide various properties of Lyapunov, Perron, and upper-limit stability or instability (from global to particular) of the zero solution to a differential system of arbitrary order. A complete set of noncoinciding stability classes is presented and some considerations are given for a similar description of instability classes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A review of methods for adaptive neural network control of actuators based on a stepper motor is presented. A model of a universal mechatronic module with a stepper motor is defined. The investigated field-oriented classical proportional-integral-derivative, neural network radial basis, and neural network sigmoidal adaptive motor control algorithms are described. The results of modeling the digital control system for the universal mechatronic module at nominal parameters and under the influence of parametric and functional uncertainties are demonstrated. [ABSTRACT FROM AUTHOR]

The main aim of this work is to use the XOR-operation technique to find the common solutions for a new class of extended system of fuzzy ordered variational inclusions with its corresponding system of fuzzy ordered resolvent equations involving the operation and fixed point problems, which are slightly different from corresponding problems considered in several recent papers in the literature and are more advantageous. We establish that the system of fuzzy ordered variational inclusions is equivalent to a fixed point problem and a relationship between a system of fuzzy ordered variational inclusions and a system of fuzzy ordered resolvent equations is shown. We prove the existence of a common solution and discuss the convergence of the sequence of iterates generated by the algorithm for a considered problem. The iterative algorithm and results demonstrated in this article have witnessed, a significant improvement for many previously known results of this domain. Some examples are constructed in support of the main results. [ABSTRACT FROM AUTHOR]

In this paper, we prove Liouville type theorem of positive weak solution of nonlinear system for Grushin operator. We give some integral inequalities, which combine the method of moving plane with the integral inequality to get the result for nonlinear system. [ABSTRACT FROM AUTHOR]

In this paper, the continuous nonlinear perturbed system is discussed. A sliding mode controller was designed using the reaching law technique. The main goal was to satisfy the state constraints in the form of linear combinations for the whole regulation process. We also took into account the control signal limitation and did not require that the external disturbances fulfill the matching conditions. Moreover, we aimed to ensure the fastest, monotonic, and finite-time convergence of the representative point to the sliding hyperplane. [ABSTRACT FROM AUTHOR]

As an option to deal with insulin-dependent disease, a recently commuted PD control strategy is designed and carefully analyzed for different clinic diabetic patients. This controller approach is mainly conceived to stabilize the glucose blood concentration in a diabetic patient around its basal value; hence, avoiding extreme situations such as hypoglycemia and hyperglycemia. This control strategy receives two inputs carefully tuned to actuate when the measured variable is out of a prescribed healthy zone. Therefore, one of these variables is invoked to decrease the glucose concentration to insulin injection, and the other is employed to increase the glucose absorption, both by using a proper PD controller. According to our numerical experiments, our controller approach performs well, even when there is an external disturbance in the controlled system. [ABSTRACT FROM AUTHOR]

The scientific advances in the machining process inculcate intelligence in computer numerical control (CNC), to enhance reliability and quality production. The study made in this article has been directed towards the metaheuristic‐based intelligent control of a nonlinear uncertain CNC machine for milling purposes. The presence of cutting force and frictional type constraints, along with parametric uncertainties, degrade the production quality in milling operations. Moreover, enhanced performance of milling operation can be achieved via intelligent control of position and velocity of machine table and tool. In this work, the black widow optimization algorithm (BWOA) driven proportional–integral–derivative (PID) and PI controllers are employed for the position and the velocity of machine table and tool, respectively. Servo and regulatory are the two important control problems that are considered, analysed, and addressed in this work. The most challenging task of simultaneous tuning of PI and PID controllers is also addressed in this brief. Finally, a vivid comparative analysis of five state‐of‐the‐art optimization techniques is also performed, and the obtained results demonstrate the superiority of the proposed control scheme. While dealing with the servo performance of x‐axis, the proposed controller gives a 100% improvement in overshoot and 61.54% improvement in settling time, and for regulatory performance, it gives a 98.45% improvement in undershoot. Moreover, for z‐axis control, the proposed controller gives a 100% improvement in overshoot and 61.54% improvement in settling time and for regulatory performance gives a 100% improvement in undershoot compared to published literature. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the identification problem of an output‐error nonlinear system with saturation and dead‐zone nonlinearity. Introducing a switching function and by means of the auxiliary model identification idea, an auxiliary model hierarchical least squares‐based iterative algorithm is proposed for estimating the parameters of the nonlinear system. Based on the hierarchical identification model, an auxiliary model hierarchical gradient‐based iterative algorithm is presented for the nonlinear system by utilizing the gradient search. In order to take full advantage of the system data, an auxiliary model hierarchical multi‐innovation gradient‐based iterative algorithm is derived for the nonlinear system according to the multi‐innovation identification theory. Finally, the numerical simulation results illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper concerns the problem of designing distributed control law for a class nonlinear system in which the measurement outputs of the system are distributed in different subsystems. It leads to a challenge that the states of the original systems cannot be completely reconstructed by any single subsystem. In order to solve this problem, distributed state observers and the distributed observer-based distributed control problem emerges as the times require. However, the distributed observers problem of the nonlinear systems is rarely studied, and the distributed control law formed by distributed nonlinear observers has hardly ever been studied up to now. To this end, this paper develops the distributed high-gain observers for a class of nonlinear systems. Unlike the previous several results, our study has the ability to deal with model uncertainty, and devotes itself to overcoming the problem that the separation principle is not tenable. In addition, based on the state estimate generated by the designed distributed observer, an output feedback control law formed by applying the state estimate is developed. Furthermore, a class of sufficient conditions is proved for guaranteeing the error dynamics of the distributed observer and the state trajectory of the closed-loop system to enter an arbitrary small invariant set around the origin. Finally, the simulation results verify the effectiveness of the proposed method. • This paper proposed distributed-observer-based distributed control law for Lipschitz nonlinear systems with model mismatch. Both the error of distributed observer and the states of the closed-loop system can converge to the arbitrary invariant set. • Compared to the existing literature, this paper overcomes the coexistence of control input, nonlinearity, and uncertainty, while the existing works do not fix the control input into account. Besides, the existence of model mismatch is also deliberated. • This paper includes the observer feedback part based on the system output without relying on the assumptions used in the existing studies concerning distributed-estimation-based distributed control framework. [ABSTRACT FROM AUTHOR]

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current study presents a data-driven integral sliding mode predictive control method for a category of discrete-time repetitive nonlinear systems. At first, a compact form of iterative dynamic linearization (IDL) technology is utilized to establish an IDL data model. Then, considering the time and iterative domain simultaneously, an iterative integral sliding mode surface is constructed to establish an iterative integral sliding mode controller. The stability of the presented control strategy is then demonstrated through a precise mathematical analysis. Furthermore, to further reduce the control error, an iterative integral sliding mode predictive control strategy is established using the model predictive control. Since the proposed method is a data-driven control scheme, it only employs the online I/O data for parameter estimation and controller design. The effectiveness and monotonic convergence of the proposed schemes are evaluated through simulations. Comparative results with the data-driven optimal iterative learning controller (DDOILC) and the enhanced DDOILC indicate that the presented controller can provide a faster convergence and less tracking error. [ABSTRACT FROM AUTHOR]

The driverless electric vehicle with electric wheels may generate the front wheel shimmy phenomenon more easily. How to mitigate or even eliminate the shimmy phenomenon by active control method is necessary. The current active shimmy control methods do not consider the sensor measurement error. But the sensor measurement error always exists in practice because of sensor design, manufacturing, and external disturbances. Therefore, this paper investigates this issue. Firstly, a one-degree-of-freedom shimmy model of the front wheel is built. The dynamic function of this model is obtained via Lagrange's theorem. Then, the shimmy control system with unknown sensor measurement error and nonlinearities is presented. A dual domination control method is proposed to construct a linear state observer as well as an output feedback control law for the shimmy control system. By the Lyapunov method, the designed controller is proved to stabilize the shimmy system globally asymptotically. Finally, simulations are carried out to verify the effectiveness of the one-degree-of-freedom shimmy model and proposed dual domination control method. [ABSTRACT FROM AUTHOR]

We establish compression-expansion type fixed point theorems for systems of operator inclusions with decomposable multivalued maps. The approach is vectorial allowing to localize individually the components of solutions and to obtain multiple solutions with multiplicity not necessarily concerned with all components of the solution. A general scheme of applicability of the theory is elaborated based on Harnack type inequalities and illustrated on systems of differential inclusions with one-dimentional ϕ-Laplacian. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of fixed‐time stabilization (FTS) for a class of second‐order nonlinear time‐delay systems. Using the fixed‐time stability theory and Lyapunov‐Krasovskii functional, a design scheme of fixed‐time stability for time‐delay systems is proposed. This scheme effectively solves the adverse impact of time‐delay on the system, and and ensure that the corresponding closed‐loop system is stable for a fixed time. The main benefit of the proposed control methods is that the influences of time‐delay state and nonlinear uncertainties can be effectively attenuated with the help of feedback method. Finally, the simulation is given to show the effectiveness of the design scheme. [ABSTRACT FROM AUTHOR]

The current investigation aims at the optimal control problem for discrete-time nonstrict-feedback nonlinear systems by invoking the reinforcement learning-based backstepping technique and neural networks. The dynamic-event-triggered control strategy introduced in this paper can alleviate the communication frequency between the actuator and controller. Based on the reinforcement learning strategy, actor–critic neural networks are employed to implement the n-order backstepping framework. Then, a neural network weight-updated algorithm is developed to minimize the computational burden and avoid the local optimal problem. Furthermore, a novel dynamic-event-triggered strategy is introduced, which can remarkably outperform the previously studied static-event-triggered strategy. Moreover, combined with the Lyapunov stability theory, all signals in the closed-loop system are strictly proven to be semiglobal uniformly ultimately bounded. Finally, the practicality of the offered control algorithms is further elucidated by the numerical simulation examples. [ABSTRACT FROM AUTHOR]

In view of the nonlinear problem of backlash caused by the gear transmission structure in the automatic pipe pile welding machine, an improved sliding mode controller design method based on the extended state observer is proposed. Firstly, the nonlinear and time-varying backlash model is equivalent to a global linearization model, and it is proved that the upper limit of the modeling error is bounded. For the situation with nonlinear input backlash and unknown external disturbance, a sliding mode controller and an improved sliding mode controller based on extended state observer are designed, respectively. The former can track the input signal asymptotically and stably, and has strong anti-interference ability, but it has large chattering. The latter uses the extended state observer to detect the unknown disturbance and directly compensates to the controller, which not only ensures the tracking error within the expected accuracy range, but also reduces chattering. The simulation results show that compared with the traditional sliding mode controller, the improved sliding mode controller system based on the extended state observer reaches the sliding mode surface faster and chatters less. [ABSTRACT FROM AUTHOR]