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Stiffness and accuracy are critical performance indicators of recirculating linear guideway which is widely used in a precision transmission mechanism. To reveal the varying stiffness behavior and dynamic motion accuracy, this paper proposes a motion stiffness model by considering ball recirculation, crowning geometry, and rail and carriage errors. The alternating carrying property of ball is mathematically expressed, and geometric errors of components are integrated into the ball contact deformation. Further, the load-deformation relationship includes the traction friction caused by ball sliding. The instantaneous impact during contact between ball and raceway is calculated. The impact position is determined when contact deformation is just equal to zero. Moreover, five-degree-of-freedom equation to describe dynamic motion errors is established by including the inertia force and dissipation term. Based on numerical simulations, position-dependent load distribution law, quasi-static stiffness, and motion accuracy are analyzed. The effectiveness of this work has been validated by comparing it with the result of published papers. Simulations show that motion stiffness and accuracy are significantly affected by the ball grouping and rail error, and are not sensitive to feed speed and instantaneous impact. This study could be extended to detect the assembly quality of rail from measured carriage error.

Most phase-locked loops (PLLs) available for single-phase applications have at least two delay operators, one for orthogonality and the other for disturbance rejection, such as the DC offset and harmonic filtering. Multi-delay operators complicate the PLL and increase its computational burden. Therefore, a new solution for grid synchronization with DC immunity in single-phase applications is introduced utilizing only a single delay operator. A deep insight into the derivation of the PLL’s small-signal model, main characteristics, stability analysis, and loop filter design are discussed in this paper. The main advantages of the proposed method are explained in terms of several performance indexes, such as settling time, steady-state error, and ripples in the estimated grid information. This paper also aims to provide optimal tuning and design guidelines for the loop filter from the large-signal model perspective, including nonlinearities, adopting a stochastic optimization method. The practical swarm optimization is nominated to locate the loop filter gains optimally offline. The results of the offline stochastic optimization are used to explicitly formulate an online PI-controller gains formula as a function of the delay factor. Simulation and experimental tests validate the proposed method with comparison to other methods.

This paper presents the design of a novel continuous robust controller for a nonholonomic mobile manipulator in the task space. The main objective of the designed controller is to compensate for the disturbance caused by an unknown time-varying input delay, while the end-effector of the manipulator accurately tracks the reference position trajectory. Notably, the designed robust controller is model-independent; thus, it does not rely on the nominal values of dynamic parameters. Moreover, the control development considers the uncertainties in the parameters of the mobile manipulator dynamics via an unknown time-varying additive disturbance in the dynamics. To guarantee the uniformly ultimate boundedness of the tracking error, the paper utilizes a novel Lyapunov-based stability analysis incorporating Lyapunov–Krasovskii functionals. The effectiveness of the designed controller for a mobile manipulator, composed of a differential drive mobile platform and a two-degree-of-freedom robot manipulator, has been confirmed via simulation results.

In the current era, renewable energy has emerged as a vital alternative to fossil fuels, driven by the repercussions of global warming and the depleting supply of fossil fuels. Among these alternative energies, wind energy is particularly noteworthy due to its minimal greenhouse gas emissions, cost-effectiveness, and widespread availability. Nonetheless, achieving efficient extraction of wind energy requires precise control of wind turbine operations to optimize power generation. This involves the utilization of different maximum power point tracking (MPPT) algorithms. This review paper extensively examines a variety of MPPT algorithms, classifying them into four main categories: indirect power control (IPC) algorithm, direct power control (DPC) algorithm, hybrid algorithm, and intelligent algorithm control techniques. The review explores the performance of conventional IPC and DPC algorithms, discussing and comparing them with modified conventional methods. Additionally, the hybrid approach, combining multiple MPPT algorithms to leverage benefits while mitigating drawbacks, is examined. Intelligent MPPT algorithms are discussed both independently and in hybrid configurations. The paper introduces a hybrid fractional-order intelligent MPPT algorithm, offering a detailed discussion and comparison with other intelligent algorithms. A meticulous comparison is conducted based on key parameters such as adaptability, computational complexity, efficiency, oscillation, overall expense, robustness, speed of convergence, storage, time response, wind speed measurement, and wind turbine characteristics. Acknowledging the exponential growth in wind energy systems and their increasing significance, this review paper aims to be an indispensable and technically advanced reference for future studies in the dynamic domain of MPPT algorithm control techniques for wind energy systems.

Modular multilevel converters (MMCs) are mostly used in active power filters (APFs) due to their high modularity and easy scalability. Furthermore, model predictive control (MPC) has become one of the preferred methods in many modern control applications due to its good control effect and strong robustness. Aiming at the problem that the computational load of the traditional MPC rises significantly with the increase in the number of submodules (SMs), this paper proposes an improved backward MPC strategy for modular multilevel active power filter (MMC-APF). This strategy is based on nearest level modulation (NLM) to reversely predict the number of SMs pre-inserted in the upper and lower bridge arms, then expands the number of SMs inserted in each phase from Nto [N− 1, N+ 1], and the optimal number of SMs finally inserted in the upper and lower bridge arms is determined by minimizing the circulating current. A sorting algorithm is used to obtain the switching states of each SM, while maintaining the balance of capacitor voltages and reducing the switching frequency. The proposed strategy does not need to design weighting factors and greatly reduces the computational load of MPC. Simulation results show that the control strategy proposed in this paper has a small computational load and has good performance in harmonic current compensation, verifying the effectiveness of the proposed control strategy.

This paper is based on a recent finding regarding the control of active suspension systems (ASS) with a non-ideal actuator using linear disturbance observer (LDO)-based sliding mode control (SMC) (Pusadkar et al. in J Sound Vib 442:428–444, 2019). The paper investigates the impact of actuator imperfections such as dead-zone and hysteresis, as well as parametric uncertainty and unknown road profiles, on ASS performance. However, the paper does not consider actuator saturation, which can lead to significant performance degradation. The analysis reveals that the LDO-based SMC violates the performance of ASS during actuator saturation. To address this issue, the paper proposes a new formulation of nonlinear disturbance compensator (NDC) specifically designed for ASS. This method prevents the actuator from becoming saturated, thereby avoiding performance degradation and extending the actuator’s life. The stability of NDC and ASS is demonstrated through proof. Finally, the proposed method is compared and verified using MATLAB/Simulink tool.

This paper focuses on the compensation of disturbances in linear dynamical systems with time-varying delays. The objective is to reduce the effect of any measured disturbances and restore the observations to their normal state by a final time T. The research paper presents a minimum energy problem describing the mathematical framework to obtain a convenient control ensuring the disturbance compensation. The presented application to the two-dimensional diffusion equation emphasizes the practical applicability and relevance of the proposed approach.

The one-phase brushless DC motor (BLDC) has become indispensable in-home appliances due to its high-power density, flexible control, and straightforward driving circuit, outperforming induction and universal motors. Additionally, it ensures higher efficiency across a wide range of speed-torque loads. This paper introduces a pioneering real-time control algorithm based on machine learning to enhance the BLDC motor’s overall performance compared to the traditional fuzzy-PID controller. A dynamic model of the BLDC motor is utilized to determine the EMF (electromotive force) and torque properties through finite element simulations conducted in the ANSYS/Maxwell environment. The targeted BLDC motor is driven by a space vector modulation inverter powered by a DC voltage source. The proposed machine learning-based control algorithm demonstrates superior performance over traditional methods under various load disturbances and reference speed variations, with overshoot/undershoot and settling time improvements of at least 60% and 46%, respectively. The enhanced performance was validated using a comprehensive dynamic model developed in the MATLAB environment and confirmed through an experimental setup.

In order to better balance the energy recovery and braking safety and stability in the composite braking process, this study takes the electromechanical composite braking system as the research object and proposed a composite braking control strategy based on different attachment coefficients. Firstly, the braking force distribution curves under various adhesion coefficients were designed based on the representative front and rear axle braking force distribution curves. On the basis of this, a fuzzy controller that can adjust the power of the electric mechanism is designed by using fuzzy algorithm considering the braking strength and other factors to improve energy recovery. Finally, a joint simulation using MATLAB/Simulink and AVL Cruise is performed. The results show that the control strategy can be applied to roads with varying of adhesion coefficients, which not only ensures the braking safety and stability, but also effectively improves the energy recovery. When the vehicle implements energy recovery on a high adhesion road surface, mode 1 of the control strategy in this paper increases the energy recovery by 301 kJ and 876 kJ, respectively, compared with the ideal braking force distribution strategy and the no energy recovery strategy; the battery SOC increases by 0.49% and 1.43%, respectively.

Many people face knee problems due to conditions like osteoarthritis, obesity, post-polio syndrome, strokes, ligament injuries, or accidents. To assist with movement, artificial knee exoskeleton systems are often used. This paper proposes an optimal proportional-integral-derivative (PID) controller for accurate control of the angular movement of such devices. Additionally, a sigmoidal class topper optimization (SCTO) technique is designed to optimally tune the PID controller for knee exoskeleton systems. The convergence performance of the SCTO algorithm is tested on various CEC 2005 benchmark functions and compared with existing methods. When applied to the knee exoskeleton system, the SCTO-based PID controller demonstrated improved control performance compared to other algorithms.

In the realm of infectious disease dynamics, stochasticity and behavioral adaptations play pivotal roles in shaping epidemic outcomes. This paper presents a novel stochastic SIRS (susceptible–infectious–recovered–susceptible) epidemic model that incorporates both behavioral changes among susceptible individuals and jump processes. By integrating dual general incidence functions and a Levy measure to accurately represent independent jump events, our model offers a comprehensive framework for understanding epidemic dynamics in real-world scenarios. We validate the model’s robustness through rigorous analysis of its well-posedness and establish a threshold that distinguishes between epidemic eradication and persistence. Numerical simulations, conducted using estimated parameters for herpes simplex virus type 2, verify the accuracy of this threshold and highlight its practical relevance in epidemic management. This work differs from previous research in Nguyen et al. (J Math Anal Appl 538:128361, 2024) , offering a more comprehensive framework and identifying optimal conditions.

This paper delves into the pressing issue of the maize streak plant epidemic, recognizing its significance in the context of global food security and sustainable agriculture. To address this concern, we propose a novel approach utilizing a discrete fractional model to accurately simulate the dynamic spread of the virus within maize populations. Our model introduces a groundbreaking integration of fractional calculus, enabling a more precise representation of the epidemic’s progression over time. We rigorously examine the biological plausibility of our model and conduct thorough sensitivity analyses to elucidate key factors influencing the epidemic dynamics. Additionally, stability assessments are performed to gauge the robustness of our model under various scenarios, highlighting its adaptability and reliability in predicting real-world outcomes. We also introduce an innovative linear feedback control mechanism tailored to our model framework, designed to optimize virus control strategies and mitigate the impact of maize streak virus outbreaks. By leveraging control theory principles, our work provides a strategic advantage in designing effective interventions, contributing to the broader goal of safeguarding agricultural productivity. Numerical simulations complement theoretical findings, providing valuable insights into the efficacy of our proposed model and control strategy in practical scenarios, thereby reinforcing the novel contributions of this study to the field of epidemic modeling.

This paper presents a novel approach to stabilizing two-wheeled balancing mobile robots (2WBMR) using hierarchical sliding mode control (HSMC) combined with sliding mode control (SMC). The 2WBMR system, an inherently unstable underactuated mechanical system, is modeled and analyzed for its dynamic properties. The controller of the 2WBMR is divided into two main components due to the independent control of state variables: the HSMC controller performs velocity control and stabilizes the vehicle body, while the SMC controller manages the vehicle’s tilt angle. Moreover, our simulations demonstrate that the HSMC-SMC approach significantly outperforms the SMC method, particularly in terms of response time and error reduction during stable control of position, pitch angle, and rotation angle. Additionally, a rapidly exploring random tree (RRT) algorithm is implemented for trajectory planning, enabling the robot to navigate complex environments. Simulation results demonstrate that the proposed control strategy effectively stabilizes and trajectory tracking for the 2WBMR.

An artificial ventilator is crucial in medical emergencies, providing severely ill patients with sufficient oxygen to reduce their breathing effort. For critical patients, clinicians must set various parameters on the ventilator to ensure safe and comfortable ventilation. Accurate tracking of the patient’s breathing profile by the ventilator is essential for synchronization, reducing the patient’s workload and effort. Designing an effective ventilator controller requires proper system modeling and a clear understanding of its purpose. This paper offers a brief review of modeling, physiological and clinical issues, and state-of-the-art advanced control practices, guiding future control design for ventilators. It also presents detailed ventilator modeling and parameters from the literature, along with a comprehensive review of traditional and soft-computing-based control approaches.

Modeling of real-world phenomena frequently contains uncertainties mainly due to impreciseness and ambiguity. In this context, fuzzy sets can be an important tool for mitigate them. In turn, fractional calculus has shown be relevant to improve physical models and to provide more accurate solutions for problems in science and engineering. Fractional integrals combined with the fuzzy theory are an important tool to explore the aforementioned uncertainties whenever we desire to model and simulate distinct phenomena. In this paper, the generalized fuzzy fractional integral operator with exponential kernels is introduced as an intriguing generalization of the fuzzy fractional integral operator with exponential kernels, and its significant properties are examined. Several Hermite–Hadamard- (ℋℋ-) and Pachpatte-type integral inclusion relations are established via fuzzy harmonic convexity using these newly proposed operators in fuzzy fractional calculus. Specifically, enhanced ℋℋ-generalized fractional integral inclusions for the fuzzy convexity are suggested. Based on variations in the parameter γand differentiable function ξ, theoretical representations of the results are provided to determine the accuracy of the obtained inclusion relations in the study. Additionally, interval-valued fractional inclusions are exceptional cases of this article's new fuzzy fractional operators where interval-valued mappings and fuzzy number mappings are integrable, respectively. The results contributes to the existing literature and may be useful in better understanding of automatic control models, artificial intelligence, computer science, physics, biosystems, mathematical economics, or econophysics, among other domains.

This paper presents a novel memristor-based magnetized Hopfield neural network (HNN) designed for generating and controlling hidden scroll chaotic attractors with reduced input complexity. The integration of memristor introduces nonlinear dynamics, enhancing the network’s ability to produce complex chaotic behaviour. By magnetizing the Hopfield model, we further extend the system’s versatility and stability in representing intricate patterns inherent to chaotic systems. The proposed model is analysed using some of the numerical methods like bifurcation and phase plots. The proposed dynamics exhibits periodic, chaotic and quasiperiodic behaviour. Further, the attractor of the proposed memristive HNN is controlled using three approaches; (i) using its parameter control, (ii) normal integral sliding mode control SMC (NISMC) and (iii) adaptive barrier function-based integral SMC (ABFISMC). It is observed that the states are converging to origin just by changing the value of a parameter of the system in the absence of the disturbance. During externally added bounded unknown disturbances, ABFISMC performs better than the NISMC. In both the cases, only two control inputs are used out of four states.

This paper investigates state estimation problems of linear discrete systems with uncertain parameters, deterministic external inputs, d-step state delay, and random measurement losses. Firstly, based on the state augmentation method, the original time-delay system is modified to a non-time-delay system. On this basis, the expectation minimization based robust state estimation is put forward, and the detailed derivation process of the estimator is described. At the same time, a new recursive form of estimation error pseudo-covariance matrix is derived. It can be seen that the robust estimator derived is similar to the intermittent observation of the Kalman filter in form, but the estimator parameters need to be adjusted when the plant output measurement arrives. Besides, it is proved that the pseudo-covariance matrix of estimation errors converges to the corresponding stationary distribution with probability one under the condition that some matrices are controllable and observable. Finally, the effectiveness of this robust state estimation algorithm is demonstrated through numerical simulations.

Designing a robust control algorithm for PMSM motors to effectively handle model uncertainties, regardless of their magnitude, is a significant challenge. Many sliding mode control methods determine the uncertainty band while proving and designing the controller. However, in some cases, the determined uncertainty band may be smaller than the actual model uncertainty, leading to reduced control accuracy. If system faults occur, the level of uncertainty increases, further complicating the control process. To address this issue, this paper presents a new control technique according to an estimator and sliding mode control. Specifically, it presents a control method based on sliding terminal control, which offers significantly faster convergence compared to conventional sliding methods. Additionally, an estimator based on the sliding method is developed in this article to estimate the state variables and provide references to the controller. This approach reduces the overall system cost and enables the estimation of disturbances and even faults that may otherwise compromise the performance of robust controllers. Notably, when a suitable sliding surface is chosen during design, the chattering phenomena have been mitigated to an acceptable level, in contrast to conventional sliding methods. A number of real-world tests are used to evaluate the suggested control structure’s performance, which demonstrates its effectiveness and superior performance in contrast to the traditional sliding technique.

In this paper, the pth moment asymptotic stability (PMAS) problem is investigated for stochastic complex networked control systems driven by Lévy noise (SCNCSL). Based on the node systems of SCNCSL, we construct a new Lyapunov function. Several sufficient criteria are obtained to ensure the PMAS of SCNCSL. These criteria are closely related to each node system and coupling form function of SCNCSL. Stability criteria for a class of SCNCSL with linear functions are also provided. In addition, we provide a specific system model of SCNCSL and obtain its numerical stability conditions. Ultimately, three numerical examples are put forward to show the availability of the theoretical results.

Time difference of arrival (TDOA) is an essential technology in localization, communication, and navigation. However, it remains a significant challenge to increase the precision of TDOA in a low signal-to-noise ratio (SNR) situation. Therefore, this paper proposed a phase difference-reassigned synchro-squeezing transform (PDTR) algorithm to offer a precise TDOA estimator. Firstly, the partial derivative of the frequency variable of the short-term cross-power spectrum technique is employed to obtain two-dimensional delay information. Secondly, this spectrum can be reordered, utilizing the delay, and time–frequency points across the frequency axis can be accumulated to generate one-dimensional delay information. Finally, the delay associated with the most robust energy is selected to obtain the TDOA. Simulations and experiments are conducted to validate the efficacy and robustness of the algorithm comprehensively. Initially, simulation validation is executed, which confirms the algorithm's effectiveness. Subsequently, the algorithm's robustness in low-SNR environments is experimentally tested. The results demonstrate that the stability of the PDRT algorithm changes slowly as the SNR decreases from 0 to − 20 dB, but the stability of the comparable algorithm decreases sharply.

As society transitions away from fossil fuels toward renewable energy sources, finding alternatives that are reliable becomes imperative. Waste-to-energy bioprocesses are promising options due to their ability to operate independently of weather conditions or time of day, making them sustainable and potentially lucrative solutions. This paper proposes an updated bioeconomic model, based on previous research (Cherkaoui Dekkaki et al. in Math Methods Appl Sci 45(1):468-482, 2022; Cherkaoui Dekkaki and Djema in American Control Conference pp. 2135-2140, 2023), to analyze investment in waste-to-energy technology and its associated valorization of waste treatment. This conceptual model represents a generic framework for studying waste-to-energy processes. By taking technological constraints into account, the updated model aims to optimize energy production processes and establish a sustainable business model. Indeed, using dynamic modeling, investment and valorization strategies will be evaluated through a maximization criterion over a finite time horizon, which is stated as an optimal control problem. The effective control strategies are then determined using the Pontryagin’s maximum principle. Furthermore, direct optimization methods are applied to derive and validate the effectiveness of the obtained optimal strategy. This approach allows for a thorough evaluation of the economic and environmental impacts in waste-to-energy technologies, identifying optimal investment and valorization strategies to promote sustainable waste management practices. In addition, a sensitivity analysis is conducted to evaluate the robustness of the studied model, and provide insights into biotechnological limitations. Finally, an extensive numerical exploration of the turnpike-like features that characterize the optimal long-term behavior of the investment problem is widely discussed.

Nonlinearity constraints are inherent in all physical systems and can impact the system output. The windup issue occurs when actuators reach their limits, causing a disparity between the system input and the controller output. To eliminate or minimize the impact of saturation, controllers are designed with anti-windup techniques. This paper proposes a new target loop-based simple analytical design of a fractional-order proportional integral (FOPI) anti-windup controller for non-integer-order (NIO) processes with time delay. Explicit tuning rules in terms of plant parameters are established to meet user-defined criteria such as phase margin (ϕm) and maximum sensitivity (Ms). To check the performance and robustness of the proposed control law, case studies are conducted and compared with recently developed control laws. The robustness of the proposed controller is examined with parameter variations. Lastly, real-time validation of the proposed control approach is carried out in a two-tank level loop.

Bistable nonlinear energy sinks have been widely studied in energy harvesting and vibration absorption systems. The precise identification of local bistable nonlinear stiffness force is of significance to predicting and controlling the dynamic responses. Sparse identification is a very popular data-driven method that has been widely used in nonlinear dynamics identification. However, the accuracy and efficiency of sparse identification of multi-degree-of-freedom bistable systems are still not yet investigated. Besides, the significance of physical information of basis functions in sparse identification has not been numerically validated. This paper established the model of Bistable Nonlinear Energy Sink Chains (BENSC) and the vibration absorption characteristic is numerically analyzed. The Sparse Identification of Nonlinear Dynamics Systems (SINDy) and physics-informed SINDy are conducted on two-degree-of-freedom to ten-degree-of-freedom BENSC systems. The results show that with the increase in noise levels, the identification accuracy will be greatly decreased using SINDy with third-order polynomial basis functions. Besides, the computing time is exponentially increased when the number of degrees of freedom increases. However, the physics-informed sparse identification with basis functions a prior still keeps an accuracy of around 0.5% even under the noise level of 20 dB. The identification efficiency greatly improved compared with SINDy using third-order polynomial basis functions. The results give new insights into the accuracy and efficiency issues of sparse identification applied to BNESC systems under noise.

Honeycomb sandwich beams are one of the most basic engineering structures and are highly susceptible to external excitation, which may cause amplified vibration phenomena. In response to this concern, support boundary measures with superior elastic properties are implemented to effectively mitigate transverse vibration interference. This paper explores the influence of beam geometry nonlinearity on structural vibration and introduces the concept of vibration transferability. The multi-mode resonant frequency of the elastic support boundary beam is analyzed. The significant impact of support stiffness symmetry on vibration response has been explored. Use approximate analytical methods and numerical calculation methods to mutually verify the research results. Observe how different parameters affect the steady-state response. The transmission law of multi-mode resonance response on elastic sandwich beams is analyzed. Compared with the vibration transmission properties of rigid boundaries, it has been confirmed that elastic supported boundaries have significant advantages in vibration isolation performance. Compared to amplitude, vibration transferability can more accurately depict vibration characteristics in high-order resonance states. This article will further explore the isolation performance of honeycomb sandwich beams and objectively reflect their isolation effect through the vibration transmission rate, thus helping to improve the overall seismic performance of engineering structures.

This paper aims to improve the efficiency of wind turbines by generating maximum power during partial load operation and under different wind speed characteristics. In most cases, the optimal control based on the maximum power point tracking (MPPT) of the wind turbine is used to optimize the effectiveness of the energy capture. In this study, the Tip Speed Ratio (TSR) method based on PI and adaptive fuzzy logic controllers is performed to apply the MPPT algorithm on a wind turbine based on a two-mass drive-train. Additionally, the effective wind speed is estimated by using an Extended Kalman Filter (EKF) based on Backward Euler Approximation (BEA) for continuous state space discretization. In fact, the controllers of the TSR method are tested based on the estimated wind speed obtained from the EKF output instead of the measurement obtained from the anemometer. This latter is due to the fact that the anemometer measurements represent the turbulent wind speed and cannot represent the wind speed upstream of the wind turbine rotor blades. In this paper, the efficiency of EKF is tested under different wind speed characteristics by varying the mean wind speed and the turbulence intensity. To compare the results obtained, the covariance and the correlation coefficient are measured each 5 ms to compute the similarity ratio between the measured and estimated wind speed. Then, the efficiency of TSR controllers on the wind turbine model based on a two-mass drive-train coupled with a common spring and damper is highlighted. It is found that the computed covariance and the correlation coefficient demonstrate that the EKF has high accuracy with a 98% similarity between the measured and estimated wind speeds. Further, the TSR based on adaptive fuzzy logic and PI controllers has an effectiveness to improve the efficiency of wind turbines by extracting maximum power from the wind energy. However, the comparison results illustrate that the TSR based on adaptive fuzzy logic controller can capture slightly more wind energy than the other one under different wind speed characteristics.

The attainable control performance of the linear time-invariant (LTI) system, controlled using the linear active disturbance rejection control (LADRC) technique, relies on its design selections, such as structure selection, order selection, tuning parameters selection, and tuning methodology selection. Each of the above LADRC design selections has an array of possibilities. So, stating reasonable choices in each design and determining whether or not the stated combination of LADRC design selection could attain the system’s targeted performance without involving a trial-and-error approach is challenging. Hence, mapping the relationship between the qualitative LADRC design selections and the quantitative control performance is needed. Based on the necessity, a validation formulation is proposed in this paper with the insight of a unique tuning methodology. The idea of the tuning methodology begins with the requirement of stating a reasonable choice in each of the LADRC design selections. Using that stated combination of LADRC design selection, the formulation applies them to the LTI system and finds the attainable control performance range. Within that range, whether or not the targeted performance gets captured is the final check of the formulation. If captured, the stated combination of LADRC design selection is justifiable to attain the targeted requirement; if not, making changes in the stated selections is justifiable. The simulation section of this paper provides the implications of the proposed validation formulation through the numerical LTI system models controlled using the stated LADRC design selections of this paper’s interest.

The established guidance laws in literature are generally designed to capture the target while considering all the states of the interceptor model to be measurable. This paper demonstrates the use of an input output observer for the unmeasured state of the missile-target model with semi-global exponential results. A well-established guidance law called pure proportional navigation law is incorporated in this paper to demonstrate the application of the proposed observer. The missile’s angle of attack is the unmeasured state, which also appears non-linearly in the remaining two kinematic equations of the missile. The occurrence of the unmeasured state in the nonlinear form makes it more challenging to theoretically prove its convergence. The Lyapunov analysis is used to show the convergence of the observed state. Different scenarios consisting of the tail chase and head-on engagements with non-maneuvering and maneuvering targets are considered in the simulations to establish the effectiveness of the proposed guidance strategy.

Vortex-induced vibration is a common phenomenon observed in many engineering applications and it is detrimental to the performances and health of the system. It is, therefore, imperative for engineers to make suitable design modifications or arrange for some type of control device to mitigate such oscillations. In this paper, the performance of the acceleration feedback control is compared with that of the simple passive vibration absorber. The effect of time-delay in the feedback loop is also investigated. The acceleration of the primary system is measured and passed through a second-order compensator. The active absorber is designed by setting the filter frequency same as the natural frequency of vibration and the optimum filter damping is numerically obtained. Nonlinear analysis is performed using the Describing Function method and the results are validated using direct numerical simulation performed in MATLAB Simulink. In the present paper, vortex shedding frequencies are selected from two different regions, one with vortex shedding frequency less than the natural frequency of the system and other having vortex shedding frequency higher than the natural frequency of the system. It is observed that the acceleration feedback control can effectively reduce the amplitude of vibration to a great extent. It is also observed that the amplitude of the system changes marginally (up to a certain value of time-delay) in the pre-locking and locking region. However, the effect of time-delay in post-locking zone is detrimental. Beyond a certain value of the time-delay, the amplitude becomes large and even the system may become unstable.

In this paper, a bifurcation tree of an independent period-3 motion to chaos in a flexible nonlinear rotor system is developed semi-analytically. The period-3 motion was traditionally called the subharmonic periodic motion of order-1/3, but one did not achieve the corresponding solutions yet. Herein, stable and unstable periodic solutions on the bifurcation tree in the flexible rotor system are achieved and the corresponding stability is analyzed by eigenvalue analysis. Harmonic frequency-amplitude characteristics for periodic motions on the bifurcation tree are presented. For comparison of analytical and numerical results, numerical simulation of periodic motions is completed. Phase trajectories, displacement orbits and velocity planes are illustrated, and harmonic amplitude spectrums of period-3 and period-6 motions are presented to show harmonic terms effects. Such studies presented in this paper help one better understand the subharmonic periodic oscillation of order-1/3 in nonlinear rotor systems.

Control of the bubble radius is essential in many medical and industrial applications. In this paper, the nonlinear governing dynamic of single spherical bubble in a perfect incompressible liquid is introduced. In continuation, the state space form of the nonlinear dynamic is presented and then observability and controllability are checked by Lie derivative concepts and finally the linearized state space is obtained around its equilibrium point. Second part of this paper is devoted to designing control strategies for the nonlinear governing dynamics of bubble, where the ultrasonic pressure plays the role of control input and the output is radius of bubble which is intended to follow desired radius in order to prevent from bubble collapse phenomenon. A novel robust control is proposed to ensure the desired level of performance in the presence of parametric uncertainty. In this method, the stability of the closed loop system is guaranteed by Lyapunov theorem and the stabilizing gain is achieved by Linear Matrix Inequality (LMI) optimization approach. For comparison, Linear Quadratic Regulator method is selected which is a very classical approach. The optimal feedback gain is also achieved through LMI approach. Simulation results indicate the effectiveness and efficiency of the robust over optimal controller.

The paper is dealing with the concept of positive real and strictly positive real properties of linear time-invariant systems. Although strict positive realness is the most important subset of positive real systems and extensively used in stabilizing control systems, there are inconsistencies in the literature for its necessary and sufficient conditions. Also, many frequency properties of such systems are less attended by the scholars. In this paper, a comprehensive discussion on definitions, properties, lemmas, and theorems related to multi-input multi-output positive real and strictly positive real systems will be presented. Further, several frequency domain properties are presented to characterize these functions. Also, three equivalent high frequency condition presented in the literature for strictly positive real systems are compared. The main results have been supported via several illustrative examples.

As a nonlinear passive vibration damper, the Nonlinear Energy Sink (NES) has garnered significant attention due to its wide vibration reduction bandwidth and high efficiency. However, when subjected to harmonic excitations of the primary system with an intensity surpassing a certain threshold, the system generates frequency islands (FI), leading to the ineffectiveness of NES in vibration reduction. This paper establishes mechanical models for multiple NES cells coupled to linear oscillators and investigates the dynamic characteristics of the system and the vibration reduction performance of NES cells using Complexification-Averaging (CxA) and Runge–Kutta methods. The results indicate that NES cells can significantly suppress the generation of FI in the system, achieving noticeably higher vibration reduction efficiency compared to a single NES of the same mass. With an increasing number of cells, the range where FI exist continuously diminishes until they disappear. Generally, the vibration reduction efficiency on the primary oscillator improves as the number of NES cells increases. Furthermore, an increased number of NES cells can effectively widen the range of excitation amplitudes for effective vibration reduction. Therefore, the proposed cellular approach in this paper effectively enhances the adaptability and vibration reduction potential of NES, overcoming the issue of ineffectiveness caused by high excitation intensities, thereby contributing to its practical engineering applications.

The severity of global climate change is exemplified by the significant increase in extreme precipitation events, leading to an urgent need for accurate rainfall prediction models to mitigate flood disasters that adversely affect economic and social development. With the rapid progress of machine learning in the big data era, novel solutions to regression problems are being proposed. In this paper, we try to construct and evaluate different rainfall prediction models based on specific humidity, relative humidity, horizontal and vertical water vapor flux, and lifting index as variables, using four classic machine learning algorithms: linear regression, random forest regression, support vector regression, and Bayesian ridge regression. The grid search method is employed for hyperparameter tuning, significantly improving the models' prediction accuracy and generalization ability. Evaluation of the predictive performance of the models on nine typical regions in China, including Zhengzhou, Beijing, and Chengdu, demonstrates that the random forest regression model has the highest predictive accuracy, with an average fitting degree of 0.8 or above, followed by support vector regression and Bayesian ridge regression models. Conversely, the linear regression model may have the poorest predictive performance. Therefore, the random forest regression model is recommended for future precipitation prediction, providing a valuable solution to various regression problems. The appropriate selection of variables for prediction and grid search for hyperparameter tuning are possibly the highlights of this paper.

This paper presents an innovative controller inspired by human emotional intelligence and learning mechanisms called Brain Emotional Learning-Based Intelligent Controller (BELBIC), introduced and applied to enhance the operation of a wind energy conversion system based on Doubly Fed Induction Generator (DFIG). The BELBIC control approach combines emotional states and reinforcement learning concepts to optimize control actions effectively. This paper focuses on BELBIC control of the rotor side converter and grid side converter of the DFIG system. The controller's performance is systematically evaluated under various scenarios, such as constant wind speed, variable wind speed, step change in reactive power, and voltage sag, and the performance of the above scheme is compared with that of the classical PI controller. The simulation results of the DFIG system, employing a BELBIC-based control strategy, show excellent dynamic performance in regulating the output powers of the doubly fed induction generator in grid-connected mode, thereby validating the effectiveness of the proposed control approach.

This paper presents the effect of modeling uncertainty of a lithium ion battery pack on the accuracies of state of charge (SOC) and state of power (SOP) estimates. The battery pack SOC is derived from the SOCs of all parallel cell modules in the pack, which is computed using a sequential estimation process. SOC and SOP estimates are essential for optimizing the energy management of hybrid electric vehicles (HEV). Two model types, an average model (AM) and a hysteresis model (HM), are considered here. The HM captures the hysteresis phenomenon, prominently observed in lithium ferro-phosphate cells commonly used in HEVs. This paper first demonstrates the accuracies of SOC and SOP estimates for AM and HM for laboratory experimental data. The feasibility of on-board implementation of the scheme is demonstrated through a closed-loop vehicle-level hardware-in-the-loop simulation. It is shown that estimates obtained using the HM result in an improved overall energy economy, as well as improved operational limits for batteries, and the underlying reasons, are revealed from the improved choice of operating points enabled by the improved estimates.

This paper introduces an optimal control system to control the depth and pitch angle of an Autonomous Undersea Vehicle (AUV). The paper considers the design of a 4th-order AUV model using state space technique to represent the behavior of depth and pitch angle states. A closed-loop control scheme-based Linear Quadratic Regulator (LQR) is designed to manipulate the depth and pitch states by reducing a cost function defined for this purpose. In this paper, LQR method is employed to guarantee the control of different states of AUV model at the same time. In addition, a new control algorithm, called Matrix Decision Control (MDC) algorithm, is presented to ensure that the search algorithm always achieves positive semidefinite Qmatrix which is crucial for LQR design. Simulation results are obtained by integrating the LQR approach to the model with different control parameters. Results are benchmarked by comparing the results of LQR based PSO and MDC algorithms with the response from optimal and robust controllers in the presence of white noise and wide range of system uncertainties. Simulation results show that the proposed control scheme delivers better performance and strong robustness in terms of reducing the overshoot, improving the pitch behavior, and covering wide range of system uncertainties.

In this paper, a novel robust gain scheduled adaptive control design is proposed for a class of fractional order linear parameter varying (LPV) systems with time-varying delay. The control design is based on a set of predefined PID controllers with an adaptive switching law. The considered class of fractional second-order-like systems with delay is able to represent a wide range of industrial and practical processes. The controller structure includes a Smith predictor, whereas the switching law is based only on the models’ outputs. The main contribution of this paper is the generalization of the gain scheduling adaptive control strategy to delayed fractional order LPV systems using a set of fractional order PID regulators. Stability analysis of the proposed controller is performed. A numerical implementation example on a piece of a sewer system illustrates the efficiency of its efficiency and robustness.

In this paper, a backstepping controller based on equilibrium manifold linearization (EMI) model and a finite time disturbance observer is proposed for power line inspection (PLI) robots. Firstly, the state equations of PLI robot are established, and the nonlinear system model is linearized at the nominal equilibrium point. Then, the EMI model is established to expand the working range of the controlled system. In this paper, a finite time disturbance observer is used to observe and compensate the external disturbance of the PLI robot system to ensure that the observation error approaches zero in a finite time. Finally, a backstepping controller is designed based on the EMI model and finite time disturbance observer. The simulation results show the disturbance approximation ability and control effectiveness of the control scheme.

In order to lessen the dynamic reactions of liquid storage tanks, the nonlinear inertial amplifier friction bearings are introduced in this paper. The liquid storage tanks’ governing equations of motion are obtained by applying Newton’s second law. The governing equations of motion of isolated structures are linearized for each nonlinear component using the statistical linearisation technique. The H2and H∞optimisation approaches are used to improve the natural frequency and damping ratio of the isolators. A frequency response function is created to determine the frequency domain responses of the separated structures. We compare the vibration reduction capabilities of innovative isolators with those of a standard damper. Taking into account the maximal dynamic responses of sloshing and impulsive masses of the isolated liquid storage tanks, the dynamic response reduction capacity of H2optimised innovative isolators is therefore 57.03 % and 61.4 % superior to H2optimised traditional base isolators. Furthermore, taking into account the maximal dynamic responses of sloshing and impulsive masses of the isolated liquid storage tanks, the dynamic response reduction capacity of H∞optimised innovative isolators is 22.2 % and 31.74 % superior to H∞optimised traditional base isolators. All of the results that were obtained make mathematical sense.

This paper explores the axial control and stability challenges of vertical shaft hybrid magnetic bearings (HMB) under varying rotor speeds. HMBs are considered an optimal solution, as the advantages of both active and passive magnetic bearings are blended. However, while effective radial control is provided through permanent magnets, axial stability is struggled with, especially at higher speeds. These axial instability issues are addressed by implementing an adaptive sliding mode controller (ASMC). Although promising performance is shown by ASMC at low speeds, significant high steady-state error and control force issues are encountered as speed increases. To overcome this, a fuzzy logic-based gain scheduler is incorporated into the ASMC. Parameters are optimally selected by this gain scheduler across different speed ranges, enhancing the performance of the standard ASMC. This results in a more robust and efficient control mechanism for the HMB. Given the significant nonlinearity and parameter uncertainties in the proposed HMB system, it is crucial for the controller’s robustness to be evaluated. This involves variations in electromagnet stiffness, permanent magnet stiffness, and the resistance and inductance of the electromagnet coil being analyzed. Excellent performance and robustness are demonstrated by the proposed fuzzy-based ASMC, maintaining low steady-state error and reduced control force requirements.

This paper introduces a novel adaptive and robust control methodology for an interleaved DC-DC buck converter with coupled inductors. Initially, a dynamical average model of the system is presented. Subsequently, a robust adaptive controller is developed, ensuring stable voltage reference tracking in the presence of system uncertainties. The controller design employs a multi-step approach, utilizing a back-stepping controller. The effectiveness of the proposed controller is evaluated through MATLAB/Simulink simulations and a Plexim RT-Box hardware-in-the-loop implementation.

In this paper, a new adaptive nonlinear robust lead compensation is proposed for controlling a class of uncertain nonlinear systems that are afflicted with a high level of nonlinearity, uncertainty, and sensitivity to outside disturbances. The procedure for control consists of a section synthesized based on adaptive feedback linearization to ensure that the influences of unknown variables, nonlinearities, and disturbances are canceled through adaptation. Then, to control the linear nominal system, an effective lead compensator is developed. As a result, the primary aim is to reach desirable stability and performance for uncertain nonlinear systems regardless of perturbation consequences, where the lead compensator is in charge of recognizing the transient criteria of the system response, and adaptive nonlinear control is used to reduce the ultimate bound around the stability point during the steady state. The results of the simulation reveal the ability of the proposed control to attain the desired goal by successfully satisfying the control objectives of stability, performance, and tracking qualities with proper robustness, persuasive control, and uncomplicated computational labor.

This paper is concerned with new approaches to the stability analysis of delayed neural networks. By modifying the non-orthogonal polynomial-based integral inequality (NPII), a new delay-product functional (DPF) is formulated. On the basis of the proposed DPF, a Lyapunov–Krasovskii functional (LKF) is constructed where a new state introduced in the second-order Bessel–Legendre integral inequality (BLII) is included in augmented vectors of Lyapunov matrix. On account of this proposed LKF, the delay-dependent criterion is introduced in terms of linear matrix inequalities (LMIs) for stability analysis of neural networks time-varying delay. Two commonly used numerical examples are considered for demonstration purpose to test the efficacy of the proposed stability criterion.

In this paper, a new identification method is developed for Wiener–Hammerstein systems that contain memory nonlinearity of backlash type. The latter is flanked by two linear transfer functions that may be parametric or not. The proposed identification method is carried out in two stages. Firstly, the parameters (the lateral borders) of memory operator are identified using a set of constant inputs. In the second stage, an identification method based on Fourier analysis is developed to determine the frequency responses of both transfer functions. The performances of the proposed identification method are highlighted by simulation results. Finally, experimental application has been established to show the effectiveness of this method.

In this paper, a new four-dimensional (4D) memristive system is presented by introducing a memristor equation to a 3D chaotic system. Firstly, the basic characteristics including existence and stability of equilibrium points, and Kaplan–Yoke dimension for the memristive system are analyzed from a theoretical perspective. The description of the 4D memristive system in the integer-order and the fractional-order cases is given. In both cases, the dynamics with the variation of a system parameter or a derivative order is studied by the numerical simulations. The results show that the integer-order and the fractional-order memristive systems have rich dynamics. Secondly, in order to realize the synchronization for the memristive system, two adaptive synchronization schemes are designed, namely, the identical structure and the different structure synchronization. Numerical simulation results show that the designed synchronization controllers are effective. Finally, a novel image encryption algorithm is expanded to the image encryption for color images based on the memristive system. The simulation results demonstrate the image encryption method based on the memristive system has a perfect encryption effect for the color images.

The paper considers the problem of identifying a non-autonomous nonlinear system of ordinary differential equations by use of time series of observed variables. Identification of non-autonomous system was done by multiple use of autonomous system identification method within moving window. As a result, unknown structure of equations, values of constant coefficients, and time series of variable input actions of the system under study were found. It has been analytically and experimentally shown that there can be a time shift between the results of the numerical identification and the true values of input actions. The proposed method does not impose restrictions on the number of input actions in the system under the study, and even allows to identify the system with all coefficients that are time dependent. The condition for the applicability of the method is the low rate of change of input actions.

Fatigue failure of hydraulic pipes, resulting from unstable alternating stress, is a common type of failure for hydraulic systems. In engineering, the reality of working with perfect straight pipes is nonexistent. Instead, the fatigue life of shallow curved pipes is an essential topic worth careful consideration and discussion. The purpose of this paper is to investigate the effect of dual-frequency excitation on the fatigue life of hydraulic pipes with an initial curved shape exposed to unstable alternating stress. Firstly, the governing equation of the shallow curved pipe is established by the generalized Hamilton principle. Secondly, the nonlinear partial differential integral equation is discretized into a set of nonlinearly coupled ordinary differential equations (ODEs) through Galerkin method. The effectiveness of GM (Galerkin method) is verified using DQEM (Differential quadrature element method). Upon this, the influence of the frequency difference between two frequencies on the forced vibration of the pipe midpoint is discussed. The phenomena of bifurcation and chaos in the transverse vibration of a system near the first natural frequency are analyzed exhaustively. The analysis shows that a low gap of frequency causes chaotic behavior. The Fourier series expansion technique is used to decouple the unstable alternating stress generated when chaos occurs. Based on Pairs theory, the linear combination mechanism of pipe life under decoupling stress is proposed, and the fatigue life of pipe under chaos is predicted. Finally, the potential impact of chaos on pipe fatigue life is investigated. The results of this research project expand the theoretical research field of pipe dynamics, provide effective research methods for a precise understanding of the entire lifecycle of pipe structures, and thus yield valuable insights and guidance for the reliability design, system management, maintenance of pipes, and fault prediction of pipe systems.

The electric vehicle charger brings convenience to people's daily travel, but faces the problems of slow charging speed and low charging efficiency. This paper proposes a cascaded single-phase PFC (Power Factor Correction) solution based on repetitive PI (Proportional Integral) compound control. The main circuit of the system adopts cascaded multilevel structure of low-voltage devices. It can effectively increase the system power density of the charger, reduce the voltage required for a single switch, and improve the power factor of the system output. The system control adopts double-loop control mode, in which the voltage outer loop adopts PI control and the current inner loop adopts repetitive PI compound control. It uses the characteristics of precise tracking of repetitive control and fast response of PI control to reduce the distortion rate of system input current and effectively increase the dynamic stability of the system. The effectiveness of the proposed scheme is verified by simulation and experimental results.

This paper presents an observer-based fault-tolerant control design for a class of sampled-data nonlinear systems with the multiplicative fault of actuators and bounded disturbances. In addition, a generalized Lipschitz condition on system nonlinearities is considered to acquire less conservative results. First, the continuous-time system is discretized by Euler approximate discrete-time model. Next, based on this discrete-time model, an observer for the estimation of continuous time states is proposed. Then, sufficient conditions are derived for constructing a nonlinear observer-based fault-tolerant controller for sampled-data system. It is worth to note that the observer and control gains are achieved by LMI-based methods. Furthermore, the control law depends on a constant gain that is easy to set and implement in practice. Finally, the effectiveness of proposed approach is illustrated by a single-link manipulator system.

The aim of this paper is to design a longitudinal motion controller for electric vehicles (EVs) and hybrid electric vehicles (HEVs) fitted with BLDC (Brushless Direct Current) in-wheel motors (IWMs). The developed controller relies on a highly complex nonlinear model, serving as a foundation for the creation of longitudinal vehicle movement simulators. This model incorporates the different nonlinear factors that arise in normal driving situations, such as rolling resistance, aerodynamic effects, and numerous phenomena occurring at the tire/road contact, which are captured by the Pacejka tire model. The backstepping design approach constitutes the basis of the controller’s design. It has been formally demonstrated that the controller developed achieves its objectives, such as close control of vehicle and driving wheel speeds in longitudinal motion.