Search Results (246)

To reveal the long-term deformation behavior of high-volume fly ash-based backfill under continuous mining and backfilling, a fly ash–cement backfill material with 73.0% fly ash content was developed, and creep characteristic tests considering initial damage were conducted. The results demonstrate that: (1) A calculation method for the initial damage of backfill based on stress–strain hysteresis loop cycles is proposed, with cumulative characteristics of initial damage across mining phases analyzed; (2) Creep behaviors of backfill affected by initial damage are investigated, revealing the weakening effect of initial damage on long-term bearing capacity; (3) An enhanced, nonlinear plastic damage element is developed, enabling the construction of an HKBN constitutive model capable of characterizing the complete creep behavior of backfill materials. The research establishes a theoretical framework for engineering applications of backfill materials with early-age strength below 5 MPa, while significantly enhancing the utilization efficiency of coal-based solid wastes. Full article

This study presents a quasi-static optimization framework for the pressure-based control of a multi-segment soft continuum manipulator. The proposed method circumvents traditional curvature and length-based modeling by directly identifying the quasi-static input–output relationship between actuator pressures and the 6-DoF end-effector pose. Experimental data were collected using a high-frequency electromagnetic tracking system under monotonic pressurization to minimize hysteresis effects. Transfer functions were identified for each coordinate–actuator pair using the System Identification Toolbox in MATLAB, and optimal actuator pressures were computed analytically by solving a constrained quadratic program via a manual active-set method. The resulting control strategy achieved sub-millimeter positioning error while minimizing the number of actuators engaged. The approach is computationally efficient, sensor-minimal, and fully implementable in open-loop settings. Despite certain limitations due to sensor nonlinearity and actuator hysteresis, the method provides a robust foundation for feedforward control and the real-time deployment of soft robots in quasi-static tasks. Full article

In the power system of a polysilicon reduction furnace, especially during the silicon rod growth process, the issue of insufficient temperature control accuracy arises due to the system’s nonlinear and time-varying characteristics. To address this challenge, a dual-loop control system is proposed, combining model-free adaptive control (MFAC) with an improved PID controller. The inner loop utilizes a hysteresis PID controller for dynamic current regulation, ensuring fast and accurate current adjustments. Meanwhile, the outer loop employs a hybrid MFAC-based improved PID algorithm to optimize the temperature tracking performance, achieving precise temperature control even in the presence of system uncertainties. The MFAC component is adaptive and does not require a system model, while the improved PID enhances stability and reduces the response time. Simulation results demonstrate that this hybrid control strategy significantly improves the system’s performance, achieving faster response times, smaller steady-state errors, and notable improvements in the uniformity of polysilicon deposition, which is critical for high-quality silicon rod growth. The proposed system enhances both efficiency and accuracy in industrial applications. Furthermore, applying the dual-loop model to actual industrial products further validated its effectiveness. The experimental results show that the dual-loop model closely approximates the polysilicon production model, confirming that dual-loop control can allow the system to rapidly and accurately reach the set values. Full article

Magnetorheological (MR) foams represent a class of smart materials with unique tunable viscoelastic properties when subjected to external magnetic fields. Combining porous structures with embedded magnetic particles, these materials address challenges such as leakage and sedimentation, typically encountered in conventional MR fluids while offering advantages like lightweight design, acoustic absorption, high energy harvesting capability, and tailored mechanical responses. Despite their potential, challenges such as non-uniform particle dispersion, limited durability under cyclic loads, and suboptimal magneto-mechanical coupling continue to hinder their broader adoption. This review systematically addresses these issues by evaluating the synthesis methods (ex situ vs. in situ), microstructural design strategies, and the role of magnetic particle alignment under varying curing conditions. Special attention is given to the influence of material composition—including matrix types, magnetic fillers, and additives—on the mechanical and magnetorheological behaviors. While the primary focus of this review is on MR foams, relevant studies on MR elastomers, which share fundamental principles, are also considered to provide a broader context. Recent advancements are also discussed, including the growing use of artificial intelligence (AI) to predict the rheological and magneto-mechanical behavior of MR materials, model complex device responses, and optimize material composition and processing conditions. AI applications in MR systems range from estimating shear stress, viscosity, and storage/loss moduli to analyzing nonlinear hysteresis, magnetostriction, and mixed-mode loading behavior. These data-driven approaches offer powerful new capabilities for material design and performance optimization, helping overcome long-standing limitations in conventional modeling techniques. Despite significant progress in MR foams, several challenges remain to be addressed, including achieving uniform particle dispersion, enhancing viscoelastic performance (storage modulus and MR effect), and improving durability under cyclic loading. Addressing these issues is essential for unlocking the full potential of MR foams in demanding applications where consistent performance, mechanical reliability, and long-term stability are crucial for safety, effectiveness, and operational longevity. By bridging experimental methods, theoretical modeling, and AI-driven design, this work identifies pathways toward enhancing the functionality and reliability of MR foams for applications in vibration damping, energy harvesting, biomedical devices, and soft robotics. Full article

This paper presents a modeling framework for hysteretically nonlinear piezoelectric composite beams using functional differential equations (FDEs). While linear piezoelectric models are well established, they fail to capture the complex nonlinear behaviors that emerge at higher electric field strengths, particularly history-dependent hysteresis effects. This paper develops a cascade model that integrates a high-dimensional linear piezoelectric composite beam representation with a nonlinear Krasnosel’skii–Pokrovskii (KP) hysteresis operator. The resulting system is formulated using a state-space model where the input voltage undergoes a history-dependent transformation. Through modal expansion and discretization of the Preisach plane, we derive a tractable numerical implementation that preserves essential nonlinear phenomena. Numerical investigations demonstrate how system parameters, including the input voltage amplitude, and hysteresis parameters significantly influence the dynamic response, particularly the shape and amplitude of limit cycles. The results reveal that while the model accurately captures memory-dependent nonlinearities, it depends on numerous real and distributed parameters, highlighting the need for efficient reduced-order modeling approaches. This work provides a foundation for understanding and predicting the complex behavior of piezoelectric systems with hysteresis, with potential applications in vibration control, energy harvesting, and precision actuation. Full article

An active rotor with trailing edge flaps (TEFs) is an effective method for helicopter vibration elimination. The nonlinear hysteresis of piezoelectric actuators used to drive TEFs can adversely affect helicopter vibration control performance. In this paper, a hysteresis modeling and compensation study is performed for piezoelectric actuators used in TEFs. Firstly, the hysteresis characteristics of a rhombic frame actuator with input voltages at different frequencies are investigated by bench-top tests. Subsequently, the Bouc–Wen model is adopted to establish the hysteresis model of the piezoelectric actuator, with its parameters identified through the particle swarm optimization (PSO) algorithm. Experimental results demonstrate that the proposed model is capable of accurately capturing the hysteresis phenomenon of the piezoelectric actuator within the frequency range of 10–60 Hz. Finally, a compound control regime is established by integrating inverse Bouc–Wen model control with fuzzy PID feedback control. The experimental results indicate that the developed compound control regime can significantly suppress the piezoelectric actuator hysteresis of TEFs within the frequency bandwidth of 10–60 Hz, which lays the foundation for improving the vibration control performance of the active rotor with TEFs in the future. Full article

This study introduces a rigid–flexible coupled constant force actuator integrated with Active Disturbance Rejection Control (ADRC) to tackle the rigidity–compliance trade-off in precision force-sensitive applications. The actuator utilizes compliant hinges to decrease contact stiffness by three orders of magnitude (${10}^6\to {10}^3 \mathrm{N}/\mathrm{m}$), facilitating effective force management through millimeter-scale placement (0.1∼1 mm) and inherently mitigating high-frequency disturbances. The ADRC framework, augmented by an Extended State Observer (ESO), dynamically assesses and compensates for internal nonlinearities (such as friction hysteresis) and external disturbances without necessitating accurate system models. Experimental results indicate enhanced performance compared to PID control: under dynamic disturbances, force deviations are limited to $\pm 0.2 \mathrm{N}$ with a 98.5% reduction in mean absolute error, a 96.3% increase in settling speed, and 99% suppression of oscillations. The co-design of mechanical compliance with model-free control addresses the constraints of traditional high-stiffness systems, providing a scalable solution for industrial robots, compliant material processing, and medical device operations. Validation of the prototype under sinusoidal perturbations demonstrates reliable force regulation (settling time $<0.56 \mathrm{s}$, errors $<0.5 \mathrm{N}$), underscoring its relevance in dynamic situations. This study integrates theoretical innovation with experimental precision, enhancing intelligent manufacturing systems via adaptive control and structural synergy. Full article

Resistive angle sensors are widely used due to their simple signal conditioning circuits and high cost-effectiveness. This paper presents a resistive angle sensor based on a rotary potentiometer, designed to offer a measurement range of 180° for low-cost angle measurement in industrial automation and electromagnetic interference (EMI)-sensitive applications. The sensor features a specially designed signal conditioning circuit and mechanical housing. Experimental results show that it exhibits excellent linearity and temperature stability over a wide temperature range of −20 °C to 60 °C, with a zero-temperature drift of approximately 0.004°/°C. For the nonlinearity and hysteresis caused by unavoidable friction and manufacturing tolerances between the transmission mechanism and rotary potentiometer, an adaptive linear neuron (ADALINE) technique based on the α-least mean square (α-LMS) algorithm was implemented for software compensation. The results show that the percentage nonlinearity error was reduced from the original 4.413% to 0.182%, and the percentage hysteresis error was decreased from the original 4.061% to 0.404%. The research results of this paper offer valuable insight for high-precision resistive angle sensors. Full article

Multi-fingered dexterous hands provide superior dexterity in complex manipulation tasks due to their high degrees of freedom (DOFs) and biomimetic structures. Inspired by the anatomical structure of human tendons and muscles, numerous robotic hands powered by pneumatic artificial muscles (PAMs) have been created to replicate the compliant and adaptable features of biological hands. Nonetheless, PAMs have inherent nonlinear and hysteresis behaviors that create considerable challenges to achieving real-time control accuracy and stability in dexterous hands. In order to address these challenges, this paper proposes a hierarchical control architecture that employs a fuzzy PID strategy to optimize the nonlinear control of pneumatic artificial muscles (PAMs). The FPGA-based hardware integrates a multi-channel digital-to-analog converter (DAC) and a multiplexed acquisition module, facilitating the independent actuation of 20 PAMs and the real-time monitoring of 20 joints. The software implements a fuzzy PID algorithm that dynamically adjusts PID parameters based on both the error and the error rate, thereby effectively managing the nonlinear behaviors of the hand. Experimental results demonstrate that the designed control system achieves high precision in controlling the angle of a single finger joint, with errors maintained within ±1°. In scenarios involving multi-finger cooperative grasping and biomimetic motion demonstrations, the system exhibits excellent synchronization and real-time performance. These results validate the efficacy of the fuzzy PID control strategy and confirm that the proposed system fulfills the precision and stability requirements for complex operational tasks, providing robust support for the application of PAM-driven multi-fingered dexterous hands. Full article

Hysteresis is a nonlinear phenomenon found in many physical systems, including soft viscoelastic actuators, where it poses significant challenges to their application and performance. Consequently, developing accurate hysteresis models is essential for the effective design and optimization of soft actuators. Moreover, a reliable model can be used to design compensators that mitigate the negative effects of hysteresis, improving closed-loop control accuracy and expanding the applicability of soft actuators in robotics. Physics-based approaches for modeling hysteresis in soft actuators offer valuable insights into the underlying material behavior. Nevertheless, they are often highly complex, making them impractical for real-world applications. Instead, phenomenological models provide a more feasible solution by representing hysteresis through input–output mappings based on experimental data. To effectively fit these phenomenological models, it is essential to rely on sensing data collected from real actuators. In this context, the primary objective of this work is a comprehensive comparative evaluation of the efficiency and performance of representative phenomenological hysteresis models (e.g., Bouc–Wen and Prandtl-Ishlinskii) using experimental data obtained from a pneumatic bending actuator made of a viscoelastic material. This evaluation suggests that the Generalized Prandtl–Ishlinskii model achieves the highest modeling accuracy, while the Preisach model with a probabilistic density function formulation excels in terms of parameter compactness. Full article

This study addresses the challenge of accurately modeling the dynamic behavior of industrial robots for precision manufacturing applications. Using a comprehensive experimental approach with modal impulse hammer testing and triaxial acceleration measurements, 360 frequency response functions were recorded along orthogonal measurement paths for a KUKA KR10 robot. Two dynamic models with different parameter dimensions (12-parameter and 24-parameter) were developed in Matlab/Simscape, and their parameters were identified using genetic algorithm optimization. The KUKA KR10 features Harmonic Drives at each joint, whose high transmission ratio and zero backlash characteristics significantly influence rotational dynamics and allow for meaningful static structural measurements. Objective functions based on the Frequency Response Assurance Criterion (FRAC) and Root Mean Square Error (RMSE) metrics were employed, utilizing a frequency-dependent weighting function. The performance of the models was evaluated across different robot configurations and frequency ranges. The 24-parameter model demonstrated significantly superior performance, achieving 70% overall average Global FRAC in the limited frequency range (≤200 Hz) compared to 41% for the 12-parameter model when optimized using a representative subset of 9 measurement points. Both models showed substantially better performance in the limited frequency range than in the full spectrum. This research provides a validated methodology for dynamic characterization of industrial robots and demonstrates that higher-dimensional models, incorporating transverse joint compliance, can accurately represent robot dynamics up to approximately 200 Hz. Future work will investigate nonlinear effects such as torsional stiffness hysteresis, particularly relevant for Harmonic Drive systems. Full article

Smart tools are limited by actuation–sensing integration and structural redundancy, making it difficult to achieve compactness, ultra-precision feed, and immediate feedback. This paper proposes a self-sensing giant magnetostrictive actuator-based turning tool (SSGMT), which enables simultaneous actuation and output sensing without external sensors. A multi-objective optimization model is first established to determine the key design parameters of the SSGMT to improve magnetic transfer efficiency, system compactness, and sensing signal quality. Then, a dynamic hysteresis model with a Hammerstein structure is developed to capture its nonlinear characteristics. To ensure accurate positioning and a robust response, a hybrid control strategy combining feedforward compensation and adaptive feedback is implemented. The SSGMT is experimentally validated through a series of tests including self-sensing displacement accuracy and trajectory tracking under various frequencies and temperatures. The prototype achieves nanometer-level resolution, stable output, and precise tracking across different operating conditions. These results confirm the feasibility and effectiveness of integrating actuation and sensing in one structure, providing a promising solution for the application of smart turning tools. Full article

The additive manufacturing of piezoresistive sensors offers several advantages, such as the elimination of assembly or installation steps, enabling integration into complex parts precisely where desired, and compatibility with soft robotics applications. Previous studies have demonstrated that several characteristics of additively manufactured sensors, such as their resistance and sensitivity, are significantly affected by the selected printing parameters. This work seeks to further the understanding of the relationships between process parameters, material, sensor design, and the resulting sensor characteristics. To this end, sensors made from two materials, with differing printing layer heights, infill angles, and thicknesses, are characterized under cyclic tensile loading. For these sensors, the nonlinearity, hysteresis, and drift are analyzed. The findings indicate that both nonlinearity and hysteresis are significantly affected by the material choice, as well as the selected parameters. Notably, parameters that affect the sensitivity of the sensor, e.g., the infill angle, can have significant indirect effects on the nonlinearity and hysteresis errors. Through correct parameter selection, nonlinearity errors can be reduced by up to 30.7% or 25.3%, depending on the material used. The hysteresis error can be reduced by up to 38.7% or 23.8%, depending on the material. The drift over multiple cycles is found to be strongly material dependent, but can also be affected by the process parameters, e.g., the infill angle. Understanding the interactions between material, design, process, and the resulting sensor characteristics provides valuable insights for the successful design and additive manufacturing of piezoresistive sensors. Full article

Fast steering mirrors (FSMs), actuated by piezoelectric ceramics, play pivotal roles in satellite laser communication, distinguished by their high bandwidth and fast responsiveness, thereby facilitating the precise pointing and robust tracking of laser beams. However, the dynamic performance of FSMs is notably impaired by the hysteresis nonlinearity inherent in piezoelectric ceramics. Under dynamic conditions, rate-dependent hysteresis models and Hammerstein models are predominantly employed to characterize hysteresis nonlinearity. By combining the advantages of these two models, a hysteresis model termed modified Hammerstein-like (MHL) model is proposed. This model integrates an input time delay, a rate-dependent hysteresis term, and a linear dynamic term in a cascaded structure, effectively capturing the dynamic characteristics of hysteresis systems across a broad frequency range. Additionally, a composite control strategy is tailored for the MHL model which consists of a feedforward compensator based on a rate-dependent hysteresis inverse model and a proportional–integral (PI) controller for closed-loop regulation. Experimental results demonstrate the effectiveness of the proposed modeling and composite control methods. Full article

The optimal design methodology for a Maxwell–Coulomb friction damper is proposed to minimize the resonant vibration of dynamic structures. The simple Coulomb friction damper has the problem of zero or little damping effect of the vibration of the spring–mass dynamic system at resonance. This problem is solved in the case of the Maxwell–Coulomb friction damper, which is formed by combining a Coulomb friction damper with a spring element in series. However, the design and analysis of the Maxwell–Coulomb friction damper become much more complicated. The optimal design methodology for this nonlinear damper is proposed in this article. The nonlinear equations of motion of the proposed damper are modelled, and its hysteresis loop can be constructed by combining four different cases of stick–slide motion. Motion responses of the turbine blade with the proposed damper are solved by a central difference solver. Optimal paths of damping and stiffness ratios are determined by the central difference Newton search method. The optimal experimental design is ascertained using a prototype damper test. Close correlation with its numerical simulations is observed in our hysteresis loop comparison. The performance of the proposed damper is also compared to that of a viscous damper in the seismic response design of adjacent single-story buildings. Full article