Flux-switching wound field machines (FS-WFMs) are gaining traction for electrified systems due to their high torque density, magnet-free design, and fault tolerance. However, rotor eccentricity—classified as static, dynamic, or mixed—induces torque ripple, unbalanced magnetic pull, and back-EMF distortion, compromising performance. This work presents a fault-aware optimization framework that combines sensitivity analysis, multi-objective genetic algorithm (MOGA), and Design for Six Sigma (DFSS) to enhance robustness under eccentricity faults. Using 2D transient finite element analysis (FEA), key design variables were identified as dominant factors influencing electromagnetic behavior. MOGA optimization was employed to maximize average torque and minimize torque ripple, while DFSS accounted for geometric variations through Monte Carlo simulations. Results show the DFSS-optimized design reduced torque ripple by over 30% and limited total harmonic distortion to below 2.7% across all eccentricity conditions. Back-EMF distortion remained within 2 V of the healthy case. The proposed approach outperforms conventional methods by embedding fault conditions directly into the design loop, enabling robust FS-WFM performance for critical applications.

This paper presents a robust and scalable multiobjective optimization framework for Flux-Switching Wound Field Machines (FSWFMs), integrating Design for Six Sigma (DFSS), surrogate modeling via Response Surface Methodology (RSM), and a Multi-Objective Sequential Optimization Method (MSOM). The proposed framework simultaneously minimizes torque ripple, maximizes efficiency, and ensures robustness under uncertainty. Sensitivity analysis identified key design variables—including rotor outer diameter, tooth width, and excitation current—thereby reducing the design space dimensionality and enhancing computational efficiency. Finite Element Analysis (FEA) was employed to generate training data, while the surrogate models guided iterative refinement. The DFSS-MSOM method achieved 90.4% efficiency, average torque of 53.4 Nm, and significantly reduced torque ripple to 4.57%, compared to 9.86% in the deterministic baseline. Notably, the design achieved zero probability of failure (PoF = 0.00), confirming six-sigma quality and high manufacturing consistency. The final hypervolume convergence error was 3.59%, validating the effectiveness of the optimization trajectory. This approach achieved a 56.8% reduction in FEA calls and demonstrates high scalability and adaptability to industrial electric drive systems. Its alignment with Industry 5.0 goals makes it well-suited for next-generation smart grid, e-mobility, and energy-efficient propulsion applications.

The inherent nonlinear and memory-dependent input-output characteristics of piezoelectric actuators pose challenges to the precision of piezoelectric positioning systems. In order to solve this problem, this paper firstly transforms the Jiles-Atherton (JA) model into a neural network structure, designs the Jiles-Atherton neural network (JANN), and combines JANN with nonlinear autoregressive exogenous input (NARX) neural network. A hybrid JA-NARX neural network model is proposed for the first time. This model has the advantages of simple structure, high modeling accuracy, and good interpretability. The effectiveness of the proposed JA-NARX neural network model is validated through a series of experiments, specifically assessing its capacity to accurately capture rate-dependent and asymmetric hysteresis characteristics. The results show that although the proposed neural network model has fewer layers and relatively simple structure, it can realize the high-precision modeling of piezoelectric hysteresis dynamics at a lower computational cost. The experimental data shows that, under the excitation of 60 Hz input signal, the model's PV error only accounts for 0.82% of the full scale range, and the modeling performance is far superior to other models.

The continuous electromechanical deformation of dielectric elastomer actuators (DEAs) suffers from rate-dependent viscoelasticity, mechanical vibration, and configuration dependency, making the generalized dynamic modeling and precise control elusive. In this work, we present a generalized motion control framework for DEAs capable of accommodating different configurations, materials and degrees of freedom (DOFs). First, a generalized, control-enabling dynamic model is developed for DEAs by taking both nonlinear electromechanical coupling, mechanical vibration and rate-dependent viscoelasticity into consideration. Further, a state observer is introduced to predict the unobservable viscoelasticity. Then, an enhanced exponential reaching law-based sliding-mode controller (EERLSMC) is proposed to minimize the viscoelasticity of DEAs. Its stability is also proved mathematically. The experimental results obtained for different DEAs (four configurations, two materials, and multi-DOFs) demonstrate that our dynamic model can precisely describe their complex dynamic responses and the EERLSMC can achieve precise tracking control; verifying the generality and versatility of our motion control framework.

This article presents a novel Artificial Intelligence (AI) workflow to enhance drilling performance by mitigating the adverse impact of drill-string vibrations on drilling efficiency. The study employs three supervised machine learning (ML) algorithms, namely the Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), and Regression Decision Tree (DTR), to train models for bit rotation (Bit RPM), rate of penetration (ROP), and torque. These models combine to form a digital twin for a drilling system and are validated through extensive cross-validation procedures against actual drilling parameters using field data. The combined SVR - Bit RPM model is then used to categorize torsional vibrations and constrain optimized parameter selection using the Particle Swarm Optimization block (PSO). The SVR-ROP model is integrated with a PSO under two constraints: Stick Slip Index (SSI<0.05) and Depth of Cut (DOC<5 mm) to further improve torsional stability. Simulations predict a 43% increase in ROP and torsional stability on average when the optimized parameters WOB and RPM are applied. This would avoid the need to trip in/out to change the bit, and the drilling time can be reduced from 66 to 31 h. The findings of this study illustrate the system's competency in determining optimal drilling parameters and boosting drilling efficiency. Integrating AI techniques offers valuable insights and practical solutions for drilling optimization, particularly in terms of saving drilling time and improving the ROP, which increases potential savings.

A two-wheeled self-balancing robot is a statically unstable non-linear system with strong coupling dynamics. Common practices in the development of control systems for such robots are either to linearise the region of application to be used with linear controllers or to use complex nonlinear controllers such as Fuzzy logic, Sliding Mode, and Neural Networks. Nonetheless, in this paper, we are proposing a novel to this field concept of switching control that would adjust its approach depending on the evaluation of the current states. The performance of the proposed controller was assessed against exemplary solely linear and solely non-linear controllers in simulated tests. The tested were evaluated against dynamic criteria (distance traveled, max. angular deviation, etc.), control criteria (settling time, % overshoot, etc.), and environmental criterion of energy consumption. The results showed an interesting behavior of the proposed controller, with superior performance in many cases.

Odd-harmonic repetitive control (ORC) has been successfully applied to improve the triangular trajectory tracking performance of nanopositioners. However, the conventional ORC tends to amplify the tracking errors at frequencies other than the odd-harmonic components, mainly the even harmonics of the fundamentals of the intended triangular trajectory to be tracked. Due to the influence from the hysteresis nonlinearity of the piezoelectric actuator, this would result in significant tracking errors. To overcome this limitation, this article proposes an enhanced odd-harmonic repetitive control (EORC) using the spectrum-selection filtering scheme to improve the loop-shaping property of the ORC. This effectively eliminates the problem of amplifying the tracking errors while preserving the advantages of the conventional ORC, such as fast convergence speed and low computation cost. The EORC is combined with a proportional–integral tracking controller to improve the tracking performance. The controller design, stability analysis, and performance evaluation are presented. The experimental results demonstrating the effectiveness of the proposed EORC-based control scheme are presented showing the excellent tracking of triangular trajectories with fundamental frequencies up to 1000 Hz. Moreover, a reduction in rms tracking errors by up to 52% is achieved.Note to Practitioners—The high-speed atomic force microscopy (AFM) plays an increasingly vital role in observing and manipulating objects at the nanoscale. In the raster scanning of AFMs, the most challenging issue is the triangular trajectory tracking of nanopositioning stages with high precision. Although the odd-harmonic repetitive control (ORC) has been successfully applied to improve the tracking performance, the conventional ORC would amplify the tracking errors distributed at the frequencies other than the odd harmonics, especially those at the even harmonics, resulting from the inherent complicated hysteresis nonlinearity. This problem of amplifying the tracking errors would lead to larger tracking errors, deteriorating the performance of AFM imaging. To address this issue, this article proposes an enhanced ORC using the spectrum-selection filtering scheme to improve the loop-shaping property of the ORC, so as to eliminate the problem of amplifying the tracking errors while preserving the advantages of the conventional ORC, such as fast convergence speed and low computation cost. The experimental results show that, with this simple modification, the positioning errors are reduced greatly, all-the-while preserving the benefits of the conventional ORC scheme– fast convergence speed and low computation cost. In terms of tracking accuracy and the simple structure, this development can be easily implemented to other systems that challenge from the tracking accuracy under ORC scheme.

In the current times, microelectromechanical systems and nanoelectromechanical systems form a major interdisciplinary area of research involving science, engineering, and technology. A lot of work has been reported in the area of modeling and control of these devices, with the aim of better understanding their behavior and improving their performance. This paper presents a review of the emerging advances in the modeling and control of these micro- and nanoscale devices and converges on the exciting research in on-chip control , with a mechatronics and controls perspective and concludes by projecting future trends.

Piezoelectric-stack actuated platforms are utilized in many nanopositioning applications. Their performance is limited by their low-frequency resonance due to the mechanical construction as well as piezoelectric nonlinear effects. We propose a hybrid control scheme comprising a loop-shaping H∞controller and an inversion-based feedforward control scheme, capable of delivering accurate nanopositioning performance at relatively high speeds, upto 40 Hz. It is shown that the implemented control strategy is robust in the presence of uncertainty in resonance frequency due to loading. It is also shown that by employing charge actuation on the fast axis and integral tracking control on the slow axis, accurate raster scans can be obtained. Experimental results that show resonance damping, integral tracking action as well as the robustness of the implemented control scheme to resonance frequency uncertainty are presented. Finally, raster scans recorded at 10 Hz, 20 Hz and 40 Hz are presented to show the achievable positioning performance.