Abstract Vertical position control of the elongated plasma is essential for the stable operation of the tokamak device. The PD controller has been the main control algorithm used for vertical position control in EAST in recent years. However, it is difficult for the controller to adapt to the vertical position response changes caused by plasma variations during discharge. Furthermore, the pure time delay in the EAST vertical position control system causes control instability at high vertical displacement growth rates γ. In this work, we expand on (Wangyi Rui et al, 2024 Nucl. Fusion 64 066040) and provide a comprehensive introduction to the neural network-based vertical position adaptive control system on EAST. To adapt the controller to vertical displacement response variations and compensate for system delay, we propose a model-based control algorithm. In this algorithm, controller parameters are calculated via the Linear Quadratic Regulator (LQR) method, while system delay is compensated using a Smith predictor. In order to achieve real-time implementation of the above process, we simplified the vertical displacement model of EAST and trained a neural network model to obtain model parameters in real time during discharge. The new control system has been applied to the EAST in the 2024 campaign experiment and can achieve stable control in multiple configurations without manual parameter tuning when the configuration changes during the discharge process. The control system has achieved stable control with a vertical displacement growth rate of~1300/s for the first time, providing important assistance for the future development of the device.

Polyphase deformation and control of structural position on lineament distribution in a pop-up fault-propagation fold: Inferences from structural and lineament analysis of the Kambhu anticline, Southern Kirthar Fold Belt, Pakistan

Application of a proven treatment planning system for accelerator-based boron neutron capture therapy.

Self-tuning RLS based multirate adaptive robust position control: A tilting-table test-bench study

Abstract This paper addresses the position control of permanent magnet synchronous machines through exact linearization via feedback, focusing on implementation for embedded systems. The research demonstrates that the exact linearization can present a feasible approach for controlling electrical drives. A comprehensive design flow for feedback linearization-based position control is presented in both continuous and discrete time, and a prefilter is derived to ensure accurate reference tracking. Motion control of electrical machines is usually performed on embedded systems. However, implementing exact linearization can often encounter problems due to limitations in the numerical representation. The research also offers a solution for implementing feedback linearization-based control on embedded devices, enabling safe and reliable operation. The developed methodology proposes a combined exact linearization and state feedback control approach that enables the effective implementation of the controller by allowing the use of well-conditioned control gains. The methodology is validated through both simulations and measurements carried out on an actual drive.

The classical controller design methods, often lead to sub-optimal performance, especially when implemented for plants exhibiting complex dynamics like integrals, non-minimum phase zeros, time-delays, etc.; and the controllers synthesised using classical methods can result in poor time domain characteristics, and limited robustness. Thus, it is essential to formulate the controller synthesis methods that improve stability, dynamic performance, and robustness. Proposed design explores the synthesis of optimal and robust controllers by posing the controller synthesis as a multi-objective optimization problem; wherein objectives of peak sensitivity, minimization of integral square error and control effort, along with phase margin penalty and delay margins are considered while formulating the objective function; followed by solving it by multi-objective genetic algorithm. Following the synthesis, a set of Pareto-optimal solutions is generated; to identify the ideal controller from these solutions, K-Means clustering is applied along with the determination of the utopia point for controller selection. The work is implemented for four systems like (a) integrating system, (b) position control of DC motor, (c) non-minimum phase hydropower system and (d) coupled tank systems. The proposed controller demonstrates significant quantitative improvement of performance metrics across all systems when compared to conventional methods. Additionally, Monte Carlo simulations for the robustness analysis are included to establish the superiority of the proposed method over the conventional.

In recent years, agricultural production activities have been advancing towards mechanization and intelligence to bridge the growing gap between the high labor intensity and time sensitivity of harvesting operations and the limited labor resources. As the component that directly interacts with target crops, the end-effector is a crucial part of agricultural harvesting robots. This paper first reviews their materials, number of fingers, actuation methods, and detachment techniques. Analysis reveals that three-fingered end-effectors, known for their stability and ease of control, are the most prevalent. Soft materials have gained significant attention due to their flexibility and low-damage characteristics, while the emergence of variable stiffness technology holds promise for addressing their issues of poor stability and fragility. The introduction of bionics and composite concepts offers potential for enhancing the performance of end-effectors. Subsequently, starting from an analysis of the biomechanical properties of fruits and vegetables, the relationship between mechanical damage and the intrinsic parameters of produce is elucidated. On the other hand, practical and efficient finite element analysis has been applied to various stages of end-effector research, such as structural design and grasping force estimation. Given the importance of compliance control, this paper explores the current research status of various control methods. It emphasizes that while hybrid force–position control often suffers from frequent controller switching, which directly affects real-time performance, active admittance control and impedance control directly convert external forces or torques into the robot’s reference position and velocity, resulting in more stable and flexible external control. To enable a unified comparison of end-effector performance, this review proposes a progressive comparison framework centered on control philosophy, comprising the ontological characteristic layer, physical interaction layer, feedback optimization layer, and task layer. Additionally, in response to the current lack of scientific rigor and systematization in performance evaluation systems for end-effectors, performance evaluation criteria (harvest success rate, harvest time, and damage rate) are defined to standardize the characterization of end-effector performance. Finally, this paper summarizes the challenges faced in the development of end-effectors and analyzes their causes. It highlights how emerging technologies, such as digital twin technology, can improve the control accuracy and flexibility of end-effectors.

A novel collaborative position and orientation control scheme (CPOCS) for dual-arm robots is proposed, which is capable of controlling the end-effectors' positions with high precision while preserving their orientations unchanged to some practical tasks (e.g., box handling). To solve the proposed CPOCS in real time while considering key factors such as unknown bounded noise and strict time response constraints in practical engineering environments, this article proposes a novel precisely predefined-time convergent barrier recurrent neural network (PCB-RNN) based on a newly developed piecewise barrier evolution formula. Unlike existing RNNs, the proposed PCB-RNN, owing to its piecewise barrier evolution formula, can achieve precisely predefined-time convergence (PPTC) when addressing the proposed CPOCS under unknown bounded noise conditions. Comprehensive theoretical analysis rigorously proves the PPTC ability of the PCB-RNN under both noise-free and unknown bounded noise conditions. Furthermore, extensive simulation and physical experiments on dual-arm robots validate the effectiveness of the proposed CPOCS and demonstrate the advanced PPTC capability of the proposed PCB-RNN under unknown bounded noises.

Excitation coefficients with a low dynamic range ratio (DRR) are advantageous in controlling mutual coupling between the elements of an antenna array. Their use also reduces the output power loss and simplifies the design of the feeding network. In this paper, a hybrid algorithm based on invasive weed optimization and convex optimization for the synthesis of distributed arrays with two subarrays is proposed. Arrays of this type are used in numerous applications, e.g. in aircraft. A constraint is added to the optimization problem to control the DRR of the array's excitation vector. Numerical results are presented for position-only, as well as for position and excitation control approaches. The trade-off between the peak sidelobe ratio and the obtained DRR is illustrated by numerical examples.

Free-energy landscapes (FELs) play a crucial role in understanding molecular processes via molecular dynamics (MD) simulations. However, standard umbrella sampling (US), a common technique for enhancing FEL sampling efficiency, struggles with adequately sampling high-free-energy regions and controlling the distributions of windows. We previously introduced an optimization-based approach that adaptively adjusts window positions through bias potential optimization. Here, we significantly refine this approach by explicitly controlling both the positions and variances of the sampling distributions. Our optimization method employs target Gaussian distributions with imposed upper bounds on variance, preventing excessive broadening and ensuring stable, unimodal sampling within each window. We demonstrate our method's efficacy using Langevin dynamics simulations of the two-dimensional π/4-rotated Wolfe-Quapp potential and MD simulations of alanine dipeptide in water. For the Wolfe-Quapp potential, in the non-optimized simulations, increasing bias potential strength improved accuracy, but even the best case yielded only 95% agreement. In contrast, all optimized simulations exhibited superior convergence compared to the nonoptimized simulations. Compared to the standard US, our optimized method yields significantly improved accuracy and faster convergence in reconstructing FELs, particularly near saddle points and steep free-energy gradients. This improved optimization framework provides a robust and generalizable strategy for automated tuning of bias potentials, facilitating accurate FEL reconstruction in diverse and complex molecular systems. The method is openly accessible via GitHub (https://github.com/YukiMitsuta/plumed_USopt) and fully integrates with the widely used PLUMED package. This method can be easily extended to multiple dimensions, making it possible to extend it to more complex biomolecular systems.

Abstract Lightweight and compact deployable structures are essential for manufacturing large spacecraft, enabling human exploration of deep space. Deployable mechanisms utilizing shape memory alloy (SMA) actuators meet these structural requirements without adding extra weight. However, the hysteresis, nonlinearity, and time-varying properties of SMA present challenges for their application. This study focuses on designing and validating a controller that combines an Extended State Observer (ESO) with Sliding Mode Control (SMC) to achieve accurate position control of SMA actuators. A mechanistic model of the SMA actuator was established, and the ESO was designed to monitor and compensate for uncertainties arising from parameter fluctuations and modeling inaccuracies. An experimental setup was established to measure the SMA material parameters for identification and validation. Numerical simulations were conducted to compare the proposed control method with a traditional SMC, evaluating the dynamic behavior and response of the SMA actuators under various target displacement inputs. The findings revealed that the proposed controller achieved faster convergence times and lower Root Mean Square Error (RMSE) values of 0.29398 and 0.06842, representing improvements of 34.96% and 19.71\% over the traditional SMC controller. Thus, this ESO based controller demonstrates rapid responsiveness, high precision, and robustness, with successful applications in driving deployable structures, highlighting its practical value.

The purpose of this paper is to achieve stable and ideal path tracking control of a quadrotor, despite actuator faults and external disturbances and in the presence of noise at a specific target position. The tracking control achieved using a dual-loop control strategy, where the outer loop (position control) managed by an adaptive integral sliding mode controller (AISMC). Since actuator faults can significantly affect the stability of the quadrotor and cause deviation from the desired position, the inner loop (attitude control) uses a model predictive control (MPC) scheme especially after the quadrotor reaches the target position, which maintains stability and optimal performance despite the actuator faults and external disturbances. Additionally, to reduce the impact of noise, an innovative filtering/anti-filtering technique is incorporated into the MPC which can mitigate the noise effects while preserving the nominal performance of the system. Simulation results are presented and analysed to evaluate the performance, fault tolerance, and robustness of the proposed hybrid model predictive adaptive integral sliding mode controller (MPAISMC).

The work is devoted to an actual problem – the construction of improved systems automatic control (SAC) for the position of electromagnetic levitation systems. A detailed nonlinear mathematical model of a DC electromagnet is obtained, taking into account its structural instability and the unidirectionality of the force, as well as the dependence of the parameters on the working gap. The synthesis problem is formulated in the class of supervisory and software control systems. An astatic modal position controller and a state observer are synthesized to estimate the unmeasured velocity for use in the controller feedback. The synthesized automatic control system is studied by mathematical modeling. It is shown that the process of initial stabilization of the position of a free-falling levitating body is aperiodic. It is confirmed that the system has a 1st order astatism with respect to task and force disturbances and a 2nd order astatism with respect to position disturbances. It is established that the quality indicators of transient processes, as well as the quality factor in terms of speed and frequency of transmission, correspond to those set over the entire operating range of the working gap change. The negative influence of the variable parameters of the object on the quality of the control in the case of a significant increase or decrease of the working gap in relation to the nominal value is revealed. In general, the research results confirm the high quality of the indicators of the functioning of the synthesized automatic control system of the magnetic levitation system. References 19, figures 11, table 3.

Plasma shape and position control development for NSTX-U using the GSEvolve plasma simulator

To investigate and compare the effects of bifocal soft contact lenses, single-focus soft contact lenses, and orthokeratology lenses (OK lenses) on patients with small-angle intermittent exotropia (IXT) accompanied by myopia, providing a basis for reducing secondary surgeries in clinical practice. A prospective, randomized, controlled study was conducted on 142 patients with small-angle IXT who had undergone IXT surgery and had concomitant myopia. Patients were randomly assigned to three groups using a computer-generated random number sequence with allocation concealment: the bifocal soft contact lens group (group A), the single-focus soft contact lens group (group B), and the OK lens group (group C). During the one-year treatment period, multiple visual function parameters were measured, including best-corrected visual acuity (BCVA), contrast sensitivity, accommodative function, eye position control ability, strabismus angle, and axial length (AL). There were no significant differences in BCVA among the three groups, indicating similar basic visual acuity correction effects. Compared with the other two lens types, defocus soft contact lenses led to reduced contrast sensitivity at high spatial frequencies. Orthokeratology lenses resulted in decreased accommodative amplitude, increased accommodative lag, and reduced accommodative facility compared with the other lenses. The strabismus angle decreased in all patients, with improved positive fusional convergence. Groups A and B showed better improvements in eye position control and strabismus angle reduction than group C. Defocus soft contact lenses and OK lenses were comparable in controlling AL growth. The strabismus angle positively correlated with near stereopsis acuity and eye position control scores and negatively correlated with near horizontal positive fusional convergence. After the initial IXT surgery, patients wearing defocus soft contact lenses with a concentric bifocal design can effectively control AL growth, enhance eye position control ability, improve visual function, and achieve good subjective visual outcomes.

Objective.Transcranial focused ultrasound (tFUS) for neuromodulation has attracted increasing attention, yet accurate pre-procedural planning and dose estimation is constrained by oversimplified skull representations and by the neglect of transducer-skull spacing induced wave interactions. This study aims to develop and validate a computationally efficient, CT-informed analytical framework for predicting frequency-dependent insertion loss.Approach.We propose a multi-layer analytical framework that incorporates four key factors-skull thickness, skull density ratio, ultrasound insertion angle, and the transducer physical geometry and spacing from the skull, to predict frequency-dependent pressure insertion loss. Model accuracy was evaluated against k-Wave simulations and hydrophone measurements in 20ex-vivohuman skulls across 100 kHz to 1000 kHz frequency range.Main Results.Median prediction deviations for peak pressure insertion loss were +1.1 dB (interquartile range (IQR): +0.2 dB to +2.2 dB) relative to measurement and -1.7 dB (IQR: -2.7 dB to -0.7 dB) relative to simulation. The relative median percentage errors were +30.1% (IQR: +9.5% to +35.6%) and -20.3% (IQR: -31.7% to -10.1%), respectively. Median spearman correlation and cosine similarity values reached 0.92 (IQR: 0.86-0.98,p< 0.001) and 0.73 (IQR: 0.49-0.82), respectively. Uncertainty analysis showed that varying transducer-skull spacing resulted in a median absolute percentage uncertainty of 18.1% (IQR: 17.2% to 21.3%).Significance.The balance of accuracy and efficiency of the proposed CT-informed multi-layer model makes it a practical tool for transducer positioning, frequency selection, and dose control in tFUS neuromodulation, with potential to improve reproducibility and safety in clinical applications.

This study aims to develop and evaluate an Adaptive PID–PD Hybrid Control System to enhance the position and rotation control of a Remotely Operated Vehicle (ROV) in challenging sea conditions. In this study, two main stages were conducted. First, a dynamic model of the ROV was developed, encompassing translation for movement in three-dimensional space (x, y, z) and rotation for changes in orientation (roll, pitch, yaw). Second, the adaptive PID–PD hybrid controllers were implemented and evaluated on the ROV model to ensure stability and precision in motion control. Simulation results demonstrate that the proposed controller effectively maintains position with surge overshoot of 23.3%, sway of 1.67%, and heave of 47.17%. The settling time ranges from 41.53 to 107 seconds, indicating areas for further tuning. In terms of velocity response, surge velocity shows a high overshoot of 106.26%, while sway and heave velocities present smaller overshoots but require longer stabilization times. The integration of PID and PD in a hybrid adaptive framework yields improved inner-loop response and overall robustness. These findings highlight the potential of the adaptive hybrid controller to enhance stability, responsiveness, and operational effectiveness of ROVs in dynamic marine conditions.

Thioamides─minimalist amide isosteres in which sulfur replaces the backbone carbonyl oxygen─offer a precise means to modulate peptide conformation through altered hydrogen-bond geometry and polarity. This work presents a general and experimentally validated strategy for programming β-peptide secondary structure with atomic precision, using site-selective thioamide substitution as a minimalist backbone modification. While thioamides have been studied individually, their positional control within β-peptides to direct helicity, curvature, and topology has not been achieved before. Using trans-2-aminocyclopentanecarboxylic acid (ACPC) foldamers as a model system, we show that strategic thioamide placement enables hybrid 12/8-helices, backbone-encoded curvature, and conical 16/12-helices; symmetry-defined macrocycles (pseudo-C2, pseudo-C3, pseudo-C4) inaccessible by conventional β-peptide synthesis; gram-scale, solution-phase synthesis of β-peptides up to 32-mers (>4 kDa), the longest reported to date; and orthogonal editing via mild Ag(I)-mediated backbone conversion to all-amide analogs in 97-99% yield, allowing folding to be programmed with temporary thioamide units before conversion to the desired scaffold. These advances establish a unified framework for controlling β-peptide helicity and topology through minimal backbone editing, significantly expanding the accessible structural and functional space for foldamer chemistry. The concepts and methodologies are broadly applicable to organic synthesis, supramolecular chemistry, biomolecular engineering, and peptide-inspired materials.

Contrary to tumor-infiltrating T cells with dysfunctional mitochondria, tumor-associated macrophages (TAMs) preserve their mitochondrial activity in the nutrient-limited tumor microenvironment (TME) to sustain immunosuppression. Here we identify TNF receptor-associated protein-1 (TRAP1), a mitochondrial HSP90 chaperone, as a metabolic checkpoint that restrains oxidative respiration and limits macrophage suppressive function. In the TME, TRAP1 is downregulated through TIM4-AMPK signaling, and its loss enhances immunoinhibitory activity, limits proinflammatory capacity and promotes tumor immune escape. Mechanistically, TRAP1 suppression augments electron transport chain activity and elevates the α-ketoglutarate/succinate ratio, remodeling mitochondrial homeostasis. The resulting accumulation of α-ketoglutarate further potentiates JMJD3-mediated histone demethylation, establishing transcriptional programs that reinforce an immunosuppressive state. Restoring TRAP1 by targeting TIM4 and JMJD3 reprograms TAMs, disrupts the immune-evasive TME and bolsters antitumor immunity. These findings establish TRAP1 as a critical regulator integrating metabolic and epigenetic control of suppressive TAM function and position the TRAP1 pathway as a promising target for cancer immunotherapy.

Intrinsically stretchable organic photovoltaics (is-OPVs) face a critical efficiency-stretchability trade-off that limits wearable applications. Here, a breakthrough molecular design strategy employing side-chain-engineered insulating polymers-poly(methyl methacrylate) (PMMA) and poly(benzyl methacrylate) (PBMA)-as multifunctional additives to simultaneously enhance electronic and mechanical properties is presented. Through synergistic control of compatibility, chain diffusivity, and docking position with PM6/Y6 components, PMMA selectively distributes in the amorphous regions of the PM6 donor while promoting molecular packing in crystalline regions, enabling dual stress-dissipation networks and efficient charge transport pathways. As a result, the rigid 10PMMA (with 10 wt.% PMMA) devices achieve a record 19.01% power conversion efficiency (PCE), while maintaining 18.53% PCE (only 2% loss) for the rigid 20PMMA (with 20 wt.% PMMA) devices. More remarkably, the stretchable 20PMMA devices exhibit exceptional mechanical robustness with 10.8% fracture strain (2.2-fold improvement) and 87% PCE retention after 100 stretching cycles (10% strain), far surpassing the control devices (50% retention). The work establishes fundamental design principles for insulating polymer additives in is-OPVs, demonstrating how molecular control over micro-/nanoscale distribution can simultaneously optimize electronic and mechanical properties. These findings provide a universal materials platform for high-performance stretchable electronics, particularly for next-generation wearable energy technologies where both efficiency and durability are paramount.