The necessity of rejecting the external disturbance and model uncertainty is vital for control design to achieve high performance and robustness of the system. Disturbance cancelation techniques are, therefore, of interest for both academic and industrial research and development. Among them, disturbance observer based control (DOBC) and active disturbance rejection control (ADRC) are widely applied, thanks to their high performance and effectiveness in synthesis and realization. However, two knowledge gaps vis-à-vis the dynamical delay and the unification of these techniques exist in literature. This article proposes a study on the unification of DOBC and ADRC for the application of speed control of permanent magnet synchronous motor. The explicit formulas of the estimated disturbance are carried out and the dynamical delays of the observations are, therefore, straightforwardly deduced. By addressing these transparent delay formulas, the unification of DOBC and ADRC is verified. The identical performance of two methods in rejecting both external disturbance and model uncertainty is demonstrated by simulation and experimental validations.

Hands are central to nearly every aspect of daily life, so losing an upper limb due to amputation can severely affect a person’s independence. Robotic prostheses offer a promising solution by mimicking many of the functions of a natural arm, leading to an increasing need for advanced prosthetic designs. However, developing an effective robotic hand prosthesis is far from straightforward. It involves several critical steps, including creating accurate models, choosing materials that balance biocompatibility with durability, integrating electronic and sensory components, and perfecting control systems before final production. A key factor in ensuring smooth, natural movements lies in the method of control. One popular approach is to use electromyography (EMG), which relies on electrical signals from the user’s remaining muscle activity to direct the prosthesis. By decoding these signals, we can predict the intended hand and arm motions and translate them into real-time actions. Recent strides in machine learning have made EMG-based control more adaptable, offering users a more intuitive experience. Alongside this, researchers are exploring tactile sensors for enhanced feedback, materials resilient in harsh conditions, and mechanical designs that better replicate the intricacies of a biological limb. This review brings together these advancements, focusing on emerging trends and future directions in robotic upper-limb prosthesis development.

This paper proposes a generalized high-order observer for estimating total disturbance of permanent magnet synchronous motors (PMSMs). This total disturbance is dominated by load torque but also includes many other terms such as frictions, viscous force, Eddy and flux pulling forces, and noises. Comprehensive experimental results and analyses under various scenarios will be presented to find the appropriate order of the observer. We will compare the performance of zero-order observer (ZDO), first-order observer (FDO), and second-order observer (SDO) under three different scenarios of load torque. Additionally, the results of an state observer (ESO) under the same conditions are also presented. The experimental results show that FDO and SDO achieve similar performance and both of them are notably better than the ZDO and ESO. However, during the severe conditions of load torque, the SDO-based controller can achieve better performance compared to that of FDO-based controller. Moreover, fuzzy algorithm is applied to online tune the PI gains in the speed loop. The results also show that the SDO-based fuzzy-PI control scheme is effective in disturbance rejection and high-performance speed tracking. All of the experiments are carried out on a 300-W PMSM testbed with a digital signal processor (DSP).

This paper proposes a novel disturbance rejection control scheme for permanent magnet synchronous motor (PMSM) drives. Based on the framework of modified disturbance observer (DOB)-based control, the final topology of the proposed disturbance rejection proportional–integral (DR-PI) controller includes a pre-filter and a controller in a proportional–integral (PI) form. The proposed DR-PI control scheme is practical with a straightforward gain tuning rule. Note that the gain selection method is the main issue of not only conventional PI controllers but also advanced methods such as DOB-based controllers. In addition, by starting from the framework of modified DOB, this paper also proves that the PI controller with an pre-filter possesses a disturbance rejection ability similar to a DOB-based control method. To the best of our knowledge, this is the first time that such a simple and effective PI controller is designed for the speed control of PMSMs as well as theoretically proven to have a perfect disturbance rejection ability. This paper shows the steps of selecting the parameters of the proposed controller in terms of the parameters of a desired plant model, disturbance observer and compensator. Hence, unlike a traditional DOB case, in this approach, one can simultaneously tune the controller and observer at the same time. The appearance of the pre-filter from the modified DOB scheme solves an overshoot problem, thus the general motor operation is significantly improved, which is validated by experiments. The experimental evidence under two scenarios of load torque change and speed change prove the effectiveness of the proposed method compared to conventional PI and DOB control (DOBC) schemes. All the experiments were implemented on a 300 W PMSM of a setup manufactured by Lucas-Nuelle GmbH with a digital signal processor.

The control of tendon-driven robotic fingers presents significant challenges due to their inherent underactuation, coupled with complex non-linear dynamics arising from tendon elasticity, friction, and external disturbances. Therefore, achieving precise control of finger motion and contact interactions necessitates advanced modeling, estimation, and control strategies capable of addressing uncertainties in tendon tension, routing, and elasticity. This paper presents a comprehensive comparative study of three distinct control paradigms: feedback linearization with Proportional-Derivative (FBL-PD) control, feedback linearization with super-twisting sliding-mode algorithm (FBL-STA), and deep-deterministic reinforcement learning (DDPG-RL), for the precise trajectory tracking of a three-link tendon-driven robotic finger. Through extensive simulations, the performance of each controller is rigorously evaluated based on trajectory-tracking accuracy and robustness to varying disturbances. The results indicate that under disturbance-free conditions, the FBL-PD and FBL-STA controllers, when properly tuned, achieve precise tracking of the reference trajectory; however, they produce noticeably noisy control signals. When subjected to external disturbances, these controllers exhibit increased sensitivity, producing even noisier responses. In contrast, the DDPG-RL maintains smooth control dynamics and achieves sufficiently accurate tracking in both scenarios. This comparative analysis elucidates the strengths and weaknesses of each control strategy, offering critical insights and practical guidelines for the design and implementation of advanced control systems for dexterous tendon-driven robotic fingers.

Introduction/Objectives: The mechanical reliability of prosthetic finger connectors is critical for daily prosthesis use, as these small load-bearing elements transmit forces between finger segments and are prone to localized stress concentration at hinge transitions. The objective of this study was to quantitatively evaluate the mechanical performance of prosthetic finger connectors and determine how material selection influences stress distribution and deformation behavior when connector geometry is maintained constant. Methods: A controlled comparative finite element analysis was performed using COMSOL Multiphysics 6.2 on a single prosthetic finger connector geometry manufactured from four candidate materials: titanium alloy Ti-6Al-4V (E = 110 GPa), polyetheretherketone (PEEK, E = 4.0 GPa), AvaSpire AV-621 NT (E = 3.0 GPa), and KetaSpire KT-820 FP (E = 4.1 GPa). Three representative loading scenarios were simulated using an axial grip force of 50 N and a bending moment of 0.5 N·m: (1) axial loading, (2) combined axial and forward bending, and (3) combined axial and reverse bending. Results: Across all loading conditions, peak von Mises stresses consistently localized at the hinge-transition region of the connector. Ti-6Al-4V demonstrated the lowest peak stress (approximately 2.2 MPa under combined forward bending) and the smallest maximum displacement (approximately 0.0005 mm). In contrast, the PEEK-family polymers showed greater deformation (0.0125–0.0167 mm) due to their lower elastic modulus, while maintaining comparable peak stresses of approximately 2.5–2.6 MPa. The stress profiles along the connector arc length were nearly identical among all materials, indicating that connector geometry primarily governs stress localization, whereas material stiffness mainly determines deformation magnitude. Conclusions: This study demonstrates that material selection strongly affects prosthetic finger connector compliance, while stress concentration behavior is mainly controlled by geometry. The obtained quantitative comparison provides design-oriented guidance for selecting between Ti-6Al-4V and PEEK-based polymers for the development of lightweight and mechanically reliable prosthetic finger connectors.