Jiexin Zhang

Accurate parameter identification of permanent magnet synchronous machines (PMSMs), including stator resistance, inductances, and permanent magnet flux linkage, is crucial for advanced controller design in robotic systems. Conventional identification methods, utilizing polynomial-based magnetic models to capture magnetic saturation and cross-coupling effects, suffer from degraded inductance identification accuracy under nonideal factors such as inverter nonlinearity and core loss effects. To address this issue, this paper proposes an offline parameter identification method for PMSMs based on a physically feasible magnetic model. This model explicitly incorporates the magnetic constraints of PMSMs, effectively preventing overfitting and enhancing inductance identification accuracy. A sensitivity analysis via an ablation study shows that the symmetric and mutual inductance constraints mainly suppress overfitting, while the magnetic saturation constraint mainly reduces model complexity. Based on this model, a high-frequency (HF) current injection method is developed for inductance identification. Crucially, this method eliminates the need for numerical differentiation of the magnetic model, thereby substantially improving the robustness of inductance identification against nonideal factors. Additionally, stator resistance and inverter nonlinearity are estimated simultaneously by solving a constrained nonlinear least-squares problem. Finally, the permanent magnet flux linkage is identified based on the steady-state voltage equation when rotor rotation is permitted. The effectiveness of the proposed physically feasible magnetic model and offline parameter identification method is experimentally validated on a 0.21-kW surface-mounted PMSM. Compared with conventional polynomial-based methods, it avoids overfitting and achieves higher inductance identification accuracy with lower cross-validation errors. Furthermore, the identified parameters improve the accuracy of MTPA control and remain robust under different thermal conditions.

Isokinetic training has been proven to be an effective method in rehabilitative therapy. However, the quantitative relationship between training speed and the biophysical condition of the human body is not well established. To address this, a biceps isokinetic training robot implemented the dual-fatigue speed guidance model that integrated cognitive fatigue perception and physiological fatigue index based on sEMG was proposed. The sEMG signals were transformed from time series to a Markov state transition field (MTF) expressed as a state transition network diagram, from which modularity (MD) was extracted as a physiological fatigue index. MD combined with cognitive perceptions by the Borg RPE scale, formed a dual-fatigue speed guidance model for isokinetic training. After one month of training with nine male participants, the proposed model demonstrated improvements in peak torque, muscle dimensions, and cognitive fatigue compared to constant speed training, with a significant absolute peak torque increase (P = 0.0427). The proposed method enables personalized configuration of sports rehabilitation and enhancement, and it can be expected to apply in clinical precision therapy with more participants.

Increasing the viscosity of elastic joints can significantly improve the performance of elastic joint robots during physical human–robot interactions. However, current approaches for injecting viscous elements require an additional damper to be added in parallel with the elastic elements. In this paper, we propose a new concept called virtual viscous element injection (VVI), which enables a robot to exhibit viscoelasticity without altering its mechanical structure. VVI relies only on motor-side dynamics reshaping and state feedback. Interestingly, the VVI method allows high-resolution joint torque measurements in elastic joint robots, unlike in physical viscoelastic joint robots, which measure joint torque using higher-order derivatives of the positions. Furthermore, the VVI method is proved to preserve the passivity of robot dynamics, which provides numerous possibilities for the applications of combined passivity-based controllers. Specifically, we first emphasize the impedance control method using VVI. The results demonstrate that the VVI-DF method, which combines the direct feedback (DF) method with VVI, addresses the issue of excessive acceleration feedback in the controller. This provides looser constraints for achieving a high-gain torque loop in impedance control. Moreover, this paper also provides examples of the application of VVI combined with passivity-based position and torque controllers. Experiments and simulations demonstrate the effectiveness of the proposed methods. The proposed method can be extended to various robots, such as exoskeletons, and collaborative robots.

Joint acceleration feedback is widely used in the design of controllers and observers since joint accelerations reflect the joint dynamics of robots, especially in physical human-robot interaction. However, joint acceleration acquisition is a technical difficulty for robots. The dynamics-based methods can achieve joint acceleration estimation using only a nominal model. Still, the performance of these methods is limited by the fast time-varying disturbances in the system. This letter proposes a joint acceleration estimation method based on a high-order disturbance observer. This method can observe and compensate for fast time-varying lumped disturbances in the observer while maintaining joint acceleration estimation performance at low frequencies. The finite-time stability of the proposed estimation method is proved using the Lyapunov theory. Simulations and experiments with a lower limb rehabilitation robot are implemented to verify the performance of the proposed method.