This article presents an improved super-twisting high-order sliding mode observer for permanent magnet synchronous motors to achieve high-performance sensorless control. The proposed observer is able to simultaneously estimate rotor position and speed, as well as track parameter disturbances online. Then, according to the back-EMF model, the sensorless observer is further constructed to improve the estimation effect. The estimated rotor position and speed are used to replace the actual values detected by the sensor, and the estimated parameter disturbances are considered as feedback values to compensate the command voltage. In this way, not only is the estimation accuracy improved, but the robustness against uncertainties is also enhanced. Simulation and experimental results show that the proposed observer can effectively track the rotor position and speed and obtain good dynamic and steady-state performance.

The conflict between dynamic rapidity and steady-state accuracy is a crucial factor hindering the performance improvement of motor control system. To overcome the issue, this article proposes an optimized cooperative control combining feedback linearization (FBL) and error port-controlled Hamiltonian (EPCH) for permanent magnet synchronous motor (PMSM). First, FBL and EPCH are separately designed to obtain good dynamic and steady-state performances. Then, considering the individual advantages of FBL and EPCH, a cooperative strategy based on the real-time position error is applied to realize the smooth switching between the two methods, so that each method is utilized efficiently within the corresponding operating range. In addition, the particle swarm optimization (PSO) algorithm is introduced to properly select the controller parameters. Thus, an optimized cooperative control method, which takes into account both fast dynamic response and high steady-state precision, is developed for PMSM drives. The experimental results are finally given to illustrate the effectiveness and superiority of the proposed method.

By denaturing proteins and promoting the formation of multiprotein complexes, protein phosphorylation has important effects on the activity of protein functional molecules and cell signaling. The regulation of protein phosphorylation allows microbes to respond rapidly and reversibly to specific environmental stimuli or niches, which is closely related to the molecular mechanisms of bacterial drug resistance. Accurate prediction of phosphorylation sites (p-site) of prokaryotes can contribute to addressing bacterial resistance and providing new perspectives for developing novel antibacterial drugs. Most existing studies focus on human phosphorylation sites, while tools targeting phosphorylation site identification of prokaryotic proteins are still relatively scarce. This study designs a capsule network-based prediction technique for p-site in prokaryotes. To address the poor scalability and unreliability of dynamic routing processes in the output space of capsule networks, a more reliable way is introduced to learn the consistency between capsules. We incorporate a self-attention mechanism into the routing algorithm to capture the global information of the capsule, reducing the computational effort while enriching the representation capability of the capsule. Aiming at the weak robustness of the model, EcapsP improves the prediction accuracy and stability by introducing shortcuts and unconditional reconfiguration. In addition, the study compares and analyzes the prediction performance based on word vectors, physicochemical properties, and mixing characteristics in predicting serine (Ser/S), threonine (Thr/T), and tyrosine (Tyr/Y) p-site. The comprehensive experimental results show that the accuracy of the developed technique is close to 70% for the identification of the three phosphorylation sites in prokaryotes. Importantly, in side-by-side comparisons with other state-of-the-art predictors, our method improves the Matthews correlation coefficient (MCC) by approximately 7%. The results demonstrate the superiority of EcapsP in terms of high performance and reliability.