Model predictive control is widely used in the control of power converters due to its simple implementation, excellent performance, and fast dynamic response. However, the conventional model predictive control relies on load parameters for prediction. Hence, it exhibits high sensitivity. Furthermore, high switching frequency also contributes to increased unnecessary losses. To solve this issue, a novel integral sliding mode observer-based robust finite control-set model predictive control methodology for power converters is proposed in this article, aiming to enhance the robustness of the control system while reducing its switching frequency. In this proposal, the current prediction is accomplished by the implementation of an extended integral sliding mode observer for ultra-local model observation, which successfully mitigates the impact of load parameter perturbations. Meanwhile, the two-step prediction horizon structure extends the optimization range to two control periods, thereby enhancing the probability of applying the same voltage vector in consecutive control periods, which significantly reduces the switching frequency. The notable advantage of the proposed control method lies in enhancing the robustness of the system and reducing sensitivity to load parameters, while maintaining the switching frequency within a lower range, concurrently. Finally, simulation and experimental results are obtained to verify the robustness and low-switching-frequency performance of the proposed approach.

This paper proposes a novel integrated multi-model optimization method to enhance the accuracy and practicality of motor structure optimization for free-piston Stirling generators (FPSGs). Previous studies have been limited by oversimplification of the coupling relationship between the Stirling engine and motor, leading to imprecise or impractical results. The proposed method addresses this issue by incorporating the thermodynamic, hydrodynamic, dynamic, and electromagnetic models of the Stirling engine and motor. This comprehensive approach facilitates the appropriate consideration of various motor factors, such as load force and mass, on the stability, stroke, and frequency characteristics of the FPSG system. Furthermore, the proposed method utilizes conformal transformation and Particle Swarm Optimization to enhance computational efficiency. Motor structure optimization is carried out using the proposed approach, achieving a power density of 115.67 W/kg. The efficacy of the devised method is corroborated through finite element analysis and towing test bench experiments.

This article presents an improved multiple‐step model‐free predictive control (MS‐MFPC) method for permanent magnet synchronous motor (PMSM) drives. Specifically, a reduced‐order MS‐MFPC based on an ultra‐local model, which employs only the input and output of the plant without addressing any motor parameters, is developed to enhance the robustness and reliability of the non‐cascaded PMSM system in the presence of parametric uncertainties. Meanwhile, the decoupled form of algebraic ultra‐local model is derived to extend the prediction horizon to multiple steps without increasing the computational burden. Besides, in order to improve the dynamic performance, an online adaptive modification approach is embedded into this suggested design to enhance the cost function calculation for the first time. Compared with the conventional continuous control set‐model predictive control (CCS‐MPC) method, our development not only achieves the smaller overshoot, settling time and steady‐state error, but also attenuates the inherent issue of system uncertainties in widely existing industrial application. Finally, the proposed MS‐MFPC methodology is experimentally assessed for a PMSM test bench, where steady‐state and transient‐state performance tests confirm the interest of the proposal.

The low-carbon martensitic stainless steel CA6NM is widely used in the impellers of hydroelectric and nuclear power units due to its advantages of high hardness, corrosion fatigue strength, and good fracture toughness. In order to analyze the number of repair welding times on the properties of heat-affected zone (HAZ, the most unstable area of welded joint property) in CA6NM, the microstructure and mechanical properties of HAZ with once-repaired welds (1R) and twice-repaired welds (2R) were tested. Through data analysis, the following conclusions are drawn for welded joints: (1) the grain size of HAZ with a different number of repair welding processes is smaller than that of the base material; (2) the density of texture concentration is reduced and the directionality is not obvious; (3) the density of twins and dislocations are increased; (4) the hardness and impact energy of HAZ of the 1R and 2R specimens are higher than that of the base material.

In this paper, we propose a biomimetic approach of adaptive locomotion generation for a hexapod robot. Inspired by biology, a Central Pattern Generator (CPG) model is employed to generate tripod locomotion in the six-legged robot and the parameters can be tuned adaptively. Both simulation studies and experiments verified that the proposed control scheme can help the hexapod robot improve maneuverability and environmental adaptability with different parameters in various terrains.

This article proposes a novel axially magnetized Vernier permanent-magnet (AMVPM) machine which has permanent magnets (PMs) clamped on both ends of the rotor with alternately magnetized directions. The topology improves the mechanical strength of the structure without weakening the electromagnetic performances. The detailed operating principles are described and the design parameters are optimized. The open-circuit back electromotive force (EMF) is calculated analytically by using the harmonic series expressions. Then, a comparison between the AMVPM machine and regular Vernier permanent-magnet (VPM) machine is analyzed by finite-element analysis (FEA) method. Finally, the machine prototype is manufactured and the electromagnetic performances are experimentally validated.