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This paper aims to optimize passive damper system (PDS) design by configuring its parameters to improve its performance and behavior in permanent magnet synchronous machines (PMSM).

First, the principle and effectiveness of the PDS are recalled. Second, the impact of different PDS parameters on its operation is analyzed. Third, a multi-objective optimization is proposed to explore a compromise design of PDS. Finally, the transient finite element method simulation is performed to validate the optimized design, which can ensure an excellent noise reduction effect and weaker negative impacts.

A suitable capacitance value in PDS is a key to realizing the damping effect. A larger copper wire can improve the noise reduction performance of PDS and reduce its Joule losses. A compromise solution obtained from a multi-objective optimization remains the excellent noise reduction and reduces Joule losses.

This paper explores the impact of PDS parameters on its operation and provides an orientation of PDS optimization, which is favorable to extend its application in different electrical machines.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.