With the rapid development of automation and intelligent technology, mobile robots have shown wide application potential in many fields, and accurate navigation systems are the key to robots completing tasks. This paper proposes an enhanced pure pursuit path tracking algorithm for mobile robots, which is optimized using NSGA-II, with high-precision GNSS navigation for accurate positioning. The improved algorithm considers the dynamic characteristics and real–world operating conditions of the robot, optimizing steering decisions to enhance path tracking accuracy. Experimental results demonstrate the effectiveness of the algorithm: with a look–ahead distance of 0.5 and a maximum linear velocity of 3, the average absolute pose error (APE) is reduced by 14.63%, while a velocity of 4 reduces the APE by 55.94%. The enhanced algorithm significantly reduces path deviation and improves navigation performance.

The DC micro-grid has emerged as an increasingly popular solution for localized energy integration and dispatching. With multiple DC micro-grids in a system, an effective DC tie can facilitate various generation, storage, and consumption modes. It enhances energy versatility and contributes to improved energy resiliency. Hence, this paper introduces the concept of a multi-port energy router (MER) aims to multiplex DC power across various DC communities all with close-margin bus voltages. The proposed MER functions as a "super node" following Kirchhoff’s current law. It incorporates a unique partial power topology, enabling direct control of DC current flow between any two buses or towards local energy storage, solely based on bus voltage differences. To align modulation and control with industrial standard frameworks and provide flexibility in controlling energy storage current, a multi-dimension space vector modulation method is proposed. The instantaneous power theory is re-defined for the DC energy router’s operation. The power transfer efficiency of the standard half-bridges partial power topology is empirically verified through rigorous testing. Various operational scenarios are examined to demonstrate the feasibility of the MER concept, with findings supported by both simulation and experimental data.

This paper proposes a low-cost on-chip Hardware-in-the-Loop (HIL) platform for teaching and fast prototyping of dynamic systems. A dual-core digital signal controller (DSC)-based solution is proposed for the HIL system. CPU core A, as the simulation engine, is dedicated to circuit and system simulation. The actuation and control logic are implemented in CPU core B, which is working as the control engine. Inter-processor communication is used to interchange variables between the CPUs. The digital-to-analog converter and digital outputs are used to send the duty cycle and system state variables to the oscilloscope for users’ visual feedback. Two typical systems with fast and slow dynamics are modeled and implemented in the simulation engine. Under the excitation generated by the control engine, system dynamics can be observed for studying purposes. Close-loop control for a buck converter is also demonstrated on the developed prototype, where both input voltage and load variations performance are tested. The test results indicate that the digital simulator can well emulate the average small signal model of a power converter in open-loop and close-loop scenario. Meanwhile, the control parameters can be modified for system performance evaluation and education purposes. The proposed low-cost HIL system can be easily applied to the engineering teaching as well as fast prototype development phase of product design.

In commercial electrical equipment, interior permanent magnet (IPM) synchronous motors undergo variations in temperature and parameters under different operating conditions. Conventional maximum torque per ampere (MTPA) control using fixed parameters lacks accurate motor parameter information, which can negatively impact the operating efficiency of electrical equipment. To address the above problem, this paper proposes a simple online MTPA control method without motor parameters. To approximate the interface structure of the conventional MTPA controller, a back propagation neural network (BP-NN) adjuster with a space vector decomposer is designed as the MTPA controller. Moreover, by leveraging the similarity between the loss function of the BP-NN and the stator current representing the efficiency of the IPM motor, the teacher signal is cleverly chosen to update the parameters of the BP-NN online. In the discrete domain, the motion mechanism of the proposed method is analyzed and characterized by the perturb & observe technique. Compared to the conventional MTPA control using fixed parameters, the feasibility and high efficiency of the proposed method for copper-loss-minimization control were validated in the simulation. The feasibility of the proposed method was further validated in simulated scenarios involving sudden changes in torque and speed. Furthermore, the transient characteristics of the output stator current were analyzed. Overall, the proposed method can achieve true online MTPA control without motor parameters.

Although significant efforts have been made to enhance industrial air conditioning systems, there are still efficiency and transient response issues in vehicle air conditioning systems using IPMSM compressors. This paper focuses on the neural network compensator-based control in an interior permanent-magnet synchronous motor (IPMSM) to address the occurrence of reduced power copper loss efficiency and degraded velocity response in the motor system when confronted with periodic dynamic disturbances of step signals. This paper encompasses two main objectives: the first objective is to introduce a neural network (NN) compensator to improve the power copper loss efficiency. The NN compensator is developed using the velocity loop and current loop control model equation of an IPMSM, and trained to implement optimal compensation control based on the back propagation algorithm. The second objective is to optimize the dynamic performance of velocity response compared to the traditional maximum torque per ampere (MTPA) current control method under step disturbance and dynamic control conditions by building an experimental system for validation, incorporating both hardware and simulation. Another significant advantage is the low computational load introduced by the neural network compensator, rendering it well-suited for implementation within low-order DSP systems. The results indicate that the neural network compensator surpasses conventional MTPA control method in both simulation and hardware-based implementations concerning power copper loss and velocity response in an IPMSM control system.

The constant current followed by constant voltage (CC-CV) charging method is commonly employed for battery charging, effectively extending battery life and reducing charging time. However, as charging progresses, the battery’s internal resistance increases, complicating the charging circuit. This paper designs a wireless power transfer system utilizing an LCL coupling network for battery charging. The system employs frequency modulation (FM) to manage its CC and CV output characteristics at two fixed frequency points. The influence of the LCL coupling network parameters on system output characteristics was investigated using MATLAB. A simulation model was developed in the PSIM environment to validate the CC and CV output characteristics. The simulation results show that the system has load-independent constant-current and constant-voltage characteristics at two different frequency points. In order to verify the theoretical analysis, an experimental platform was also established. Experimental results demonstrate that the proposed system operates effectively in CC mode, maintaining constant output current across various loads, while in CV mode, it effectively regulates output voltage for different loads. The designed frequency modulation controller ensures a rapid response to sudden changes in load resistance, regardless of operating in CC or CV modes.