Autonomous mobile robots have become integral to daily life, providing crucial services across diverse domains. This paper focuses on path following, a fundamental technology and critical element in achieving autonomous mobility. Existing methods predominantly address tracking through steering control, neglecting velocity control or relying on path-specific reference velocities, thereby constraining their generality. In this paper, we propose a novel approach that integrates the conventional pure pursuit algorithm with deep reinforcement learning for a nonholonomic mobile robot. Our methodology employs pure pursuit for steering control and utilizes the soft actor-critic algorithm to train a velocity control strategy within randomly generated path environments. Through simulation and experimental validation, our approach exhibits notable advancements in path convergence and adaptive velocity adjustments to accommodate paths with varying curvatures. Furthermore, this method holds the potential for broader applicability to vehicles adhering to nonholonomic constraints beyond the specific model examined in this paper. In summary, our study contributes to the progression of autonomous mobility by harmonizing conventional algorithms with cutting-edge deep reinforcement learning techniques, enhancing the robustness of path following.

In this article, we develop a modular design method of decentralized controllers for linear dynamical network systems, where multiple subcontroller designers aim at individually regulating their local control performance with accessibility only to their respective subsystem models. First, we derive a constrained version of the Youla parameterization that characterizes all retrofit controllers for a single subcontroller, defined as an add-on-type subcontroller that manages a subsystem. The resultant feedback system is kept robustly stable for any variation in the neighboring subsystems, other than the subsystem of interest, provided that the original system is stable prior to implementing the retrofit control. Subsequently, we find out a unique internal structure of the retrofit controllers, assuming that the interaction input signal from the neighboring subsystems is measurable. Furthermore, we show that the simultaneous implementation of multiple retrofit controllers, designed by individual subcontroller designers, can improve the upper bound of the overall control performance. Finally, the practical significance of the method is demonstrated via an illustrative example of frequency regulation using the IEEE 68-bus power system model.

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.

The potential of autonomous driving technology to revolutionize the transportation industry has attracted significant attention. Path following, a fundamental task in autonomous driving, involves accurately and safely guiding a vehicle along a specified path. Conventional path-following methods often rely on rule-based or parameter-tuning aspects, which may not be adaptable to complex and dynamic scenarios. Reinforcement learning (RL) has emerged as a promising approach that can learn effective control policies from experience without prior knowledge of system dynamics. This paper investigates the effectiveness of the Deep Deterministic Policy Gradient (DDPG) algorithm for steering control in ground vehicle path following. The algorithm quickly converges and the trained agent achieves stable and fast path following, outperforming three baseline methods. Additionally, the agent achieves smooth control without excessive actions. These results validate the proposed approach’s effectiveness, which could contribute to the development of autonomous driving technology.

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.