A DC microgrid’s tightly regulated DC/DC converter encounters significant challenges in voltage stability, primarily due to the negative incremental resistance of constant power loads (CPLs). Conventional controllers often struggle with load variations and changes in system parameters. Therefore, there has been growing interest in adaptive machine learning algorithms, such as Deep Reinforcement Learning (DRL), to improve voltage regulation. This paper presents an end-to-end DRL framework based on a modified Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The framework is designed to directly control power switches for regulating the voltage of a DC/DC buck converter that supplies power to CPLs. Real-time experiments were conducted using OPAL-RT to validate the approach under diverse load cycles and converter parameter changes.Comparative analysis against other DRL-based control strategies, including Deep Q-learning (DQN) and Deep Deterministic Policy Gradient (DDPG), demonstrated the superior static and dynamic voltage response of the proposed modified TD3 DRL controller, particularly in scenarios involving load and parametric variations.

This research paper presents a novel approach to current control in Grid-Connected Inverters (GCI) using Deep Reinforcement Learning (DRL) based Twin Delayed Deep Deterministic Policy Gradient (TD3) method. The study focuses on addressing the limitations of traditional control techniques and state of the art techniques, particularly Proportional-Integral (PI) control and Model Predictive Control (MPC), by leveraging the adaptive and autonomous learning capabilities of DRL. The proposed novel modified TD3-based DRL method learns an optimal control policy directly from raw data, enabling the controller to adapt and improve its performance in real-time. The research includes a comprehensive analysis of the implementation and validation of the modified TD3-based DRL control in a grid-connected three phase three level Neutral Point Clamped (NPC) inverter system with Inductor-Capacitor-Inductor (LCL) filter. Real-time validation experiments are conducted to evaluate the control performance, power transfer capability in grid compliance. Furthermore, a detailed comparison is presented with experimentation, highlighting the advantages of the TD3-based DRL control over PI and MPC control techniques. Robustness checking is performed under various operating conditions, including parameter variations and dynamic conditions in the grid. The results analysis demonstrates that the TD3-based DRL control outperforms traditional PI control techniques in terms of static, dynamic response, and robustness. Additionally, The DRL based grid connected inverter current control method is validated in Renewable Energy Source (RES) solar PV grid integration application.

This work presents a novel control paradigm to improve the Direct Current Regulation (DCR) of two-level inverters that are connected to the grid with LCL filters. The Deep Reinforcement Learning (DRL) based Deep Deterministic Policy Gradient (DDPG) algorithm is utilized to address the constraints of traditional control methods, such as Proportional Integral (PI) controllers and Model Predictive Control (MPC). The suggested method tackles challenges like as non-linearity, model dependency, and parameter fluctuations, which have a substantial impact on the performance of the DCR of grid connected two-level inverters of electric power system. The DDPG algorithm offers a flexible control technique that enables adaptive learning and optimization of policies. The results are validated in Real time mode Hardware In Loop (HIL) using Opal-RT & Texas instrument launchpad. Simulations performed on the MATLAB platform provide a reliable testing environment to assess the effectiveness of the proposed controller compared to traditional alternatives. The simulation results clearly show that the DDPG-based controller performs better than any other controller. This strategy surpasses traditional methods, demonstrating an increased resistance to reliance on specific models and uncertainties in parameters.

The growing deployment of connected and autonomous vehicles (CAVs) requires coordinated control strategies that jointly address safety, mobility, and energy efficiency. This paper presents a novel two-stage cooperative control framework for autonomous hybrid electric vehicle (HEV) platoons based on machine learning. In the first stage, a metric learning-based distributed model predictive control (ML-DMPC) strategy is proposed to enable cooperative longitudinal control among heterogeneous vehicles, explicitly incorporating inter-vehicle interactions to improve speed tracking, ride comfort, and platoon-level energy efficiency. In the second stage, a multi-agent twin-delayed deep deterministic policy gradient (MATD3) algorithm is developed for real-time energy management, achieving an optimal power split between the engine and battery while reducing Q-value overestimation and accelerating learning convergence. Simulation results across multiple standard driving cycles demonstrate that the proposed framework outperforms conventional distributed model predictive control (DMPC) and multi-agent deep deterministic policy gradient (MADDPG)-based methods in fuel economy, stability, and convergence speed, while maintaining battery state of charge (SOC) within safe limits. To facilitate future experimental validation, a dSPACE-based hardware-in-the-loop (HIL) architecture is designed to enable real-time deployment and testing of the proposed control framework.

Autonomous robotic navigation is essential in modern systems for revolutionising various industries that operate in both static and dynamic environments. To achieve this autonomous navigation, various conventional techniques that handle environment mapping, path planning, and motion control as individual modules often face challenges in addressing the complexities of autonomous navigation. Therefore, this paper presents an integrated technique that combines three essential components, such as environment mapping, path planning, and motion control, to enhance autonomous navigation performance. The first component, i.e., the mapping, utilises both binary and probabilistic occupancy maps to represent the environment. The second component is path planning, which incorporates various graph- and sampling-based algorithms such as PRM, A*, Hybrid A*, RRT, RRT*, and BiRRT, which are evaluated in terms of path length, computational time, and safety margin on various maps. The final component, i.e., motion control, utilises both conventional and advanced controller strategies such as PID, FOPID, SFC, and MPC, for better sinusoidal trajectory tracking. The four case studies for path planning and one case study on trajectory tracking on various occupancy maps demonstrated that the A* algorithm and MPC outperformed all the compared techniques in terms of optimal path length, computational time, safety margin, and trajectory tracking error. Thus, the proposed integrated approach reveals that the interplay between mapping fidelity, planning efficiency, and control robustness is vital for reliable autonomous navigation.

Enhancing fuel efficiency in hybrid electric vehicles (HEVs) requires energy management strategies (EMSs) that can operate effectively under nonlinear powertrain dynamics and uncertain, time-varying driving conditions. This paper proposes a deep reinforcement learning (DRL)- based EMS using the double actors regularized critics softmax deep deterministic policy gradient (DARC SD3) algorithm, which integrates Boltzmann-softmax value estimation, a dual-actor architecture, and critic regularization to improve learning stability and value-estimation accuracy. Simulation results show that the proposed DARC SD3 achieves faster convergence, improved state-of-charge (SOC) regulation, and reduced value estimation bias compared with DDPG, TD3, and baseline SD3. Under the FTP-75 driving cycle, the proposed EMS attains 94.6% of the dynamic programming (DP) benchmark fuel economy, while reducing engine transients and smoothing battery power flow. Further evaluation on an unseen composite driving cycle confirms that the trained policy maintains consistent fuel economy and SOC control, demonstrating strong generalization capability across diverse driving conditions.