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May-Win Thein

Publications and source records attributed to May-Win Thein.

3 recordsLinked to original sources

Feedback Linearization and Control of a Grid-Forming Power Converter in an Islanded Microgrid

In an islanded setting, grid-forming inverters must regulate their terminal voltage without support from an external grid, even though the load current depends directly on that voltage. The usual approach is a cascaded proportional--integral (PI) controller, built on a fast inner current loop and a slower outer voltage loop, with feedforward terms used to compensate dq rotational coupling. However, this compensation is only exact at the operating point where the controller is tuned. This tutorial presents an alternative based on full-state feedback linearization. It is shown that the islanded inverter model has full relative degree, which allows exact state-space linearization with no internal or zero dynamics. A single feedback law cancels the main nonlinear effects; rotational coupling, resistive drops, and load conductance, so that the closed-loop system behaves like two independent double integrators. A standard pole-placement design is then used to shape the response. The controller is tested in MATLAB against a cascaded PI baseline under identical conditions at a 20 MW operating point, including reference tracking, load step disturbances, and parameter mismatch. The feedback-linearizing controller settles a reference step in 0.76 ms, while the PI controller does not reach the 2 % band within 50 ms. The cascaded PI controller shows better robustness to filter parameter mismatch due to its inner-loop integral action, which reduces steady-state errors under modeling uncertainty. Overall, the performance improvement and the robustness trade-off both come directly from the controller structures, rather than from tuning choices.

eess.SY

Optimal Kinodynamic Motion Planning Through Anytime Bidirectional Heuristic Search with Tight Termination Condition

This paper introduces Bidirectional Tight Informed Trees (BTIT*), an asymptotically optimal kinodynamic sampling-based motion planning algorithm that integrates an anytime bidirectional heuristic search (Bi-HS) and ensures the \emph{meet-in-the-middle} property (MMP) and optimality (MM-optimality). BTIT* is the first anytime MEET-style algorithm to utilize termination conditions that are efficient to evaluate and enable early termination \emph{on-the-fly} in batch-wise sampling-based motion planning. Experiments show that BTIT* achieves strongly faster time-to-first-solution and improved convergence than representative \emph{non-lazy} informed batch planners on two kinodynamic benchmarks: a 4D double-integrator model and a 10D linearized Quadrotor. The source code is available here.

cs.RO

Self-Supervised Learning-Based Path Planning and Obstacle Avoidance Using PPO and B-Splines in Unknown Environments

This paper introduces SmartBSP, an advanced self-supervised learning framework for real-time path planning and obstacle avoidance in autonomous robotics navigating through complex environments. The proposed system integrates Proximal Policy Optimization (PPO) with Convolutional Neural Networks (CNN) and Actor-Critic architecture to process limited LIDAR inputs and compute spatial decision-making probabilities. The robot's perceptual field is discretized into a grid format, which the CNN analyzes to produce a spatial probability distribution. During the training process a nuanced cost function is minimized that accounts for path curvature, endpoint proximity, and obstacle avoidance. Simulations results in different scenarios validate the algorithm's resilience and adaptability across diverse operational scenarios. Subsequently, Real-time experiments, employing the Robot Operating System (ROS), were carried out to assess the efficacy of the proposed algorithm.

cs.RO