The field of control systems and robotics is rapidly evolving, with a focus on developing innovative solutions to complex problems. Recent research has explored the use of hybrid game control envelope synthesis, which enables the modeling of control problems for embedded systems like cars and trains as two-player hybrid games. This approach has led to the development of nondeterministic winning policies that can be used to synthesize control solutions. Another area of research has focused on evaluating robot program performance using power consumption driven metrics, which provides a more accurate assessment of a robot's physical impact. Additionally, there have been significant advancements in the development of data-driven energy consumption models for manipulators, which can be used to optimize energy efficiency and reduce costs. Noteworthy papers in this area include the introduction of a novel framework for assessing robot program performance from an embodiment perspective, and the development of a data-driven optimal control architecture for grid-connected power converters. These advancements have the potential to significantly impact the field of control systems and robotics, enabling the development of more efficient, reliable, and adaptive systems.
Advances in Control Systems and Robotics
Sources
Hybrid Game Control Envelope Synthesis
Evaluating Robot Program Performance with Power Consumption Driven Metrics in Lightweight Industrial Robots
EcBot: Data-Driven Energy Consumption Open-Source MATLAB Library for Manipulators
Surrogate-Enhanced Modeling and Adaptive Modular Control of All-Electric Heavy-Duty Robotic Manipulators
Memory Enhanced Fractional-Order Dung Beetle Optimization for Photovoltaic Parameter Identification
Fixed-Time Voltage Regulation for Boost Converters via Unit-Safe Saturating Functions
An Analogy of Frequency Droop Control for Grid-forming Sources
When are safety filters safe? On minimum phase conditions of control barrier functions
Deep Reinforcement Learning-Based Control Strategy with Direct Gate Control for Buck Converters
Aerial Target Encirclement and Interception with Noisy Range Observations
Robust Adaptive Discrete-Time Control Barrier Certificate
DeePConverter: A Data-Driven Optimal Control Architecture for Grid-Connected Power Converters
A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
Control Systems Analysis of a 3-Axis Photovoltatic Solar Tracker for Water Pumping
From Formal Methods to Data-Driven Safety Certificates of Unknown Large-Scale Networks
Shepherd Grid Strategy: Towards Reliable SWARM Interception
Online Safety under Multiple Constraints and Input Bounds using gatekeeper: Theory and Applications
Synthesis of Deep Neural Networks with Safe Robust Adaptive Control for Reliable Operation of Wheeled Mobile Robots