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Control and Systems Engineering

The design of control systems for dynamic processes, including feedback control, robotics, automation, adaptive systems, and the integration of sensors and actuators in intelligent systems

4 papers

Papers

An Event-based State Estimation Approach for Positive Systems with Positive Observers

This paper introduces an event-based observer to estimate the states of "positive systems" (systems with non-negative values, like water tank levels) in networked environments. By only transmitting data when significant changes occur, the proposed method drastically reduces communication traffic while ensuring system stability and non-negative estimates. Simulations on a three-tank system demonstrated a 78.75% data reduction compared to traditional periodic sampling.

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Control and Systems Engineering Oct 11, 01:25 PM

Learning Unified Force and Position Control for Legged Loco-Manipulation

This paper presents a novel unified policy for legged robots that enables joint force and position control without relying on physical force sensors, instead estimating forces from historical robot states. Through reinforcement learning and experiments on quadrupedal and humanoid robots, the policy demonstrates enhanced success rates (approx. 39.5% higher) in contact-rich tasks like wiping and opening cabinets compared to position-only methods. However, the force estimation accuracy can degrade in high-frequency interactions and at the edges of the robot's workspace, and sim-to-real gaps exist.

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Control and Systems Engineering Sep 28, 11:21 AM

Potential, challenges and future directions for deep learning in prognostics and health management applications

This paper reviews the potential, challenges, and future directions of deep learning in prognostics and health management (PHM). It highlights the opportunities and difficulties of applying deep learning to PHM applications, discussing aspects such as data processing, model development, and practical considerations for industrial settings.

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Control and Systems Engineering Jul 14, 10:36 AM

The effect of gamma value on support vector machine performance with different kernels

This paper explored the effect of the gamma parameter on SVM classifier performance using polynomial, RBF, and sigmoid kernels across five datasets. The results indicated an uneven influence of gamma on accuracy, with polynomial and sigmoid kernels showing greater sensitivity to changes in gamma than the RBF kernel. Optimal gamma values varied across datasets and kernel functions, suggesting the need for careful parameter tuning.

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Control and Systems Engineering Jul 14, 10:35 AM