Accurate dynamic models of human hands grasping haptic devices can help to inform stability analyses, control algorithms, device design, and technology development efforts. The current study develops and tests a lumped second-order dynamic model for nine subjects holding a haptic knob in a simple pinch grasp. Results include steadily increasing damping and stiffness parameters with increasing g...

1 Networked RFID in Industrial Control: Current and Future This paper introduces the notion of networked Radio Frequency Technology (RFID) and reviews the work of the Auto ID Center in providing a low cost, global networked RFID solution. The paper then examines the role of networked RFID in changing the nature of industrial control systems operations. In particular the notions of connectedness...

The high cost of model predictive control (MPC) technology has hampered its wide application in process industries beyond the refining/petrochemical industry. This work aims to increase the efficiency of MPC deployment. First a semi-automatic MPC system is introduced. It consists of three modules: an MPC control module, an online identification module and a control monitor module. The goal of t...

This paper describes recent improvements of the Automatic Control Telelab (ACT), a remote laboratory of automatic control developed at the University of Siena. In particular, it focuses on a new tool which allows students to perform system identification experiments on remote processes by means of both classical (statistical) and set-membership (deterministic) techniques. Through this machinery...

As a technology solution, radio frequency identification (RFID) has proven increased efficiency and accuracy within traditional production and inventory control environments. RFID also offers increased transparency, accountability, and quality across the healthcare industry. This paper provides an a priori perspective to implementing RFID applications in a hospital environment. The paper descri...

ISIAC (as Industrial System Identification for Advanced Control) is a new software package geared to meet the requirements of system identification for model predictive control and the needs of practicing advanced process control (APC) engineers. It has been designed to naturally lead the user through the different steps of system identification, from experiment planning to ready-to-use models....

Efficient numerical techniques for multivariable system identification and model reduction are investigated. The techniques are implemented in the system identification and model reduction toolboxes based on the Fortran 77 Subroutine Library in Control Theory (SLICOT). Besides highly performant Fortran drivers and computational routines, these toolboxes provide MATLAB or Scilab interfaces, impl...

In thia paper we investigate some important iasuer in system identification for the windsvrier approsch to robust adaptive control. In particular, correlation function estimates and power spectrum estimates are compared as methods for model validation. This comparison leads us to suggest a reliable procedure for deciding (1) when should we identify a better model, and (2) whether we have identi...

In this paper, a linear active disturbance rejection controller is proposed for a waste heat recovery system using an organic Rankine cycle process, whose model is obtained by applying the system identification technique. The disturbances imposed on the waste heat recovery system are estimated through an extended linear state observer and then compensated by a linear feedback control strategy. ...