This paper provides an overview of building structure modeling and control. It focuses on different types of control devices, control strategies, and sensors used in structural control systems. This paper also discusses system identification techniques and some important implementation issues, like the time-delay in the system, estimation of velocity and position from acceleration signals, and ...

The investigation of robot-environment interaction is the main aim of the RobotMODIC project at the Universities of Essex and Sheffield. The methods developed under this project model and characterise all aspects relevant to the robot’s operation: modelling of sensor perception (“environment identification” or simulation), sensor modelling, and task modelling. In this paper we describe a new pr...

The duality between time and frequency domain methods for linear systems is well known. It plays a crucial role for example in control systems design, and the domains are thought of complementing rather than competing. Quite recently, the full interplay and duality between the two domains have been clear also in system identi cation applications. In this contribution, this duality will be discu...

This paper describes an international research effort that has been working toward identification of vulnerabilities in industrial control systems, mitigation strategies to address vulnerabilities, and development of tools to prevent intrusion on such systems. The research represents over five years of externally funded work within the United States and a strong partnership between the U.S. ins...

A novel technique for closed-loop sytem identification of reduced-order models is presented. The method arrives at a process model and its corresponding compensator in an iterative fashion by introducing a series of step changes at the manipulated variable/setpoint. The bias introduced into the identification data set by the closed-loop system, coupled with a control-relevant prefilter, yields ...

Various methods can be used for nonlinear, dynamic-system identification and Gaussian process (GP) model is a relatively recent one. The GP model is an example of a probabilistic, nonparametric model with uncertainty predictions. It possesses several interesting features like model predictions contain the measure of confidence. Further, the model has a small number of training parameters, a fac...

The cross gramian matrix is a tool for model reduction and system identification, but it is only computable for square control systems. For symmetric control systems the cross gramian possesses a useful relation to the associated system’s Hankel singular values. Yet, many real-life models are neither square nor symmetric. In this work, concepts from decentralized control are used to generalize ...

Identification of unknown system by training the parameters adaptively using different fuzzy models has been proven an interesting research area over last few decades. The objective of this paper is to identify a standard fuzzy system using different gradient methods and discuss their characteristics. Approach of this work is to calculate the gradient of the appropriate cost function to minimiz...

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