Comparison of low-complexity controllers in varying time-delay systems

Abstract : Motivated by the recent developments in networked control systems and control over wireless, this paper presents a comparison of five control algorithms that are based on PID, IMC and fuzzy gain scheduling techniques and discusses their performance in varying time-delay systems. The low complexity of the proposed algorithms makes their use attractive in resource-constrained environments such as wireless sensor and actuator networks. The control system consists of a controller, a simple process and an output delay in the feedback loop. Three different delay models are considered in this framework; constant, random, and correlated random delay. In addition to presenting modifications to the control algorithms to better fit the varying time-delay systems a delay-robust tuning method is proposed, and the performance of various controllers is evaluated using simulation. The results show the benefits of adapting the controller parameters based on delay measurement if its amplitude is significant with respect to the time-constant of the process. Nevertheless, the PID algorithm used in the study also performs well in all scenarios, and this is achieved by its careful tuning

Fuzzy Control of a Nonlinear Servomotor Model

: A nonlinear servomotor model with friction, saturation, backlash and motor starting voltage is presented in this paper. Fuzzy control and nonlinear PID control are compared using numerous computer simulations. A systematical method for tuning a PD-type fuzzy controller with training data is introduced. 1.0 Introduction Actual servomotors always have nonlinearities that have to be considered in careful controller design. Most important of these are amplifier saturation, load friction, backlash and motor starting voltage. Mathematical analysis that takes all these nonlinearities into account at the same time becomes extremely complex and experimental tuning is still frequently required. 4,6 Complexity is also the main disadvantage of some sophisticated control methods such as adaptive control or friction compensation. 3,5 With fuzzy inference control, however, it is possible to tune an accurate controller without any complicated mathematical analysis, which provides a large number ..

Ambient environmental effects in micro/nano handling

Peer reviewe

A PI- Power Control Algorithm for Cellular Radio Systems

Along with "distributiveness", convergence speed of power control is one of the most important criteria bywhichwe can determine the practical applicability of a given power control algorithm. Agoodpower control algorithm should quickly and distributively converge to the state where the system supports as many users as possible. This paper proposes a fast and distributed power control algorithm based on the well-known PI-controller. As in the paper byFoschini and Miljanic, we start with differential equation form of the controller and analyze its convergence properties in the case of feasible systems. The actual power control algorithm is then derived by discretization of the continuous time version. Using the distributed constrainedpower control (DCPC) as a reference algorithm, we carried out computational experiments on a CDMA system. The results indicate that our algorithm significantly enhances the convergence speed of power control

Estimating the number of persons in an unknown indoor environment by applying wireless acoustic sensors and blind signal separation

Abstract — Determining the number of persons and their locations in an unknown indoor environment is one of the first tasks that must be done in military, rescue or intelligence operations. If there is no functioning security system in the building, one must use a monitoring system, which is rapidly deployable but simultaneously as invisible as possible. Wireless sensor network is an attractive candidate to fill such requirements. Many sensor types are suitable for indoor situation modeling, but there are also several technical challenges rising from the limited energy and communication resources of the sensor nodes. This paper focuses on estimating the number of persons in an unknown indoor environment by using low-power acoustic sensors. The number of persons is estimated by applying blind signal separation to the acoustic signal collected by the sensor nodes. I