Time delay systems - meromorphic functions control approach

The paper brings an engineering acceptable approach for anisochronic controllers. The method is based on algebraic tools in the ring of RQ-meromorphic functions and it was developed for a wide class of delayed systems. This contribution deals with so-called anisochronic systems which include delays also in dynamics. Both stable and unstable systems are assumed. The control synthesis consists in the solution of the Bèzout identity and Youla-Kučera parameterization resulting in the Smith-like control structure. A final controller can be tuned by a suitable choice of a scalar real parameter. Among many others tuning methods, the equalization method is adopted. The approach is suitable also for high order dynamics approximation and autotuning procedures. First order stable and unstable simulation examples are presented. Copyright © 2007 IFAC

Fractional Order Phase Shaper Design with Routh's Criterion for Iso-damped Control System

Phase curve of an open loop system is flat in nature if the derivative of phase with respect to frequency is zero. With a flat phase curve, the corresponding closed-loop system exhibits an iso-damped property i.e. maintains constant overshoot with the change of gain and with other parametric variations. In recent past application, fractional order (FO) phase shapers have been proposed by contemporary researchers to achieve enhanced parametric robustness. In this paper, a simple Routh tabulation based methodology is proposed to design an appropriate FO phase shaper to achieve phase flattening in a control loop, comprising a system, controlled by a classical PID controller. The method is demonstrated using MATLAB simulation of a second order DC motor plant and also a first order with time delay system.Comment: 4 pages, 4 figures; Proceedings of INDICON 2009 - An IEEE India Council Conference, art. no. 5409434, Dec. 2009, Gujara

Algebraic principles as a tool for energy saving

This paper discusses algebraic approaches of control design for a set of Single Input - Single Output (SISO) delayed systems that are further developed and discussed. The first principle utilises a special ring RQM, - a set of RQ-meromorphic functions. The second one is based on a ring of proper and stable rational functions RPS and can be considered as a special case. Controller parameters are derived through the general solution of linear Diophantine equations in the appropriate ring. A final controller can be tuned by the scalar real parameter m0>0. The methodology is illustrated by a comparison with another approach, some analyses of a tuning parameter and example. The simulations are performed in the Matlab environment. Copyright © 2020, AIDIC Servizi S.r.l

Applications of neural networks to control systems

Tese de dout., Engenharia Electrónica, School of Electronic Engineering Science, Univ. of Wales, Bangor, 1992This work investigates the applicability of artificial neural networks to control systems. The following properties of neural networks are identified as of major interest to this field: their ability to implement nonlinear mappings, their massively parallel structure and their capacity to adapt. Exploiting the first feature, a new method is proposed for PID autotuning. Based on integral measures of the open or closed loop step response, multilayer perceptrons (MLPs) are used to supply PID parameter values to a standard PID controller. Before being used on-line, the MLPs are trained offline, to provide PID parameter values based on integral performance criteria. Off-line simulations, where a plant with time-varying parameters and time varying transfer function is considered, show that well damped responses are obtained. The neural PID autotuner is subsequently implemented in real-time. Extensive experimentation confirms the good results obtained in the off-line simulations. To reduce the training time incurred when using the error back-propagation algorithm, three possibilities are investigated. A comparative study of higherorder methods of optimization identifies the Levenberg-Marquardt (LM)algorithm as the best method. When used for function approximation purposes, the neurons in the output layer of the MLPs have a linear activation function. Exploiting this linearity, the standard training criterion can be replaced by a new, yet equivalent, criterion. Using the LM algorithm to minimize this new criterion, together with an alternative form of Jacobian matrix, a new learning algorithm is obtained. This algorithm is subsequently parallelized. Its main blocks of computation are identified, separately parallelized, and finally connected together. The training time of MLPs is reduced by a factor greater than 70 executing the new learning algorithm on 7 Inmos transputers

The design of periodic excitations for dynamic system identification

System identification techniques are developed for modelling linear and nonlinear systems. The main results of the work are concerned with the design and utilisation of periodic perturbation signals in general areas of time- and frequency-domain system identification. A design strategy is given for a new class of perturbation signals, together with examples of their use in system identification applications. Signal processing procedures are developed for the practical treatment of drift disturbances and transient effects, and also for the detection of nonlinear contributions to the measurement data. The techniques rely completely on the periodicity of the excitation, and so the advantageous properties of periodic input signals are considered in detail. The use of periodic excitations in discrete- and continuous-time nonlinear system identification is also reported, with the identification methods illustrating the worth of frequency-domain measurements in this area. An automatic tuning procedure for PID controllers is also developed, which illustrates an application of system identification techniques to control problems