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Integral Controller (integral + controller)
Selected AbstractsThe application of NN technique to automatic generation control for the power system with three areas including smes unitsEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 4 2003A. Demirören The study includes an application of layered neural network controller to study automatic generation control (AGC) problem of the power system, which contains superconducting magnetic energy storage (SMES) units. The effectiveness of SMES unit over frequency oscillations improvement against load perturbations in power system is well known. In addition, the proposed control scheme provides the steady state error of frequency and inadvertent interchange of tie-lines to be maintained in steady state values. The power system considered has three areas two of which including steam turbines while the other containing a hydro turbine, and all of them contain SMES units, in addition. In the power system each area with a steam turbine contains the non-linearity due to reheat effect of the steam turbine and all of the areas contain upper and lower constraints for generation rate. Only one neural network (NN) controller, which controls all the inputs of each area in the power system, is considered. In the NN controller, back propagation-through-time algorithm is used as neural network learning rule. The performance of the power system is simulated by using conventional integral controller and NN controller for the cases with or without SMES units in all areas, separately. By comparing the results for both cases, it can be seen that the performance of NN controller is better than conventional controllers. [source] Identification and fine tuning of closed-loop processes under discrete EWMA and PI adjustmentsQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 6 2001Rong Pan Abstract Conventional process identification techniques of a open-loop process use the cross-correlation function between historical values of the process input and of the process output. If the process is operated under a linear feedback controller, however, the cross-correlation function has no information on the process transfer function because of the linear dependency of the process input on the output. In this paper, several circumstances where a closed-loop system can be identified by the autocorrelation function of the output are discussed. It is assumed that a proportional integral controller with known parameters is acting on the process while the output data were collected. The disturbance is assumed to be a member of a simple yet useful family of stochastic models, which is able to represent drift. It is shown that, with these general assumptions, it is possible to identify some dynamic process models commonly encountered in manufacturing. After identification, our approach suggests to tune the controller to a near-optimal setting according to a well-known performance criterion. Copyright © 2001 John Wiley & Sons, Ltd. [source] Application of two-loop robust control to air-conditioning systems,ASIAN JOURNAL OF CONTROL, Issue 6 2009Gongsheng Huang Abstract This paper presents the design and application of a two-loop robust controller for temperature control in air-conditioning systems. A Takagi-Sugeno fuzzy model with uncertain local models is developed to describe the associated nonlinearities and uncertainties in the operation of the air handling units. Parallel distributed compensation is used to design the global control law. The local control law consists of two loops: an inner-loop integral controller and an outer-loop min-max predictive controller with short prediction horizon. A discounting scheme is developed to weight the contribution of the two loops. Experimental results are presented which show that the proposed strategy can achieve acceptable control performance with a minimum of onsite tuning. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Discrete-time low-gain control of linear systems with input/output nonlinearitiesINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 12 2001T. Fliegner Abstract Discrete-time low-gain control strategies are presented for tracking of constant reference signals for finite-dimensional, discrete-time, power-stable, single-input, single-output, linear systems subject to a globally Lipschitz, non-decreasing input nonlinearity and a locally Lipschitz, non-decreasing, affinely sector-bounded output nonlinearity (the conditions on the output nonlinearities may be relaxed if the input nonlinearity is bounded). Both non-adaptive and adaptive gain sequences are considered. In particular, it is shown that applying error feedback using a discrete-time ,integral' controller ensures asymptotic tracking of constant reference signals, provided that (a) the steady-state gain of the linear part of the plant is positive, (b) the positive gain sequence is ultimately sufficiently small and (c) the reference value is feasible in a very natural sense. The classes of input and output nonlinearities under consideration contain standard nonlinearities important in control engineering such as saturation and deadzone. The discrete-time results are applied in the development of sampled-data low-gain control strategies for finite-dimensional, continuous- time, exponentially stable, linear systems with input and output nonlinearities. Copyright © 2001 John Wiley & Sons, Ltd. [source] | ||||||||||