SEQUENTIAL LOOP CLOSING IDENTIFICATION OF MULTIVARIABLE PROCESSES USING THE BIASED RELAY FEEDBACK METHOD

Each controller in multiloop control systems for multivariable processes can be tuned sequentially with the ultimate information for the paired input and output while former loops have been closed, and hence, single-input single-output autotuning methods can be applied. In this sequential autotuning for multiloop control systems, several iterations are usually required for better control performances. Especially when pairings are undesirable, the autotuning sequences should be repeated with correct pairings, which result in long field experiments. Here, to avoid this drawback, a simple method to identify process models while loops are being sequentially tuned is proposed. The identified models can be used to correct pairings of multiloop control systems and to improve tuning performances without several field iterations. In addition, they can be used to obtain model-based control systems such as decoupling control systems.     

IDENTIFICATION OF TRANSFER FUNCTION MODELS FROM THE RELAY FEEDBACK TEST

A MIMO process is considered to be composed of several single-input-multi-output (SIMO) processes. In this article, a method for identifying such SIMO process is devised. To identify each SIMO process, a single run of relay feedback experiment on a particular input is required. For each of such experiments, models of FOPDT (First-Order-Plus-Dead-Time) or of SOPDT (Second-Order-Plus-Dead-Time) are identified. Estimation of the parameters is carried out one-by-one and does not involve least square minimization. Direct application of these SIMO identification to MIMO processes is thus illustrated. The accuracy of each estimated gain can be controlled within one single run of experiment. A sufficient condition for the experiments to ensure robustness of the resulting model is presented. Thus, this proposed method can be applied to some MIMO processes even they are ill-conditioned.     

BP神经网络补偿快速成型系统闭环误差的研究

提出快速成型系统闭环误差反馈的必要性,将BP神经网络引入快速成型技术,建立了快速成型误差补偿模型,探求出一种快速成型系统闭环误差的反馈方法,并通过实例进行了验证。结果表明,基于BP神经网络控制的最大绝对误差和相对误差均比传统误差补偿方法大为降低。     

A PARAMETER PLANE METHOD FOR THE PID CONTROL OF A MULTIRATE, SAMPLED-DATA CHEMICAL REACTOR SYSTEM WITH A TRANSPORTATION LAG

The control of a multirate sampled-data, stirred-tank chemical reactor system using a parameter plane method is considered. Due to wide acceptance of proportional-plus-integral-plus-derivative (PID) control in the chemical process industries, a PID controller with a “slow-fast”multirate scheme is used for the chemical reactor system. Based on two related stability equations and using the PID gains as the adjustable parameters, the set of all possible PID gains to maintain the chemical reactor system's stability, and at the same time, to make the system having a specified gain margin, phase margin, damping ratio, and damping factor is determined. The effects of changing the integer N (which is the ratio of the sampling rates between a slow- and a fasl-samplcr)and (he basic sampling period Ton the set of PID gains satisfying the specifications are examined. The results for single-rate and multirate cases are also studied.