Design and experimental evaluation of a multivariable self-tuning PID controller

Abstract

In the paper, a new multivariable self-tuning PID controller design scheme is proposed which has a static matrix precompensator to reduce the interaction terms of the process transfer function matrix adjusted by an online estimator. The p £ p precompensated multivariate system is then controlled via 'p' univariate self-tuning PID controllers whose parameters are adjusted by a second identifier placed around the precompensated plant. The PID parameters are calculated online based on the relationship between the PID and generalised minimum variance control laws. The scheme is experimentally evaluated on a 2 £ 2 level plus temperature control system. Experimental results illustrate the effectiveness of this scheme.

System Change Detection Method Using Recurrent Neural Networks

“…However, if system performances are changed due to aging and change of operation conditions, fixed control parameters can often cause deterioration of control performances. If so, a real-time adjustment of control parameters on line is useful, such as self-tuning control [1]. If a control performance is in a good state, such a method is not always practical, and thus an approach to modify control parameters is demanded only if a control performance is degraded.…”

Adaptive PID Control System Design Based on ASPR Property of Systems

“…Furthermore, the control plays a very important role in the improvement of production quality, accuracy and in reducing production costs. As a result a great deal of attention has been focused on automatic or self tuning of PID controllers (Astrom & Hagglund, 1995), and in recent decades several kinds of auto-tuning PIDs including self-tuning schemes and adaptive control strategies have been proposed (Chang et al, 2003;Iwai et al, 2006;Kono et al, 2007;Ren et al, 2008;Tamura & Ohmori, 2007;Yamamoto & Shah, 2004;Yu et al, 2007). Unfortunately, most PID auto-tuning methods did not pay sufficient attention to the stability of the resulting PID control system and the tuned PID parameters did not guarantee the stability of the control system after any change of the systems.…”

A design of multiloop PID controllers with neural-net based decoupler

“…However, this is complicated and the problem with respect to the reliability remains, even though the exactly decoupled system is designed based on the model. Therefore, along with the references [12][13][14], the controlled object is approximately decoupled by the following static gain decoupler:…”

“…The design procedure of the proposed method is summarized as follow: Step 1. The static gain matrix H S [12][13][14] is designed based on the linear nominal model given by the linear part of Eq. (1).…”

“…Moreover, a design scheme of a multiloop PID control system for the augmented system constructed by the gain matrix decoupler mentioned above, has been reported [13,14]. However, it is difficult to sufficiently remove the effect of the mutual interactions by using only the static gain matrix, since the static decoupler just acts so as to remove the mutual interactions in the low frequency and the nonlinearities of the controlled object.…”