industry can reap great economic benefi ts. It must also be pointed out that most of the industrial control loops are of PID type (about 90%) and subject to stochastic disturbances. It will be of practical value and interest to fi nd the maximum CLPI that can be achieved by a PI(D) controller in the control loop, which is referred to as PI(D) achievable performance, and attain it through proper tuning. Ko and Edgar (1998) proposed the Approximate Stochastic Disturbance Rejection (ASDR) technique to determine PI achievable performance. Their technique assumes the availability of a process model and uses routine operating data to approximately determine the noise model followed by estimation of the PI achievable performance. However, in many cases, the process model is not known. In such cases, it is diffi cult to fi nd the PI achievable performance using ASDR technique. Agrawal and Lakshminarayanan (2003) in their work do not assume a This work presents a practical scheme to attain PI achievable performance (the performance achievable with a proportional integral (PI) controller) for linear SISO processes with dead time driven by stochastic disturbances. It uses a combination of a quantitative measure called Control Loop Performance Index (CLPI) and signature plots of the closed loop disturbance impulse response (IR) to arrive at PI achievable performance in an iterative manner. The proposed methodology requires only routine operating data and knowledge of the process delay, and its effi cacy is illustrated through several case studies.On présente dans ce travail un schéma pratique pour atteindre la performance PI réalisable (performance réalisable avec un contrôleur à intégrale proportionnelle) pour des procédés SISO linéaires avec un temps mort induit par des perturbations stochastiques. Ce schéma utilise une combinaison d'une mesure quantitative appelée "Indice de performance à boucle de contrôle" (CLPI) et des graphes de la signature de la réponse à des pulses de perturbation en boucle fermée (IR) pour atteindre la performance PI réalisable de manière itérative. La méthodologie proposée requiert seulement des données opératoires de routine et la connaissance du retard dans le procédé. Son effi cacité est illustrée par plusieurs études de cas.
We describe an approach that is useful in deciding if significant benefits, in terms of control loop performance index (through variability reduction), will be achieved by a change in control loop configuration from simple feedback (SFB) to cascade control. The problem is considered in a stochastic setting and solved using the variance decomposition technique. The proposed methodology requires only routine operating data from an existing simple feedback control loop and knowledge of the process delays. Several simulation examples and one experimental case study exemplify the utility of this approach.
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