Paper: A method for auto-tuning of PID control parameters

A class of controllers with integral action is shown to arise directly from appropriate system models. Via the zero-gain predictor approach, a corresponding class of hybrid self-tuning controllers is shown to have both integral action in the controller and offset removal in the tuning algorithm. Implementation details and some experimental results are given.

Self-tuning PID controllers: Algorithms and implementation

An architecture for expert system based feedback control

Direct Methods for Self-Tuning PID Regulators

Conventional controllers have transfer-functions with fixed parameters which are chosen from knowledge of plant dynamics or by manual tuning experiments. For cases where the dynamics are unknown a self-tuning algorithm can be used; with the advent of microprocessor technology such methods are easily implemented and are increasingly popular. This article describes the background to many current approaches to self-tuning design which are based on a Generalised Minimum Variance strategy. The plant is described as a discrete-time transfer-function with dead-time and with stochastic disturbances; a predictor model is then generated and the unknown parameters of this model are estimated from plant input/output data using a Recursive Least Squares estimator. An outline of the implementation of this self-tuner is given together with a discussion of additional features required to make it work in practice. One important limitation of the GMV approach is the need to know the plant’s dead-time; a new method called Generalised Predictive Control is shown to overcome this problem. For simple process-control applications PID regulators are entirely adequate and methods for the self-tuning of their proportional, integral and derivative terms are described — all of which are easy to embed into a microcomputer.

K. Tanaka

Papers

A Method for Auto-Tuning of PID Control Parameters

A New Optimization Method of PID Control Parameters for Automatic Tuning by Process Computer