http://www.ee.ncu.edu.tw/~ee321101/English/journal/2000/A%20PID%20Type%20Fuzzy%20Controller%20Design%20with%20Single-Input%20Fuzzy%20logic.pdf

A PID type fuzzy controller with self-tuning scaling factors

Nonlinear PID control to improve the control performance of 2 axes pneumatic artificial muscle manipulator using neural network

This article provides a study of modern and classical approaches used for PID tuning and its applications in various domains. Most of the control systems that are implemented to date with the use of PID control because of its simple structure, ease of implementation, and active research in tuning the PID for a long time. The techniques reviewed in the paper are in the order from classical to modern optimization rules used for the PID tuning. This paper attempts to address the literature review of PID control in an era of control system and bio-medical applications. The development of classical PID to the integration of intelligent control to it, has been surveyed by consideration of various application domains. The primary purpose of this document is to provide a detailed point of information for the people to understand the command of PID in different application areas.

A review of PID control, tuning methods and applications

Automatic tuning of PID controllers based on transfer function estimation

In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control.

Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

A recursive algorithm for the automatic tuning of PID regulators is described. The tuning strategy is based on the minimisation of a performance index which considers the quadratic deviations of the predicted output, over the set point, as well as the control efforts, over a time horizon. The adaptive version of this algorithm follows an explicit scheme so that the applications are not limited to self-tuning in the commissioning phase. It can also be applied to parameter adaptive control. The method has been successfully tested and some results are presented.

P. Vega

Papers

Self-tuning predictive PID controller

Optimal Approaches to Self-Tuning PID Control

A Comparison of Identification Algorithms for Rapidly Time-Varying Parameters on a Real Process

A computer system for design and training on advanced control techniques

Fault Detection of Large Parameter Changes in a Wastewater Plant