Articles and discussion on proven methods and best practices in process
control is available at: http://www.controlguru.com
Here is a complete table of articles:
(visit: http://www.controlguru.com/pages/table.html )

Fundamental Principles of Process Control Motivation and Terminology of Automatic Process Control Sensors Should Be Fast, Cheap and Easy Model Fitting is Fundamental to Profitable Control

Graphical Modeling of Process Dynamics: Heat Exchanger Study Step Test Data From a Heat Exchanger Process Gain is the "How Far" Variable Process Time Constant is the "How Fast" Variable Dead Time is the "How Much Delay" Variable Validating Our Heat Exchanger FOPDT Model

Modeling Process Dynamics: Gravity Drained Tanks Study The Gravity Drained Tanks Process Dynamic Testing of the Gravity Drained Tanks Process Graphical Modeling of Gravity Drained Tanks Step Test Modeling Gravity Drained Tanks Data Using Software

Process Control Preliminaries A Controller’s "Process" Goes From Wire Out to Wire In The Normal or Standard PID Algorithm Sample Rate Critical to Analysis/Tuning Success Control is Easy; Good Data is Hard

Proportional Control - The Simplest PID Controller The P-Only Control Algorithm P-Only Control of the Heat Exchanger Shows Offset P-Only Disturbance Rejection of the Gravity Drained Tanks

Nonlinear Process Dynamics and Implications for Control Nonlinear Behavior and a Refined Tuning Recipe

Integral Action and PI Control Integral Action and PI Control PI Control of the Heat Exchanger PI Disturbance Rejection of the Gravity Drained Tanks The Challenge of Interacting Tuning Parameters Controller Tuning Should Not Be a Daily Ritual Integral (Reset) Windup and Jacketing Logic

Derivative Action and PID Control PID Control and Derivative on Measurement The Chaos of Commercial PID Control PID Control of the Heat Exchanger Measurement Noise Degrades Derivative Action

Signal Filters and the PID with Controller Output Filter Algorithm Using Signal Filters In Your PID Loop PID with Controller Output Filter PID with CO Filter Control of the Heat Exchanger

Closed-Loop Tuning Ziegler-Nichols Tuning Poor Choice for Production Processes Controller Tuning Using Set Point Driven Data

Evaluating Controller Performance Good Control Performance is a Matter Of Application Comparing Controller Performance Using Plot Data

Cascade Control and Feed Forward Control Control Strategies for Improved Disturbance Rejection Cascade Controller Design and Tuning Feed Forward Should Be as Dynamic as the Process

Non-Self Regulating (Integrating) Processes (by Bob Rice) Non-Self Regulating (Integrating) Process Behavior The Pumped Tank Process Shows Integrating Behavior Control of Integrating Processes Challenges Our Intuition

Advanced Control Strategies Why Advanced Control Projects are Always Profitable Definition of a Successful Control Project

Discrete Time Modeling of Dynamic Systems (by Peter Nachtwey) A Discrete Time Linear Model of the Heat Exchanger

Application - Boiler Level Control (by Allen Houtz) Cascade, Feed Forward and Boiler Level Control Dynamic Shrink/Swell and Boiler Level Control

Application - Distillation Control (by Jim Riggs) Distillation: Introduction to Control Distillation: Major Disturbances & First-Level Control Distillation: Inferential Temperature & Single-Ended Control Distillation: Dual Composition Control & Constraint Control

Fundamental Principles of Process Control Motivation and Terminology of Automatic Process Control Sensors Should Be Fast, Cheap and Easy Model Fitting is Fundamental to Profitable Control

Graphical Modeling of Process Dynamics: Heat Exchanger Study Step Test Data From a Heat Exchanger Process Gain is the "How Far" Variable Process Time Constant is the "How Fast" Variable Dead Time is the "How Much Delay" Variable Validating Our Heat Exchanger FOPDT Model

Modeling Process Dynamics: Gravity Drained Tanks Study The Gravity Drained Tanks Process Dynamic Testing of the Gravity Drained Tanks Process Graphical Modeling of Gravity Drained Tanks Step Test Modeling Gravity Drained Tanks Data Using Software

Process Control Preliminaries A Controller’s "Process" Goes From Wire Out to Wire In The Normal or Standard PID Algorithm Sample Rate Critical to Analysis/Tuning Success Control is Easy; Good Data is Hard

Proportional Control - The Simplest PID Controller The P-Only Control Algorithm P-Only Control of the Heat Exchanger Shows Offset P-Only Disturbance Rejection of the Gravity Drained Tanks

Nonlinear Process Dynamics and Implications for Control Nonlinear Behavior and a Refined Tuning Recipe

Integral Action and PI Control Integral Action and PI Control PI Control of the Heat Exchanger PI Disturbance Rejection of the Gravity Drained Tanks The Challenge of Interacting Tuning Parameters Controller Tuning Should Not Be a Daily Ritual Integral (Reset) Windup and Jacketing Logic

Derivative Action and PID Control PID Control and Derivative on Measurement The Chaos of Commercial PID Control PID Control of the Heat Exchanger Measurement Noise Degrades Derivative Action

Signal Filters and the PID with Controller Output Filter Algorithm Using Signal Filters In Your PID Loop PID with Controller Output Filter PID with CO Filter Control of the Heat Exchanger

Closed-Loop Tuning Ziegler-Nichols Tuning Poor Choice for Production Processes Controller Tuning Using Set Point Driven Data

Evaluating Controller Performance Good Control Performance is a Matter Of Application Comparing Controller Performance Using Plot Data

Cascade Control and Feed Forward Control Control Strategies for Improved Disturbance Rejection Cascade Controller Design and Tuning Feed Forward Should Be as Dynamic as the Process

Non-Self Regulating (Integrating) Processes (by Bob Rice) Non-Self Regulating (Integrating) Process Behavior The Pumped Tank Process Shows Integrating Behavior Control of Integrating Processes Challenges Our Intuition

Advanced Control Strategies Why Advanced Control Projects are Always Profitable Definition of a Successful Control Project

Discrete Time Modeling of Dynamic Systems (by Peter Nachtwey) A Discrete Time Linear Model of the Heat Exchanger

Application - Boiler Level Control (by Allen Houtz) Cascade, Feed Forward and Boiler Level Control Dynamic Shrink/Swell and Boiler Level Control

Application - Distillation Control (by Jim Riggs) Distillation: Introduction to Control Distillation: Major Disturbances & First-Level Control Distillation: Inferential Temperature & Single-Ended Control Distillation: Dual Composition Control & Constraint Control