F.J. Rivas | Process Control with MatLAB/Simulink: A Guide for Beginners (2025) [PDF] [EN]

MatLAB stands as a cornerstone in the realm of process control teaching, empowering students and educators alike with a comprehensive suite of tools for modeling, analysis, and implementation of control systems. The integration of MatLAB into the curriculum has revolutionized the way process control concepts are taught and understood, offering a dynamic platform for exploration and experimentation.
One of the most notable contributions of MatLAB to process control teaching is its robust support for system modeling and simulation. MatLAB's extensive library of built-in functions and toolboxes offers students access to a wide range of analysis and design techniques for control systems. From classical PID control to advanced model predictive control (MPC) algorithms, MatLAB provides students with the flexibility to explore various control strategies and methodologies, gaining insights into their performance and robustness in different scenarios.

TABLE OF CONTENTS

PREFACE

APPROACHING THE TIME PROCESS DYNAMIC
1. Time domain system dynamics
1.1. Linearization
1.2. Time invariant first order processes
1.3. Time invariant second order processes

THE LAPLACE DOMAIN
2. Laplace domain system dynamics
2.1. Inverse Laplace transform by Heaviside expansion
2.2. Differential equations systems solution by Laplace transform
2.3. The transfer function concept
2.4. Transfer Function determination from empirical data
2.5. Block algebra. Block diagram simplification (Masonґs rule)

FREQUENCY DOMAIN
3. Frequency domain
3.1. Graphic representation of the frequency response
3.1.1. Nyquist Diagram (polar graph or representation in the G-plane)
3.1.2. Bode diagram
3.1.3. Black diagram

FEEDBACK CONTROL
4. Feedback control. Proportional integral derivative controllers
4.1. Open versus closed loops
4.2. Proportional controllers
4.3. Proportional integral controllers
4.4. Proportional, integral, derivative controllers

STABILITY OF CLOSED LOOP CONTROL SYSTEMS
5. Stability analysis in system control
5.1. General Criteria
5.2. Root Locus analysis. Processes without dead time
5.3. Bode stability criteria
5.4. Nyquist stability criteria

CONTROLLER TUNING
6. PID controllers tuning. Compensators design
6.1. Tuning of Feedback Controllers
6.2. Tuning based on response quality
6.2.1. Criteria Based on a Specific Response Feature
6.2.2. Criteria Based on Entire Response
6.3. Tuning techniques. PID tuning methods
6.3.1. Methods Based on Approximate Models
6.4. Methods based on detailed process models
6.5. Compensator tuning
6.5.1. Lead compensators
6.5.2. Lag compensators
6.5.3. Lead-Lag compensators

STATE SPACE APPROACH
7. State space control
7.1. Space State representation in canonical forms
7.1.1. Controllable canonical form
7.1.2. Observable canonical form
7.1.3. Diagonal canonical form
7.1.4. Jordan canonical form
7.1.5. Modal and companion MATLAB canonical forms
7.2. Solving State Space systems
7.3. Design of control system in state space dominion. The regulator control strategy
7.3.1. Pole placement
7.3.2. Linear Quadratic Regulator (LQR) control
7.4. Design of control system in state space. The servo control strategy

DISCRETE SYSTEMS
8. Discrete time process control
8.1. The Z transform
8.2 The pulse transfer function
8.3. Closed loop response for discrete systems
8.4. Stability of discrete systems
8.5. Digital controllers tuning
8.5.1. Digitalization of continuous tuned controllers
8.5.2. Discrete-time based tuning

9. Used bibliography

10. Appendix