1.2 Trends in process control
1.3 Modelling and Identification
1.4 Adaptive Control
1.5 Model-based Predictive Control (MPC)
1.6 Robust control, frequency domain control and optimal control
1.7 Artificial Intelligence Techniques
2.2 Objective of the control system
2.3 Data acquisition system
2.4 Dynamic simulation models of the field
2.5 Analysis of the dynamic response of the plant
2.6 Linear plant models
3.2 Fixed Ziegler-Nichols rule based PID controllers
3.3 Backup controller
3.4 Fine-tuned PID controller
4.2 Supervisory levels
4.3 Adaptive Ziegler-Nichols rule based PID controllers
4.4 Pole-placement adaptive PI controller
4.5 Simulation analysis of PID controllers
4.6 Plant results with adaptive PI controllers
5.2 Constrained generalized predictive control
5.3 Adaptive generalized predictive control
5.4 Robust adaptive model predictive control with bounded uncertainties
5.5 Gain scheduling generalized predictive control
5.6 GPC scheme with nonlinear prediction of the free response
6.2 Linear Quadratic Gaussian Optimal Control (LQG)
7.2 Incremental fuzzy PI control (IFPIC)
7.3 Fuzzy logic controller (FLC)
8.2 Fixed PID controller
8.3 Adaptive GPC controller
8.4 Robust adaptive GPC controller
8.5 Gain scheduling GPC controller
8.6 Nonlinear GPC controller
8.7 Frequency domain adaptive IMC controller
8.8 Robust LQG/LTR controller
8.9 Heuristic incremental fuzzy PI controller (IFPIC)
8.10 Heuristic fuzzy logic controller (FLC)
8.11 Conclusions