Advanced Process Control

Introduces a range of advanced process control methods based on multivariable & model predictive control strategies. Topics covered include model development; plant testing, time series models, non-linear models, applicability & model validation: multiple linear regression: identification; batch and recursive least squares estimators, instrumental variables, generalised and extended least squares: recursive estimation; initialisation, forgetting factors, windup, stability, convergence and consistency: Kalman filtering: adaptive & self-tuning control; controller structures, minimum variance, effect of time delay, adaptive PID, auto tuners: model based predictive control (MBPC); state space models and observers, internal model control (IMC), generalised predictive control (GPC): non-linear control; generic model control, globally linearising control.

Demonstrations and case studies are provided based upon proprietary MBPC packages. Practical classes using MATLAB & SIMULINK provide hands-on experience of regression, estimation, MBPC and filtering. [details]