Model Predictive Control with Constraints

Course Outline

Model Predictive Control (MPC) has a long history in the field of control engineering. It is one of the few areas that has received on-going interest from researchers in both the industrial and academic communities. Three major aspects of model predictive control make the design methodology attractive to both engineers and academics. The first aspect is the design formulation, which uses a completely multivariable system framework where the performance parameters of the multivariable control system are related to the engineering aspects of the system; hence, they can be understood and 'tuned' by engineers. The second aspect is the ability of method to handle both 'soft' constraints and hard constraints in a multivariable control framework. This is particularly attractive to industry where tight profit margins and limits on the process operation are inevitably present. The third aspect is the ability to perform process on-line optimization.

This subject covers the core materials of model predictive control system design and implementation.  Upon completion of this subject, you should have the essential skills to design and implement a model predictive control system for real-world applications. 

Teaching Schedule (lectures and tutorials and quizzes= 30 hours)

Model Predictive Control with Constraints

Lecture One (4 hrs=2hrs lecture+2hrs tutorial) Introduction to discrete-time systems

Key words: Sampling of continuous time systems, discrete state space models, poles and zeros of discrete-time systems, shift operators, z-transforms 

Lecture Two (4 hrs=2hrs lecture+2hrs tutorial) Discrete-time state feedback control and state estimation

Key words: controllability, observability, state feedback controller design, observer design

Lecture Three (4 hrs=2hrs lecture+2hrs tutorial) Introduction to model predictive control

Key words: History of model predictive control, control horizon, prediction horizon, moving horizon window, objective function, optimization 

Lecture Four  (4 hrs=2hrs lecture+2hrs tutorial)  Receding horizon control

Key words: receding horizon control, state estimation, state estimate model predictive control

Lecture Five (6 hrs=3hrs lecture+3hrs tutorial) Quadratic programming

Key words: quadratic objective function with equality constraints and inequality constraints, Lagrange multipliers, active set methods, Hildreth Quadratic programming.

Lecture Six (4 hrs=2hrs lecture+2hrs tutorial) Model predictive control with constraints-Key words: constraint formulations, input constraints, output constraints, state constraints, solving model control problem with constraints.

Lecture Seven (4 hrs=2hrs lecture+2hrs tutorial ) Model predictive control using Laguerre functions

Key words:  Laguerre functions, MPC design using Laguerre functions

Assessment

In-course assessment : 30 percent

Final examination: 70 percent;