Distributed Model Predictive Control: Theory and Algorithms
Event details
| Date | 21.05.2013 |
| Hour | 11:15 › 12:00 |
| Speaker |
Pontus Giselsson Bio: I am a postdoc researcher at the Department of Automatic Control since January 2013. My research interests include distributed optimization, distributed control, and model predictive control. |
| Location |
ME C2 405
|
| Category | Conferences - Seminars |
In this talk, we discuss various topics related to distributed model
predictive control (DMPC). DMPC is a control methodology for large-scale
dynamical systems that consist of several subsystems with a sparse
dynamic interaction structure. In this context, an optimization problem
that takes the system-wide performance into account is solved in
distributed fashion in each sample of the DMPC controller. Two main
topics are discussed in this talk: Theory and Algorithms.
Theory: Traditional methods to show stability and recursive feasibility
in model predictive control (MPC) include a terminal cost and a terminal
constraint set that usually involve variables from all subsystems. This
hinders distributed implementation of the solution algorithm and sets
requirements for new stability theory in DMPC. We will briefly present
some results in this direction. We will also show that these results
can, for some examples, increase the region of attraction significantly,
compared to using traditional MPC methods.
Algorithms: To enable distributed implementation of the DMPC controller,
dual decomposition is used to solve the optimization problem in
distributed fashion. In dual decomposition, a gradient method is
traditionally used to maximize the dual problem. However, gradient
methods are known for their slow rate of convergence. In this talk, we
will present different ways to improve the slow convergence rate in dual
decomposition, e.g.; by using fast gradient methods, by preconditioning
the problem data, and by incorporating second order information into the
distributed algorithm. We will, by an example, show that these methods
can reduce the number of iterations in dual decomposition by several
orders of magnitude, compared to if gradient methods are used.
predictive control (DMPC). DMPC is a control methodology for large-scale
dynamical systems that consist of several subsystems with a sparse
dynamic interaction structure. In this context, an optimization problem
that takes the system-wide performance into account is solved in
distributed fashion in each sample of the DMPC controller. Two main
topics are discussed in this talk: Theory and Algorithms.
Theory: Traditional methods to show stability and recursive feasibility
in model predictive control (MPC) include a terminal cost and a terminal
constraint set that usually involve variables from all subsystems. This
hinders distributed implementation of the solution algorithm and sets
requirements for new stability theory in DMPC. We will briefly present
some results in this direction. We will also show that these results
can, for some examples, increase the region of attraction significantly,
compared to using traditional MPC methods.
Algorithms: To enable distributed implementation of the DMPC controller,
dual decomposition is used to solve the optimization problem in
distributed fashion. In dual decomposition, a gradient method is
traditionally used to maximize the dual problem. However, gradient
methods are known for their slow rate of convergence. In this talk, we
will present different ways to improve the slow convergence rate in dual
decomposition, e.g.; by using fast gradient methods, by preconditioning
the problem data, and by incorporating second order information into the
distributed algorithm. We will, by an example, show that these methods
can reduce the number of iterations in dual decomposition by several
orders of magnitude, compared to if gradient methods are used.
Practical information
- General public
- Free
Organizer
- Colin Jones