Non-Cooperative Distributed MPC with Iterative Learning

Cooperative distributed MPC for tracking

The problem of controlling large scale systems, usually divided into subsystems controlled by different agents, is usually solved using cooperative distributed control schemes, where the agents share open-loop information in order to improve closed-loop performance, (Rawlings and Mayne 2009, Chapter 6). In this paper we proposed a cooperative distributed linear model predictive control strategy...

Distributed cooperative nonlinear economic MPC ∗

Model predictive control (MPC) is a design technique that solves (typically on-line) optimization problems to determine a suitable feedback action. It is widely used because of the capability to deal with constraints and MIMO systems [3]. To treat practical industrial processes with MPC, use of so called economic cost functionals are proposed and considered for optimization in [1]. In this sche...

Iterative Learning - MPC: An Alternative Strategy

Cooperative Distributed MPC using the Alternating Direction Multiplier Method

We consider cooperative distributed model predictive control of a linear, timeinvariant, discrete-time plant, which consists of coupled subsystems. The cooperating controllers minimize a quadratic cost criterion subject to input and state constraints. We outline two approaches for such distributed controllers using the alternating direction multiplier method, which allows to obtain convergence ...

Extremum Seeking-based Iterative Learning Model Predictive Control (ESILC-MPC)

In this paper, we study a tracking control problem for linear time-invariant systems, with model parametric uncertainties, under input and states constraints. We apply the idea of modular design introduced in [1], to solve this problem in the model predictive control (MPC) framework. We propose to design an MPC with input-to-state stability (ISS) guarantee, and complement it with an extremum se...