Giulio Betti

A Robust MPC Algorithm for Offset-Free Tracking of Constant Reference Signals

A robust model predictive control algorithm solving the tracking and the infeasible reference problems for constrained systems subject to bounded disturbances is presented in this technical note. The proposed solution relies on three main concepts: 1) the reformulation of the system in the so-called velocity form to obtain offset-free tracking when constant disturbances are present, 2) the use of a tube-based approach to cope with non-constant but bounded disturbances, 3) the use of reference outputs as arguments of the optimization problem to cope with infeasible references. Convergence results are derived by suitably defining the auxiliary control law and the terminal set used in the problem formulation.

Development of a Control-Oriented Model of Floating Wind Turbines

This paper deals with the development of a simplified, control-oriented mathematical model of an offshore variable speed wind turbine with tension leg platform. First, the model is derived with the goal of describing the most relevant physical phenomena of the turbine/platform dynamics, while limiting its complexity. The unknown model parameters are identified and a model validation phase is carried out using Fatigue, Aerodynamics, Structures, and Turbulence (FAST), an accurate reference model available in the literature. Then, an H∞ controller is designed for above-rated power operating conditions. The ability of the controller to attenuate the effect of wind variations and waves is tested in simulation both on the small-scale simulation model and on the FAST simulator.

An Approach to Distributed Predictive Control for Tracking–Theory and Applications

In this brief paper, a new distributed model predictive control algorithm for the solution of the tracking problem is presented for systems made by a number of dynamically coupled subsystems. The method is developed according to a hierarchical structure, and it guarantees that state and control constraints are fulfilled and that the controlled outputs reach the prescribed reference values whenever possible, or their nearest feasible value when feasibility problems arise. The algorithm is used for control of a small fleet of unicycle robots, and of a simulated four-tank system.

Distributed predictive control for tracking constant references

This paper presents a Distributed Predictive Control (DPC) method for tracking piecewise constant reference signals. The system under control is assumed to be composed by a number of non-overlapping subsystems interconnected through states and inputs. The algorithm is non cooperative, based on neighbor-to-neighbor communication, does not require an iterative exchange of information among neighbors and relies on the robustness properties of the “tube-based” approach developed of the design of robust model predictive controllers. Convergence results are stated and a simulation example is reported to illustrate the performance of DPC.

Distributed predictive control of continuous-time systems

Abstract In the last years focus has been put in the development of distributed Model Predictive Control (MPC) algorithms. With a few exceptions, they have been mostly developed in the discrete-time framework. However, discretization of large-scale systems may destroy the sparsity of the original continuous-time models, making distributed control design and implementation more difficult. Also, more in general, discrete-time control of continuous-time systems does not allow to consider the process inter-sampling behavior. In this paper we present a novel non-cooperative distributed predictive control algorithm for continuous-time systems based on robust MPC concepts. The convergence properties of the proposed control scheme are stated, and its realizability is tested through a simulation case study.

Distributed MPC: A Noncooperative Approach Based on Robustness Concepts

The Distributed Predictive Control (DPC) algorithm presented in this chapter has been designed for control of an overall system made by linear discrete-time dynamically interconnected subsystems. It consists of a non-cooperative, non-iterative algorithm where a neighbor-to-neighbor transmission protocol is needed. The DPC algorithm enjoys the following properties: (i) state and input constraints can be considered; (ii) convergence is guaranteed; (iii) it is not necessary for each subsystem to know the dynamical models of the other subsystems; (iv) the transmission of information is limited.

Decentralized predictive control for tracking constant references

This paper describes a novel Decentralized Predictive Control (DePC) technique for tracking constant reference signals. The controlled system is supposed to be constituted by a set of non-overlapping subsystems coupled by states and inputs. First the offset-free tracking problem is recast as a regulation one by reformulating the plant model in the so-called “velocity-form”; secondly the decentralized control problem is solved by resorting to “tube-based” robust MPC, where dynamic interactions between subsystems are interpreted as perturbations to be rejected. Convergence results are reported and a simulation example is provided to evaluate the performances of DePC.