Paola Falugi

Predictive control for constrained systems with polytopic uncertainty

The paper addresses predictive control for polytopic discrete-time systems subject to input and state constraints. The objective is to optimize nominal performance while guaranteeing robust stability and constraint satisfaction. The latter goal is achieved by exploiting robust invariant sets under linear and nonlinear control laws. The feasibility domain is evaluated for open-loop and closed-loop input optimization.

Multi-model predictive control based on LMI: from the adaptation of the state-space model to the analytic description of the control law

This article proposes an explicit robust predictive control solution based on linear matrix inequalities (LMIs). The considered predictive control strategy uses different local descriptions of the system dynamics and uncertainties and thus allows the handling of less conservative input constraints. The computed control law guarantees constraint satisfaction and asymptotic stability. The technique is effective for a class of nonlinear systems embedded into polytopic models. A detailed discussion of the procedures which adapt the partition of the state space is presented. For the practical implementation the construction of suitable (explicit) descriptions of the control law are described upon concrete algorithms.

Robust predictive control of the furuta pendulum

In this paper, a Linear Parameter Varying (LPV) model of the Furuta pendulum is derived. Based on this model, a balancing controller is designed using robust predictive control techniques. Invariant set theory is used to accurately characterise the region of the state space in which the balancing controller is effective. An energy-based control law is used to swing up the pendulum. Invariant sets calculated for the balancing controller can be exploited to systematically determine the switching condition between swing up and balancing controllers. In practice, the pendulum may be swung towards the upright position with varying speed and the speed of the rotating arm may also vary. Based on this physical insight, the speed of the rotating arm is chosen as gain scheduling variable. It is shown that this strategy is effective in achieving a more consistent swing up and balancing behaviour which is not sensitive to the performance of the swing up controller.

Asymptotic tracking for constrained monotone systems

This note addresses tracking control of nonlinear discrete-time monotone systems subject to input and state constraints. Forcing saturation on a previously designed controller may, in general, lead to destabilization or, at least, result in constraint violation and performance losses. Hereby, it is shown that for a certain class of nonlinear monotone systems it is possible to design a static nonlinear output feedback which, saturated among suitable state-dependent bounds, is able to guarantee constraint satisfaction and asymptotic tracking of piecewise constant references, with a moderate online computational burden. Simulation experiments concerning the synthesis of a protein demonstrate the effectiveness of the proposed control strategy.

Explicit robust multi-model predictive control

The paper deals with robust predictive control based on a LMI approach. With respect to the well established case of linear models, with a global polytopic uncertainty, in the present approach the conservativeness reduction is assured by allowing different local descriptions of the uncertainty. The prediction model can thus be interpreted as a multi-model description of the plant to be controlled. The techniques is effective for a large class of nonlinear systems embedded into polytopic models. For the practical implementation the construction of suitable (explicit) descriptions of the control law are described upon concrete algorithms.

Asymptotic tracking for state-constrained monotone systems

This paper addresses tracking control of nonlinear discrete-time monotone systems subject to input and state constraints. Forcing saturation on a previously designed controller may, in general, lead to destabilization or, at least, result in constraint violation and performance losses. Hereby it is shown that for a certain class of nonlinear monotone systems it is possible to design a static nonlinear output feedback which, saturated among suitable state-dependent bounds, is able to guarantee constraint satisfaction and asymptotic tracking of piecewise constant references, with a moderate on-line computational burden.

Tracking control for constrained monotone systems

Abstract Tracking control of nonlinear systems subject to constraints on the input is a challenging issue in control design. Forcing saturation on a previously designed controller may in general lead to destabilization or at least result in performance losses. Hereby it is shown that for a certain class of nonlinear monotone systems it is possible to design a suitable static nonlinear output feedback which stabilizes the system and preserves stability under control saturation.

PARAMETER BOUNDED ESTIMATION FOR QUASISPECIES MODELS OF MOLECULAR EVOLUTION

Abstract The Quasispecies models identification for Evolutionary Dynamics is considered in a worst-case deterministic setting. These models analyze the DNA and RNA evolution or describe the population dynamics of viruses and bacteria. In this paper we identify the Fitness and the Replication Probability parameters of a genetic sequences, subject to a set of stringent constraints to have physical meaning and to guarantee positiveness. The conditional central estimate and the Uncertainty Intervals are determined. The effectiveness of the proposed procedure has been illustrated by means of simulation experiments while tests on real data are under concern.

Set-point tracking for a class of constrained nonlinear systems with application to a CSTR

The paper considers a case-study of constrained nonlinear control relative to the temperature control of a continuous stirred tank reactor (CSTR). The use of linear parameter varying (LPV) embedding techniques along with admissible set theory allow fast and safe tracking in presence of stringent input and state constraints.

LPV Predictive Control of the Stall and Surge for Jet Engine 1

Abstract Predictive control of constrained LPV systems is applied to the model of the stall and surge control for jet engine compressors. The objective of the used technique is to optimize nominal performance while guaranteeing robust stability and constraint satisfaction. This is achieved by exploiting invariant sets and a receding horizon optimization procedure which provides on-line a non-linear correction to a gain-scheduled linear feedback designed off-line. A comparison with a contractive gain-scheduling control technique is also shown.