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Control, Estimation and Optimization

Team

• Amélia Cristina Duque Caldeira Matos
• Ana Filipa de Castro Amarante e Ribeiro
• Fernando Armenio da Costa Castro e Fontes
• Igor Kornienko
• Jorge Leite Martins de Carvalho
• Luís Augusto Correia Roque
• Maria do Rosario Marques Fernandes Teixeira de Pinho
• Maria Margarida de Amorim Ferreira
• MD. Haider Ali Biswas
• Paulo Jorge de Azevedo Lopes dos Santos
• Sofia Oliveira Lopes

Objectives

Optimality conditions for constrained control problems will be pursued. By focussing on the
• extension of existing necessary conditions to more general constrained problems,
• strengthening of necessary conditions to guarantee non-degeneracy,
• study of the conditions regularity,
the range of applicability of currently known conditions of this kind will be extended.

This research group is committed to apply as much of the optimal control theory developed as possible. For this, collaborations with other groups in this unit and with other international research centres will be established. In particular, optimal control algorithms involving pseudo-spectral methods will be developed in collaboration with other research units.

A framework for Model Predictive Control already developed has proved to be a good way to address nonlinear systems that need discontinuous feedbacks to be stabilized. These include important classes of non-linear systems such as the non-holonomic systems frequently arising in robotics and other mechanical systems. Addressing specific under actuated mechanical systems using the approach developed is a research goal.

New estimation and system identification algorithms will be studied for: bilinear systems with general inputs, linear parameter varying systems with general scheduling signals, and hybrid systems
New algorithms will be studied for the: state estimation in nonlinear systems and hybrid systems, state and output prediction in nonlinear systems, and local prediction and estimation in cooperative systems with unreliable communication.
Joint applications of nonlinear predictive control and nonlinear system identification for bilinear plants will be investigated.