Antonio A. Alonso

Stabilization of distributed systems using irreversible thermodynamics

We connect thermodynamics and the passivity theory of nonlinear control. The storage function is derived from the convexity of the entropy and is closely related to the thermodynamic availability. We relate dissipation to positivity of the entropy production. In this form the supply function is a product of force and flow variables in deviation form. Feedback signals originate from intensive variables like temperature, pressure and composition. We show that the physical dimension of the system matters: The larger the distributed system is, the more difficult the stationary state may be to stabilize. Any chemical process can be stabilized by distributed PID control provided that the sensor and actuator locations are suitable. We apply the results to heat conduction and reaction diffusion equations.

Process systems, passivity and the second law of thermodynamics

Abstract In this paper we use the first and second laws of thermodynamics to motivate a theory for nonlinear process control. Our main tenets are: positive entropy production, boundedness of entropy in terms of energy, concavity of the entropy density and Helmholtz free energy as a storage function. These give the process system a causal input-output description, zero state detectability and stabilizability. To make the theory apply to practical systems we follow ideas from classical irreversible thermodynamics and extend the concept of entropy of the non-equilibrium by assuming local equilibrium.

From irreversible thermodynamics to a robust control theory for distributed process systems

Abstract In this paper we combine recent results that link passivity, as it is understood in systems theory, with concepts from irreversible thermodynamics to develop a robust control design methodology for distributed process systems. In this context, we show that passivity and stabilization of systems where non-dissipative phenomena are taking place is possible under very simple, finite dimensional control configurations. These include, boundary and high gain controllers, which combined with robust identification schemes should be able to provide efficient plant operation.

Dynamic analysis and control of biochemical reaction networks

In the present work, we combine the concepts and tools from Irreversible Thermodynamics and Control Theory in a contribution to unravel the origin of complex nonlinear behaviour in biochemical networks. Regarding cells as thermodynamic systems, we can consider dynamic evolution of intracellular processes in terms of the combined action of an endogenous entropy production and the entropy flux associated to chemicals passing through the control volume. Based on a generalized description of biochemical systems, a physically motivated storage function is constructed and used for stability analysis. In this way, the entropy flux of open systems can be meaningfully modified by efficient nonlinear control schemes capable of network stabilization, and irreversible thermodynamics provide us with the physical insight to further interpret the controlled response.

La teoría de redes en ingeniería de control: aplicación al análisis dinámico y al control de procesos

Los autores agradecen la financiacion recibida del Gobierno Espanol (Proyecto MCyT PPQ2001-3643 y DPI2004-0744-C04-03) y de la Xunta de Galicia (PGIDIT02-PXIC40209PN).

From Irreversible Thermodynamics to a Robust Control Theory for Distributed Process Systems

Abstract In this paper we combine recent results that link passivity, as it is understood in system’s theory, with concepts from irreversible thermodynamics to develop a robust control design methodology for distributed process systems. In this context, we show that passivity and stabilization of systems where non-dissipative phenomena are taking place is possible under very simple, finite dimensional control configurations. These include, boundary and high gain controllers, which combined with robust identification schemes should be able to provide efficient plant operation.