On the design of Robust tube-based MPC for tracking

17th IFAC World Congress (IFAC'08)Seoul, Korea, July 6-11This paper deals with the design procedure of the recently presented robust MPC for tracking of constrained linear systems with additive disturbances. This controller is based on nominal predictions and it is capable to steer the nominal predicted trajectory to any target admissible steady state, that is retaining feasibility under any set point change. By means of the notion of tube of trajectories, robust stability and convergence is achieved. The controller formulation has some parameters which provides extra degrees of freedom to the design procedure of the predictive controller. These allow to deal with control objectives such as disturbance rejection, output offset prioritization or enlargement of the domain of attraction. In this paper, output prioritization method, LMI based design procedures and algorithms for the calculation of invariant sets are presented. The proposed enhanced design of the MPC is demonstrated by an illustrative example

MPC for tracking of piece-wise constant referente for constrained linear systems

16th IFAC World Congress. Praga (República Checa) 03/07/2005Model predictive control (MPC) is one of the few techniques which is able to handle with constraints on both state and input of the plant. The admissible evolution and asymptotically convergence of the closed loop system is ensured by means of a suitable choice of the terminal cost and terminal contraint. However, most of the existing results on MPC are designed for a regulation problem. If the desired steady state changes, the MPC controller must be redesigned to guarantee the feasibility of the optimization problem, the admissible evolution as well as the asymptotic stability. In this paper a novel formulation of the MPC is proposed to track varying references. This controller ensures the feasibility of the optimization problem, constraint satisfaction and asymptotic evolution of the system to any admissible steady-state. Hence, the proposed MPC controller ensures the offset free tracking of any sequence of piece-wise constant admissible set points. Moreover this controller requires the solution of a single QP at each sample time, it is not a switching controller and improves the performance of the closed loop system

Cooperative distributed MPC for tracking

This paper proposes a cooperative distributed linear model predictive control (MPC) strategy for tracking changing setpoints, applicable to any finite number of subsystems. The proposed controller is able to drive the whole system to any admissible setpoint in an admissible way, ensuring feasibility under any change of setpoint. It also provides a larger domain of attraction than standard distributed MPC for regulation, due to the particular terminal constraint. Moreover, the controller ensures convergence to the centralized optimum, even in the case of coupled constraints. This is possible thanks to the warm start used to initialize the optimization Algorithm, and to the design of the cost function, which integrates a Steady-State Target Optimizer (SSTO). The controller is applied to a real four-tank plant

Control of Solar Power Systems: a survey

9th International Symposium on Dynamics and Controlof Process Systems (DYCOPS 2010)Leuven, Belgium, July 5-7, 20109This paper deals with the main control problems found in solar power systems and the solutions proposed in literature. The paper first describes the main solar power technologies, its development status and then describes the main challenges encountered when controlling solar power systems.Ministerio de Ciencia y Tecnología DPI2008-05818Ministerio de Ciencia y Tecnología DPI2007-66718-C04-04Junta de Andalucía P07-TEP-0272

Robust control of the distributed solar collector field ACUREX using MPC for tracking

17th IFAC World Congress 2008. Seoul (Korea). 06/07/2008This paper presents the application of a robust model predictive control for tracking of piece-wise constant references (RMPCT) to a distributed collector field, ACUREX, at the solar power plant of PSA (Solar Plant of Almería). The main characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong disturbances in the process. The real plant is assumed to be modeled as a linear system with additive bounded uncertainties on the states. Under mild assumptions, the proposed RMPCT can steer the uncertain system in an admissible evolution to any admissible steady state, that is, under any change of the set point. This allows us to reject constant disturbances compensating the effect of then changing the setpoint

An Educational plant based on the Quadruple-tank process

7TH IFAC SYMPOSIUM ON ADVANCES IN CONTROL. 21/06/2006. MADRIDThis paper presents an experimental tank system developed at the University ofSeville for process control education. This plant is based on the well known quadruple-tank process and some modifications have been done in order to obtain a wide rangeof applications. The quadruple tank process is a multivariable laboratory plant of inter-connected tanks that can be easily configured to exhibit the effect of multivariable zero(minimum and non-minimum phase) on the system behavior, as well as the effect of nonlinear dynamics, saturation, constraints, etc.In the real plant implementation, the original structure of the process has been modifiedto offer a wide variety of uses for both educational and research purposes.Thus, differentplants can be configured such as one single tank, two or three cascaded tanks, a mixtureprocess and hybrid dynamics. Moreover the dynamics parameters of each tank can be setup by tuning the cross-section of the outlet hole of the tank. Furthermore, the real plant hasbeen implemented using industrial instrumentation and a PLC for the low level control.Supervision and control of the plant is carried out in a computer by means of OPC (Olefor Process Control) which allows one to connect the plant with a wide range of controlprograms such as LabView, Matlab or industrial SCADA.Ministerio de Ciencia y Tecnología DPI 2004-0744

Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm

This article presents a sparse, low-memory footprint optimization algorithm for the implementation of model predictive control (MPC) for tracking formulation in embedded systems. This MPC formulation has several advantages over standard MPC formulations, such as an increased domain of attraction and guaranteed recursive feasibility even in the event of a sudden reference change. However, this comes at the expense of the addition of a small amount of decision variables to the MPC's optimization problem that complicates the structure of its matrices. We propose a sparse optimization algorithm, based on an extension of the alternating direction method of multipliers, that exploits the structure of this particular MPC formulation. We describe the controller formulation and detail how its structure is exploited by means of the aforementioned optimization algorithm. We show closed-loop simulations comparing the proposed solver against other solvers and approaches from the literature

Tractable robust MPC design based on nominal predictions

Many popular approaches in the field of robust model predictive control (MPC) are based on nominal predictions. This paper presents a novel formulation of this class of controller with proven input-to-state stability and robust constraint satisfaction. Its advantages are: (i) the design of its main ingredients are tractable for medium to large-sized systems, (ii) the terminal set does not need to be robust with respect to all the possible system uncertainties, but only for a reduced set that can be made arbitrarily small, thus facilitating its design and implementation, (iii) under certain conditions the terminal set can be taken as a positive invariant set of the nominal system, allowing us to use a terminal equality constraint, which facilitates its application to large-scale systems, and (iv) the complexity of its optimization problem is comparable to the non-robust MPC variant. We show numerical closed-loop results of its application to a multivariable chemical plant and compare it against other robust MPC formulations.Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación PID2019-106212 RB-C41Junta de Andalucía - Fondo Europeo de Desarrollo Regional P20_00546Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación PDC2021-121120-C2

Agrupaciones de modelos locales con descripción externa. Aplicación a una planta de frío solar

En este artículo se presenta y analiza un método de creación de modelos formados por agrupaciones de submodelos locales lineales. La principal novedad es que la ponderación empleada con los submodelos no es la habitual, basada en el estado o parte del mismo sino que se toman exclusivamente señales instantáneas de entrada y salida del sistema. Esta elección simplifica algunos aspectos de la creación del modelo, manteniendo intacta la capacidad de representación, siendo ´esta comparada con otras técnicas. La simplificación aludida es importante pues acerca el método a la práctica industrial del control de procesos. La técnica de identificación resultante se ilustra mediante dos casos prácticos: un sistema simulado propuesto por Narendra y el sistema de captación de una planta real de producción de frío a partir de energía solar. En ambos casos se muestran los errores de generalización para la predicción a un paso y para la simulación usando gran cantidad de situaciones. Los resultados indican que es factible el uso del método propuesto como técnica simplificada aplicable en la industria.Comisión de la Comunidad Europea Proyecto de investigación HYCON FP6-51136

Control Predictivo para el seguimiento de referencias constantes aplicado a un motor lineal

XXVII Jornadas de Automática 6-9 de septiembre de 2006 Universidad de AlmeríaEste artículo trata de la aplicación de una estrategia de control predictivo (MPC) para seguimiento de referencias a un sistema real: un motor lineal. La formulación del MPC para seguimiento de referencias usada en este artículo fue presentado recientemente por los autores en [6]. Las principales novedades de este controlador son que: (1) utiliza un estado de referencia artificial como variable de decisión, (2) penaliza la desviación entre este estado artificial y el real en el coste y (3) como restricción terminal usa un conjunto invariante para tracking calculado con una ley local como se ver´a en la sección 3. Esta ley de control asegura la convergencia asintótica verificando las restricciones a cualquier referencia definida dentro de un conjunto de referencias admisibles, partiendo de cualquier punto inicial definido dentro del la región de atracción. El problema de optimización a resolver en cada periodo de muestreo resulta ser un QP (Quadratic Programming) parametrizado en el estado actual y el estado asociado a la referencia (estado objetivo), lo que nos permite obtener la ley de control de forma explícita y por lo tanto ser capaces de aplicar este controlador a sistemas rápidos como el motor lineal, con un tiempo de muestreo de 10ms