An Efficient Maximization Algorithm With Implications in Min-Max Predictive Control

n this technical note, an algorithm for binary quadratic programs defined by matrices with band structure is proposed. It was shown in the article by T. Alamo, D. M. de la Pentildea, D. Limon, and E. F. Camacho, ldquoConstrained min-max predictive control: modifications of the objective function leading to polynomial complexity,rdquo IEEE Tran. Autom. Control , vol. 50, pp. 710-714, May 2005, that this class of problems arise in robust model predictive control when min-max techniques are applied. Although binary quadratic problems belongs to a class of NP-complete problems, the computational burden of the proposed maximization algorithm for band matrices is polynomial with the dimension of the optimization variable and exponential with the band size. Computational results and comparisons on several hundred test problems demonstrate the efficiency of the algorithm

Min-max model predictive control as a quadratic program

This paper deals with the implementation of min-max model predictive control for constrained linear systems with bounded additive uncertainties and quadratic cost functions. This type of controller has been shown to be a continuous piecewise affine function of the state vector by geometrical methods. However, no algorithm for computing the explicit solution has been given. In this paper, we show that the min-max optimization problem can be expressed as a multi-parametric quadratic program, and so, the explicit form of the controller may be determined by standard multi-parametric techniques

Min–max MPC using a tractable QP problem

Min–max model predictive controllers (MMMPC) suffer from a great computational burden that is often circumvented by using approximate solutions or upper bounds of the worst possible case of a performance index. This paper proposes a computationally efficient MMMPC control strategy in which a close approximation of the solution of the min–max problem is computed using a quadratic programming problem. The overall computational burden is much lower than that of the min–max problem and the resulting control is shown to have a guaranteed stability. A simulation example is given in the paper

Min-Max MPC based on a computationally efficient upper bound of the worst case cost

Min-Max MPC (MMMPC) controllers [P.J. Campo, M. Morari, Robust model predictive control, in: Proc. American Control Conference, June 10–12, 1987, pp. 1021–1026] suffer from a great computational burden which limits their applicability in the industry. Sometimes upper bounds of the worst possible case of a performance index have been used to reduce the computational burden. This paper proposes a computationally efficient MMMPC control strategy in which the worst case cost is approximated by an upper bound based on a diagonalization scheme. The upper bound can be computed with O(n3) operations and using only simple matrix operations. This implies that the algorithm can be coded easily even in non-mathematical oriented programming languages such as those found in industrial embedded control hardware. A simulation example is given in the paper

Oracle-based economic predictive control

Article number 107434This paper presents an economic model predictive controller, under the assumption that the only mea- surable signal of the plant is the economic cost to be minimized. In order to forecast the evolution of this economic cost for a given input trajectory, a prediction model with a NARX structure, the so-called oracle, is proposed. Sufficient conditions to ensure the existence of such oracle are studied, proving that it can be derived for a general nonlinear system if the economic cost function is a Morse function. Based on this oracle, economic model predictive controllers are proposed, and their stability is demonstrated in nominal conditions under a standard dissipativity assumption. The viability of these controllers in practi- cal settings (where the oracle may provide imperfect predictions for generic inputs) is proven by means of input-to-state stability. These properties have been illustrated in a case study based on a continuously stirred tank reactorMinisterio de Economía y Competitividad (MINECO). España DPI2016-76493-C3-1-RAgencia Estatal de Investigación (AEI) PID2019-106212RB-C41/AEI/10.13039/50110001103

Model predictive control techniques for hybrid systems

This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduación y Ciencia DPI2007-66718-C04-01Ministerio de Eduación y Ciencia DPI2008-0581

Sistema de Evaluación Automática Vía Web en Asignaturas Prácticas de Ingeniería

En este trabajo se presenta una nueva herramienta web para educación que permite automatizar la recogida y evaluación de ejercicios prácticos de diferentes disciplinas de ingeniería con la complejidad típica que la formación técnica requiere. Una de las principales características de la herramienta propuesta es la posibilidad de personalizar los ejercicios para cada alumno. El sistema realiza una comparación funcional de las soluciones propuestas por los alumnos con las soluciones correctas proporcionadas por el profesorado y en función de los resultados obtenidos asigna una calificación al alumno de forma automática. La plataforma permite al profesor implementar técnicas innovadoras de docencia que fomentan el autoaprendizaje en cursos numerosos. Se presentan los resultados de uso del sistema de evaluación en dos cursos, uno de Teoría de Sistemas y otro de Fundamentos de Informática.Universidad de Sevilla I Plan Propio de Docenci

An Application of Cooperative Game Theory to Distributed Control

18th World CongressThe International Federation of Automatic ControlMilano (Italy) August 28 - September 2, 2011In this paper we propose to study the underlying properties of a given distributed control scheme in which a set of agents switch between different communication strategies that define which network links are used in order to regulate to the origin a set of unconstrained linear systems. The problems of how to decide the time-varying communication strategy, share the benefits/costs and detect which are the most critical links in the network are solved using tools from game theory. The proposed scheme is demonstrated through a simulation example

Offset free data driven control: application to a process control trainer

This work presents a data driven control strategy able to track a set point without steady-state error. The control sequence is computed as an affine combination of past control signals, which belong to a set of trajectories stored in a process historian database. This affine combination is computed so that the variance of the tracking error is minimised. It is shown that offset free control, that is zero mean tracking error, is achieved under the assumption that the state is measurable, the underlying dynamics are linear and the trajectories of the database share the same error dynamics and are in turn offset free. The proposed strategy learns the underlying controller stored in the database while maintaining its offset free tracking capability in spite of differences in the reference, disturbances and operating conditions. No training phase is required and newly obtained process data can be easily taken into account. The proposed strategy, related to direct weight optimisation learning techniques, is tested on a process control trainer.MINECO-Spain and FEDER Funds project DPI2016-76493-C3-1-RUniversity of Seville(Spain) grant 2014/42

Economic model predictive control based on a periodicity constraint

This paper addresses a novel economic model predictive control (MPC) formulation based on a periodicity constraint to achieve an optimal periodic operation for discrete-time linear systems. The proposed control strategy does not rely on forcing the terminal state by means of a terminal equality constraint and hence it does not require a priori knowledge of a periodic steady trajectory. Instead, at each sampling time step the economic cost function is optimized based on a periodicity constraint over all the periodic trajectories that include the current state. The recursive feasibility and the closed-loop convergence to a periodic steady trajectory are discussed. Moreover, an optimality certificate of this steady trajectory is provided based on the Karush–Kuhn–Tucker (KKT) optimality conditions. Finally, an application to a well-known water distribution network benchmark is presented to demonstrate the proposed economic MPC in which the closed-loop simulation results obtained with a linear model and a virtual–reality simulator are both providedUnión Europea, European Development Research Fund (EDRF) DEOCS (DPI2016-76493) and SCAV (DPI2017-88403-R),Generalitat de Catalunya 2017-SGR-482FPI Grant BES-2014-06831