J.V. Salcedo

Air management in a diesel engine using fuzzy control techniques

Air management for diesel engines is a major challenge from the control point of view because of the highly nonlinear behavior of this system. For this reason, linear control techniques are unable to provide the required performance, and nonlinear controllers are used instead. This article discusses two fundamental steps when designing a control system. Firstly, a methodology to identify Takagi-Sugeno (T-S) structures using experimental data is proposed. Secondly, the design of a fuzzy controller in PDC structure (Parallel Distributed Compensation) is presented. The parameters of this controller are obtained from a LMI (Linear Matrix Inequalities) minimization problem.

Robust constrained receding-horizon predictive control via bounded data uncertainties

The main objective of this work consists of obtaining a new robust and stable Model Predictive Control (MPC). One widely used technique for improving robustness in MPC consists of the Min-Max optimization, where an analogy can be established with the Bounded Data Uncertainties (BDU) method. The BDU is a regularization technique for least-squares problems by taking into account the uncertainty bounds. So BDU both improves robustness in MPC and offers a guided way of tuning the empirically tuned penalization parameter for the control effort in MPC due to the duality that the parameter coincides with the regularization one in BDU. On the other hand, the stability objective is achieved by the use of terminal constraints, in particular the Constrained Receding-Horizon Predictive Control (CRHPC) algorithm, so the original CRHPC-BDU controller is stated, which presents a better performance from the point of view of robustness and stability than a standard MPC.

Design of PDC fuzzy controllers under persistent disturbances and application in mechanical systems

This article proposes a method for designing parallel-distributed compensation fuzzy state-feedback controllers for nonlinear systems affected by persistent disturbances. Firstly, some methods proposed in the literature are analyzed and their weak points are discussed. A design scheme based on minimizing the 1-norm between the disturbance signal input and output is then proposed. Specifically, an upper bound on this norm, the *-norm, is minimized, which, unlike the 1-norm, can be formulated in terms of linear and bilinear matrix inequalities. Finally, this method is applied to a nonlinear mechanical system.

Predictive LPV control of a liquid-gas separation process

The problem of controlling a liquid-gas separation process is approached by using LPV control techniques. An LPV model is derived from a nonlinear model of the process using differential inclusion techniques. Once an LPV model is available, an LPV controller can be synthesized. The authors present a predictive LPV controller based on the GPC controller [Clarke D, Mohtadi C, Tuffs P. Generalized predictive control - Part I. Automatica 1987;23(2):137-48; Clarke D, Mohtadi C, Tuffs P. Generalized predictive control - Part II. Extensions and interpretations. Automatica 1987;23(2):149-60]. The resulting controller is denoted as GPC-LPV. This one shows the same structure as a general LPV controller [El Gahoui L, Scorletti G. Control of rational systems using linear-fractional representations and linear matrix inequalities. Automatica 1996;32(9):1273-84; Scorletti G, El Ghaoui L. Improved LMI conditions for gain scheduling and related control problems. International Journal of Robust Nonlinear Control 1998;8:845-77; Apkarian P, Tuan HD. Parametrized LMIs in control theory. In: Proceedings of the 37th IEEE conference on decision and control; 1998. p. 152-7; Scherer CW. LPV control and full block multipliers. Automatica 2001;37:361-75], which presents a linear fractional dependence on the process signal measurements. Therefore, this controller has the ability of modifying its dynamics depending on measurements leading to a possibly nonlinear controller. That controller is designed in two steps. First, for a given steady state point is obtained a linear GPC using a linear local model of the nonlinear system around that operating point. And second, using bilinear and linear matrix inequalities (BMIs/LMIs) the remaining matrices of GPC-LPV are selected in order to achieve some closed loop properties: stability in some operation zone, norm bounding of some input/output channels, maximum settling time, maximum overshoot, etc., given some LPV model for the nonlinear system. As an application, a GPC-LPV is designed for the derived LPV model of the liquid-gas separation process. This methodology can be applied to any nonlinear system which can be embedded in an LPV system using differential inclusion techniques.

LQR Robusto Mediante Incertidumbre Acotada en los Datos

Resumen En este trabajo se presenta el sintonizado del Regulador Lineal Cuadratico (LQR) mediante la tecnica de incertidumbre acotada en los datos o Bounded Data Uncertainties (BDU) con el fin de mejorar la robustez del sistema, planteandose como un Min-Max donde se busca la mejor solucion en el peor escenario posible. Asi se ofrece un nuevo metodo guiado de ajuste del LQR, considerando los limites de la incertidumbre. La aplicacion a sistemas multidimensionales no es trivial, pues presenta la forma de un Two-Point Boundary Value Problem (TPBVP), el cual se resuelve iterativamente.

Sistema de Control Borroso para el Proceso de Renovación de la Carga en Motores Turbodiesel

Modelling and control for air management in diesel engines is a major challenge from the control point of view, because of the high nonlinear behaviour of this system. For this reason, classic control techniques are unable to provide the required performance, and nonlinear controllers are used instead. This article discusses two fundamental steps when designing a control system. Firstly, a methodology to identify a nonlinear system with a fuzzy model in a Takagi-Sugeno (T-S) structure using experimental data is proposed. Secondly, the design of a fuzzy controller in PDC structure (Parallel Distributed Compensation*) is presented. The parameters of this controller are obtained from a minimization problem that is subject to LMIs (Linear Matrix Inequalities**).

Practice tool based on open source SCADA for experimentation in nonlinear control using the inverted pendulum

This paper presents the potential of open source software for designing educational tools in the automatization field. In particular, this paper presents a complete tool that students can use for studying and testing nonlinear control algorithms. The system has three different parts that students can evaluate and modify. Firstly, a virtual model represents the physical model and, in this case, an inverted pendulum is used. Secondly, the controller is implemented by a real‐time distributed control system. Finally, the system can be managed with a JAVA application. Therefore, students have all the necessary elements to practice using nonlinear and complex systems. The main tools applied in the design are open source software and the developed platform is Generalized Public License(GPL).

A PLS approach to identifying predictive ARX models

MPC (Model Predictive Control) based on linear models is an extensively used methodology in the industrial field as a control solution for MIMO processes. The identification of ARX models for multivariable systems from input-output data often requires the use of LVMs (Latent Variable Methods) such as PCR (Principal Components Regression) or PLS (Partial Least Squares) due to the so called “curse of dimensionality”. LVMs however, do not take into consideration the prediction horizon in which the model will be used in MPC. PLS-PH (Partial Least Squares Prediction Horizon) is presented in this paper as a modification to PLS aiming to provide a model which performs better within a given prediction horizon. The advantage of using PLS-PH is shown in a simulation example.

Discrete Forward-Backward Fuzzy Predictive Control

An extension of the model predictive control philosophy to the field of fuzzy control design is discussed. The main goal is to bring together the best features from both techniques. The basic idea is to divide the initial optimization problem in a set of recursive optimization subproblems or decision stages. Each subproblem is raised as a fuzzy LQR design where the goal is to define the set of feedback gains of a fuzzy Parallel Distributed Compensator (PDC) that minimizes the function cost using Linear Matrix Inequalities (LMIs). Therefore, the global controller is a set of PDC controllers that satisfies the Bellman optimality principle, minimizing the cost function both locally and globally, and guarantees stability and satisfies the control action constraints.

Non-linear Robust Identification: Application to a Thermal Process

In this article, a methodology to obtain the Feasible Parameter Set (FPS) and a nominal model in a non-linear robust identification problem is presented. Several norms are taken into account simultaneously to define the FPSwhich improves the model quality but, as counterpart, it increases the optimization problem complexity. To determine the FPSa multimodal optimization problem with an infinite number of minima, which constitute the FPS, is presented and a special evolutionary algorithm (??GA) is used to characterize it. Finally, an application to a thermal process identification, where ||·|| ? and ||·|| 1 norms have been considered simultaneously, is presented to illustrate the technique.