Sergio García-Nieto

Controller Tuning by Means of Multi-Objective Optimization Algorithms: A Global Tuning Framework

A holistic multi-objective optimization design technique for controller tuning is presented. This approach gives control engineers greater flexibility to select a controller that matches their specifications. Furthermore, for a given controller it is simple to analyze the tradeoff achieved between conflicting objectives. By using the multi-objective design technique it is also possible to perform a global comparison between different control strategies in a simple and robust way. This approach thereby enables an analysis to be made of whether a preference for a certain control technique is justified. This proposal is evaluated and validated in a nonlinear multiple-input multiple-output system using two control strategies: a classical proportional-integral-derivative control scheme and a feedback state controller.

Applied Pareto multi-objective optimization by stochastic solvers

It is well known that many engineering design problems with different objectives, some of which can be opposed to one another, can be formulated as multi-objective functions and resolved with the construction of a Pareto front that helps to select the desired solution. Obtaining a correct Pareto front is not a trivial question, because it depends on the complexity of the objective functions to be optimized, the constraints to keep within and, in particular, the optimizer type selected to carry out the calculations. This paper presents new methods for Pareto front construction based on stochastic search algorithms (genetic algorithms, GAs and multi-objective genetic algorithms, MOGAs) that enable a very good determination of the Pareto front and fulfill some interesting specifications. The advantages of these applied methods will be proven by the optimization of well-known benchmarks for metallic supported I-beam and gearbox design.

Genetic algorithms optimization for normalized normal constraint method under Pareto construction

This paper presents the resolution of multiobjective optimization problems as a tool in engineering design. In the literature, the solutions of this problems are based on the Pareto frontier construction. Therefore, substantial efforts have been made in recent years to develop methods for the construction of Pareto frontiers that guarantee uniform distribution and exclude the non-Pareto and local Pareto points. The normalized normal constraint is a recent contribution that generates a well-distributed Pareto frontier. Nevertheless, these methods are susceptible of improvement or modifications to obtain the same level of results more efficiently. This paper proposes a modification of the original normalized normal constraint method using a genetic algorithms in the optimization task. The results presented in this paper show a suitable behavior for the genetic algorithms method compared to classical Gauss-Newton optimization methods which are used by the original normalized normal constraint method.

Physical programming for preference driven evolutionary multi-objective optimization

Graphical abstractDisplay Omitted HighlightsWe deal with preference driven evolutionary multi-objective optimization statements.Our approach uses physical programming to include preferences in the optimization.Preferences and constraints are included in a meaningful way for the designer.The implemented algorithm shows its usefulness to compute a pertinent Pareto front. Preference articulation in multi-objective optimization could be used to improve the pertinency of solutions in an approximated Pareto front. That is, computing the most interesting solutions from the designers point of view in order to facilitate the Pareto front analysis and the selection of a design alternative. This articulation can be achieved in an a priori, progressive, or a posteriori manner. If it is used within an a priori frame, it could focus the optimization process toward the most promising areas of the Pareto front, saving computational resources and assuring a useful Pareto front approximation for the designer. In this work, a physical programming approach embedded in an evolutionary multi-objective optimization is presented as a tool for preference inclusion. The results presented and the algorithm developed validate the proposal as a potential tool for engineering design by means of evolutionary multi-objective optimization.

Hole distribution in phononic crystals: Design and optimization

An exhaustive study has been made into the potential improvement in attenuation and focusing of phononic crystal arrays resulting from the deliberate creation of vacancies. Use is made of a stochastic search algorithm based on evolutionary algorithms called the epsilon variable multi-objective genetic algorithm which, in conjunction with the application of multiple scattering theory, enables the design of devices for effectively controlling sound waves. Several parameters are analyzed, including the symmetries used in the distribution of holes and the optimum number of holes. The validity and utility of the general rules obtained have been confirmed experimentally.

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.

Enhancing controller’s tuning reliability with multi-objective optimisation: From Model in the loop to Hardware in the loop

Abstract In general, the starting point for the complex task of designing a robust and efficient control system is the use of nominal models that allow to establish a first set of parameters for the selected control scheme. Once the initial stage of design is achieved, control engineers face the difficult task of Fine-Tuning for a more realistic environment, where the environment conditions are as similar as possible to the real system. For this reason, in the last decades the use of Hardware-in-The-Loop (HiL) systems has been introduced. This simulation technique guarantees realistic simulation environments to test the designs but without danger of damaging the equipment. Also, in this iterative process of Fine-Tuning, it is usual to use different (generally conflicting/opposed) criteria that take into account the sensitivities that always appear in every project, such as economic, security, robustness, performance, for example. In this framework, the use of multi-objective techniques are especially useful since they allow to study the different design alternatives based on the multiple existing criteria. Unfortunately, the combination of multi-objective techniques and verification schemes based on Hardware-In-The-Loop presents a high incompatibility. Since obtaining the optimal set of solutions requires a high computational cost that is greatly increased when using Hardware- In-the-Loop. For this reason, it is often necessary to use less realistic but more computationally efficient verification schemes such as Model in the Loop (MiL), Software in the Loop (SiL) and Processor in the Loop (PiL). In this paper, a combined methodology is presented, where multi-objective optimisation and multi-criteria decision making steps are sequentially performed to achieve a final control solution. The authors claim that while going towards the optimisation sequence over MiL → SiL → PiL → HiL platforms, the complexity of the problem is unveiled to the designer, allowing to state meaningful design objectives. In addition, safety in the step between simulation and reality is significantly increased.

Unmanned Aerial Vehicles Model Identification using Multi-Objective Optimization Techniques

Abstract The total amount of UAVs civil applications is getting bigger and bigger. The cost and the risks of the development phase of this systems has to be decreased in order to make them affordable. It is required to minimize the number of hours of real flight, making use of simulation tools and taking full advantage of the acquired data. Thus, obtaining a dynamic model that tightly adjusts to the real flight behaviour of the aircraft gains in importance, in the way that it will lead to precise simulation results and, therefore, to correctly designed control algorithms. A model identification technique based on experimental data and Multi-Objective optimization evolution algorithm, is presented here. This methodology makes profit of the possibility given by this type of algorithm of facing different objetives at the same time, to take full advantage of the experimental data and to get better adjusted models.

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**).

Adaptive microwave system for optimum new material sintering

Microwave sintering has emerged in recent years as a non-conventional method for sintering materials with significant advantages against conventional procedures. Then, the design of microwave systems, including adaptive parts and control devices, becomes a very important topic to be performed. The present investigation describes the microwave cavity design and control and monitoring system for sintering alumina-zirconia nanocomposite. The samples were sintered in a multimode microwave furnace (2.45 GHz) at different temperatures (1200-1400 °C) in air. By microwave sintering are achieved higher density (~99%), hardness (18 GPa) and fracture toughness (6.3 MPa·m1/2) properties and homogeneous microstructure compared to conventional heating.