Multiobjective analysis for the design and control of an electromagnetic valve actuator

The electromagnetic valve actuator can deliver much improved fuel efficiency and reduced emissions in spark ignition (SI) engines owing to the potential for variable valve timing when compared with cam-operated, or conventional, variable valve strategies. The possibility exists to reduce pumping losses by throttle-free operation, along with closed-valve engine braking. However, further development is required to make the technology suitable for accept- ance into the mass production market. This paper investigates the application of multiobjective optimization techniques to the conflicting objective functions inherent in the operation of such a device. The techniques are utilized to derive the optimal force–displacement characteristic for the solenoid actuator, along with its controllability and dynamic/steady state performance

Multi-objective optimization of a wing fence on an unmanned aerial vehicle using surrogate-derived gradients

In this paper, the multi-objective, multifidelity optimization of a wing fence on an unmanned aerial vehicle (UAV) near stall is presented. The UAV under consideration is characterized by a blended wing body (BWB), which increases its efficiency, and a tailless design, which leads to a swept wing to ensure longitudinal static stability. The consequence is a possible appearance of a nose-up moment, loss of lift initiating at the tips, and reduced controllability during landing, commonly referred to as tip stall. A possible solution to counter this phenomenon is wing fences: planes placed on top of the wing aligned with the flow and developed from the idea of stopping the transverse component of the boundary layer flow. These are optimized to obtain the design that would fence off the appearance of a pitch-up moment at high angles of attack, without a significant loss of lift and controllability. This brings forth a constrained multi-objective optimization problem. The evaluations are performed through unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations. However, since controllability cannot be directly assessed through computational fluid dynamics (CFD), surrogate-derived gradients are used. An efficient global optimization framework is developed employing surrogate modeling, namely regressive co-Kriging, updated using a multi-objective formulation of the expected improvement. The result is a wing fence design that extends the flight envelope of the aircraft, obtained with a feasible computational budget

Improved branch and bound method for control structure screening

The main aim of this paper is to present an improved algorithm of “Branch and Bound” method for control structure screening. The new algorithm uses a best- first search approach, which is more efficient than other algorithms based on depth-first search approaches. Detailed explanation of the algorithms is provided in this paper along with a case study on Tennessee–Eastman process to justify the theory of branch and bound method. The case study uses the Hankel singular value to screen control structure for stabilization. The branch and bound method provides a global ranking to all possible input and output combinations. Based on this ranking an efficient control structure with least complexity for stabilizing control is detected which leads to a decentralized proportional cont

Mathematical control of complex systems

Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Integrated design and control of chemical processes : Part II: an illustrative example

[EN] In this paper, the integrated design paradigm is illustrated with several examples taken from the wide range of methodologies developed in last decades and presented in the first article of this series [Part 1]. The techniques included here belong to the category of simultaneous design and control in an optimization framework, and they have been developed by the authors’ research group and applied to the simultaneous process and control system design of the activated sludge process in a wastewater treatment plant (WWTP). In the present article, new aspects and results of those methodologies are presented for further understanding. The scope of the problem considers both a fixed plant layout and the plant structure selection by defining a simple superstructure. The control strategy chosen is a linear Model Predictive Controller (MPC) with terminal penalty in order to guarantee stability. As for the evaluation of the controllability, norm based indexes have been considered, and a multi-model approach to represent the uncertainty and assure robustness. The formulation of the optimization problem can be stated either as a multiobjective one considering costs and controllability, or as monoobjective adding some controllability constraints. Several strategies for solving the optimization problem are presented, mixing stochastic and deterministic methods, and genetic algorithms.[ES] En este artículo, el paradigma de diseño integrado se ilustra con varios ejemplos tomados de la amplia gama de metodologías desarrolladas en las últimas décadas y presentadas en el primer artículo de esta serie. Las técnicas utilizadas pertenecen a la categoría de diseño y control simultáneo en un marco de optimización siendo desarrolladas por el grupo de investigación de los autores y aplicadas al diseño simultáneo de procesos y sistemas de control del proceso de lodos activados en una planta de tratamiento de aguas residuales. El alcance del problema considera tanto una disposición fija de la planta como la selección de la estructura de la planta definiendo una superestructura simple. La estrategia de control elegida es un controlador predictivo modelo lineal (MPC). En cuanto a la evaluación de la controlabilidad, se han considerado índices basados en normas, y un enfoque multi-modelo para representar la incertidumbre y asegurar robustez. La formulación del problema de optimización se puede plantear bien como un objetivo multiobjetivo que considera costos y controlabilidad, o como monoobjetivo que añade algunas restricciones de controlabilidad. Se presentan varias estrategias para resolver el problema de optimización, mezclando métodos estocásticos y determinísticos, y algoritmos genéticos

Integrated design and control of chemical processes : part I : revision and clasification

[EN] This work presents a comprehensive classification of the different methods and procedures for integrated synthesis, design and control of chemical processes, based on a wide revision of recent literature. This classification fundamentally differentiates between “projecting methods”, where controllability is monitored during the process design to predict the trade-offs between design and control, and the “integrated-optimization methods” which solve the process design and the control-systems design at once within an optimization framework. The latter are revised categorizing them according to the methods to evaluate controllability and other related properties, the scope of the design problem, the treatment of uncertainties and perturbations, and finally, the type the optimization problem formulation and the methods for its resolution.[ES] Este trabajo presenta una clasificación integral de los diferentes métodos y procedimientos para la síntesis integrada, diseño y control de procesos químicos. Esta clasificación distingue fundamentalmente entre los "métodos de proyección", donde se controla la controlabilidad durante el diseño del proceso para predecir los compromisos entre diseño y control, y los "métodos de optimización integrada" que resuelven el diseño del proceso y el diseño de los sistemas de control a la vez dentro de un marco de optimización. Estos últimos se revisan clasificándolos según los métodos para evaluar la controlabilidad y otras propiedades relacionadas, el alcance del problema de diseño, el tratamiento de las incertidumbres y las perturbaciones y, finalmente, el tipo de la formulación del problema de optimización y los métodos para su resolución

Robust integrated design of processes with terminal penalty model predictive controllers

[EN] In this work, a novel methodology for the Integrated Design (ID) of processes with linear Model Predictive Control (MPC) is addressed, providing simultaneously the plant dimensions, the control system parameters and a steady state working point. The MPC chosen operates over infinite horizon in order to guarantee stability and it is implemented with a terminal penalty. The ID methodology considers norm based indexes for controllability, as well as robust performance conditions by using a multi-model approach. Mathematically, the ID is stated as a multiobjective nonlinear constrained optimization problem, tackled in different ways. Particularly, objective functions include investment, operating costs, and dynamical indexes based on the weighted sum of some norms of different closed loop transfer functions of the system. The paper illustrates the application of the proposed methodology with the ID of the activated sludge process of a wastewater treatment plant (WWTP).[ES] Este trabajo aborda una nueva metodología para el Diseño Integrado (ID) de procesos con Control Predictivo Modelo (MPC) lineal, que proporciona simultáneamente las dimensiones de la planta, los parámetros del sistema de control y un punto de trabajo en estado estacionario. El MPC elegido opera sobre horizonte infinito para garantizar la estabilidad. La metodología de ID considera los índices basados en la norma para la controlabilidad, así como las robustas condiciones de rendimiento mediante el uso de un enfoque multi-modelo. Matemáticamente, la ID se declara como un problema de optimización no lineal multiobjetivo restringido, abordado de diferentes maneras. Particularmente, las funciones objetivas incluyen inversión, costos de operación e índices dinámicos basados en la suma ponderada de algunas normas de diferentes funciones de transferencia en bucle cerrado del sistema. El trabajo ilustra la aplicación de la metodología propuesta con el ID del proceso de lodos activados de una planta de tratamiento de aguas residuales (EDAR)

On controllability of neuronal networks with constraints on the average of control gains

Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently

An efficient method for multiobjective optimal control and optimal control subject to integral constraints

We introduce a new and efficient numerical method for multicriterion optimal control and single criterion optimal control under integral constraints. The approach is based on extending the state space to include information on a "budget" remaining to satisfy each constraint; the augmented Hamilton-Jacobi-Bellman PDE is then solved numerically. The efficiency of our approach hinges on the causality in that PDE, i.e., the monotonicity of characteristic curves in one of the newly added dimensions. A semi-Lagrangian "marching" method is used to approximate the discontinuous viscosity solution efficiently. We compare this to a recently introduced "weighted sum" based algorithm for the same problem. We illustrate our method using examples from flight path planning and robotic navigation in the presence of friendly and adversarial observers.Comment: The final version accepted by J. Comp. Math. : 41 pages, 14 figures. Since the previous version: typos fixed, formatting improved, one mistake in bibliography correcte

Comprehensive entropy weight observability-controllability risk analysis and its application to water resource decision-making

Decision making for water resource planning is often related to social, economic and environmental factors. There are various methods for making decisions about water resource planning alternatives and measures with various shortcomings. A comprehensive entropy weight observability-controllability risk analysis approach is presented in this study. Computing methods for entropy weight (EW) and subjective weight (SW) are put forward based on information entropy theory and experimental psychology principles, respectively. Comprehensive weight (CW) consisting of EW and SW is determined. The values of observability-controllability risk (Roc) and gain by comparison (Gbc) are obtained based on the CWs. The quantitative analysis of alternatives and measures is achieved based on Roc and Gbc. A case study on selection of water resource planning alternatives and measures in the Yellow River Basin, China, was performed. Results demonstrate that the approach presented in this study can achieve optimal decision-making results