Proportional-integral-plus (PIP) control of time delay systems

The paper shows that the digital proportional-integral-plus (PIP) controller formulated within the context of non-minimum state space (NMSS) control system design methodology is directly equivalent, under certain non-restrictive pole assignment conditions, to the equivalent digital Smith predictor (SP) control system for time delay systems. This allows SP controllers to be considered within the context of NMSS state variable feedback control, so that optimal design methods can be exploited to enhance the performance of the SP controller. Alternatively, since the PIP design strategy provides a more flexible approach, which subsumes the SP controller as one option, it provides a superior basis for general control system design. The paper also discusses the robustness and disturbance response characteristics of the two PIP control structures that emerge from the analysis and demonstrates the efficacy of the design methods through simulation examples and the design of a climate control system for a large horticultural glasshouse system

Integration of process design and control: A review

There is a large variety of methods in literature for process design and control, which can be classified into two main categories. The methods in the first category have a sequential approach in which, the control system is designed, only after the details of process design are decided. However, when process design is fixed, there is little room left for improving the control performance. Recognizing the interactions between process design and control, the methods in the second category integrate some control aspects into process design. With the aim of providing an exploration map and identifying the potential areas of further contributions, this paper presents a thematic review of the methods for integration of process design and control. The evolution paths of these methods are described and the advantages and disadvantages of each method are explained. The paper concludes with suggestions for future research activities

Data Driven Approaches to Model Building: Applications to Energy Industries

George Box’s famous quote “All models are wrong, but some are useful” is now widely known. Mathematical models can be built based on a combination of first principles and available data. The focus of this work is on the application of data-driven modelling approaches in two specific instances of problems in upstream (oil & gas extraction) and downstream (refining & chemicals) industries, namely (a) cementing of wells drilled for production of oil and gas from unconventional resources, such as shales; and (b) design of robust control-relevant models for oil refineries and chemical plants. Shale gas production from horizontal wells faces potential problems related to gas leakage from the cemented annulus of the well into the air and water reserves, with obvious environmental and productivity implications. Whether a well will leak or not depends on several factors, related to cement composition and preparation, the cementing process, well conditions, and others. A model would be useful in assessing ahead of time whether a cementing job will produce a non-leaking well or not. Such a model could be based on first principles, but would be extremely complicated. Alternatively, as done in this work, a model can be built using multivariate statistics and available data from several leaking and non-leaking wells, cemented under different enough scenarios. The model built has 35 input variables (in the broad categories of casing properties, cement and drilling mud properties, and operating conditions) and manages to correctly classify with confidence 81% of wells as leaking or non-leaking in cross-validation tests. An advanced control system relies on a good control-relevant model that is not merely a good approximation of the actual process under control but also satisfies additional properties necessary for controller design. Control-relevant models are typically identified through industrial experiments whose design is considerably more involved than standard design for parameter estimation. The focus of this study is how to design control-relevant identification experiments when elements of the model are already known. A new theoretical framework is developed and its significant advantages over standard methods are illustrated through numerical simulations. Several possibilities for future development are suggested.Chemical and Biomolecular Engineering, Department o

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

Mixed H2/H∞ robust controllers in aircraft control problem

A leading cause of accidents during the landing phase of a flight lies in a considerable altitude loss by an aircraft as a result of the impact of a microburst of wind. One of the significant factors focuses primarily on the need to simultaneously satisfy various requirements regarding conditions of environmental disturbances and a wide range of systemic changes. The paper presents an algorithm for synthesizing an optimal controller that solves the mixed H2/H∞ control problem for the stabilization of aircraft in glide-path landing mode in the presence of uncertainty. Firstly, the principles of multi-criteria optimization are presented, and the mixed H2/H∞ problem is interpreted as the synthesis of a system with optimal quadratic performance, subject to its readiness to operate with the worst disturbance. Then, the ensuing section expounds upon the mathematical depiction of the vertical trajectory of aircraft, duly considering the perturbations imposed by wind phenomena. Subsequently, the effectiveness of mixed H2/H∞ control is confirmed compared to autonomous H2 or H∞ regulators through simulation outcomes acquired from the created system. Optimization based on a hybrid (mixed) criterion allowed combining the strengths of locally optimal systems based only on H2 or H∞ theory

Dynamics of aerospace vehicles

The focus of this research was to address the modeling, including model reduction, of flexible aerospace vehicles, with special emphasis on models used in dynamic analysis and/or guidance and control system design. In the modeling, it is critical that the key aspects of the system being modeled be captured in the model. In this work, therefore, aspects of the vehicle dynamics critical to control design were important. In this regard, fundamental contributions were made in the areas of stability robustness analysis techniques, model reduction techniques, and literal approximations for key dynamic characteristics of flexible vehicles. All these areas are related. In the development of a model, approximations are always involved, so control systems designed using these models must be robust against uncertainties in these models

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)

Dynamic operability assessment : a mathematical programming approach based on Q-parametrization

Bibliography: pages 197-208.The ability of a process plant to guarantee high product quality, in terms of low variability, is emerging as a defining feature when distinguishing between alternative suppliers. The extent to which this can be achieved is termed a plant's dynamic operability and is a function of both the plant design and the control system design. In the limit, however, the closedloop performance is determined by the properties inherent in the plant. This realization of the interrelationship between a plant design and its achievable closed-loop performance has motivated research toward systematic techniques for screening inherently inferior designs. Pioneering research in the early 1980's identified right-half-plane transmission zeros, time delays, input constraints and model uncertainty as factors that limit the achievable closedloop performance of a process. Quantifying the performance-limiting effect of combinations of these factors has proven to be a challenging problem, as reflected in the literature. It is the aim of this thesis to develop a systematic procedure for dynamic operability assessment in the presence of combinations of performance-limiting factors. The approach adopted in this thesis is based on the Q-parametrization of stabilizing linear feedback controllers and involves posing dynamic operability assessment as a mathematical programming problet? In the proposed formulation, a convex objective function, reflecting a measure of closed-loop performance, is optimized over all stable Q, subject. to a set of constraints on the closed-loop behavior, which for many specifications of interest is convex. A discrete-time formulation is chosen so as to allow for the convenient hand.ling of time delays and time-domain constraints. An important feature of the approach is that, due to the convexity, global optimality is guaranteed. Furthermore, the fact that Q parametrizes all stabilizing linear feedback controllers implies that the performance at the optimum represents the best possible performance for any such controller. The results are thus not biased by controller type or tuning, apart from the requirement that the controller be linear