Handbook of model predictive control

Handbook of Model Predictive Control / by Saša V. Raković and William S. Levine (Editors) This handbook contains 27 chapters that are organized into three parts. Part 1 is on theory and comprises 12 chapters, ranging from basic MPC theory to advanced studies and model predictive control (MPC) formulations. Part 2, on computation, includes eight chapters and covers numerical implementation of MPC-related optimization algorithms. Part 3 discusses applications of MPC in numerous fields, such as automotive, power and energy systems, health care, and finance. The book is designed for a wide audience. It is an excellent reference for graduate students, researchers, and practitioners in the field of control systems and numerical optimization who want to understand the potential, challenges, and benefits of MPC and its applications. Alternately, it is an up-to-date reference for MPC research experts (both in academia and industry). For this audience, the book helps experts address new MPC-related problems and research directions. The book provides a thorough and comprehensive reference of the underlying theory, implementation, and applications of MPC. The content of the book, contributed by various experts in the field, is well written and suitably organized into three parts. Furthermore, this book does an excellent job meeting several competing goals: clarity of communication to a diversified audience, formal rigor, and a self-contained presentation of the topics in each chapter. This handbook enables the reader to gain a panoramic viewpoint of MPC theory and practice as well as provides a state-of-the art overview of new and exciting areas of application at the forefront of MPC research

A Multivariable Approach for Control System Optimization of IGCC with CCS in DECAR Bit Project

Abstract IGCCs with CCS differ from existing IGCCs mainly because of steam integration between gasification process and combined cycle, and because of selective capture of CO2. A dynamic simulator of IGCCs with CCS considered in DECARBit project was developed by using a in house code, ALTERLEGO, and a commercial code ASPEN HYSYS ® . Simulators were used to assess flexibility of the process design and effectiveness of the control system during load changes. Starting from steady state results at nominal load, the simulator development has been implemented to assure a stable transient behavior during load reduction. As a result of this study, the flue-gas temperature and IP pressure should be regulated at fixed setpoint. Moreover, critical behavior of CO shift temperature controllers,can be mitigated by means of suitable setpoint coordination

Offset-free control of constrained linear discrete-time systems subject to persistent unmeasured disturbances

This paper addresses the design of a dynamic, nonlinear, time-invariant, state feedback controller that guarantees constraint satisfaction and offset-free control in the presence of unmeasured, persistent, non-stationary, additive disturbances. First, this objective is obtained by designing a dynamic, linear, time-invariant, offset-free controller, and an appropriate domain of attraction for this linear controller is denned. Following this, the linear (unconstrained) control input is modified by adding a perturbation term that is computed by a robust receding horizon controller. It is shown that the domain of attraction of the receding horizon controller contains that of the linear controller, and an efficient implementation of the receding horizon controller is proposed.Published versio

The beginning of the Neolithic era in Central Italy

This paper presents the general profile of the first farming communities of Central Italy in the Early Neolithic Era. Data shows the spread of early Neolithic cultures in the Italian peninsula at the beginning of the VI millennium B.C. The first Neolithic groups appeared in the southern regions of the peninsula and moved northwards following two trajectories along the Tyrrhenian and Adriatic coasts. The process of Neolithisation was initiated by peoples who probably came from different areas and traditions creating, over time, two distinct areas within the Italian peninsula, each with its own specific cultural features. Finally the article looks at how intensive exchanges both of complex knowledge and raw materials occurred between these two distinct cultural worlds

Data-driven nonlinear MPC using dynamic response surface methodology

For many complex processes, it is desirable to use a nonlinear model in the MPC design, and the recently proposed Dynamic Response Surface Methodology (DRSM) is capable of accurately modeling nonlinear continuous processes over semi-infinite time horizons. We exploit the DRSM to identify nonlinear data-driven dynamic models that are used in an NMPC. We demonstrate the ability and effectiveness of the DRSM data-driven model to be used as the prediction model for a nonlinear MPC regulator. This DRSM model is efficiently used to solve a non-equally-spaced finite-horizon optimal control problem so that the number of decision variables is reduced. The proposed DRSM-based NMPC is tested on a representative nonlinear process, an isothermal CSTR in which a second-order irreversible reaction is taking place. It is shown that the obtained quadratic data-driven model accurately represents the open-loop process dynamics and that DRSM-based NMPC is an effective data-driven implementation of nonlinear MPC

Observer-based offset-free internal model control

A linear feedback control structure is proposed that allows internal model control design principles to be applied to unstable and marginally stable plants. The control structure comprises an observer using an augmented plant model, state estimate feedback and disturbance estimate feedback. Conditions are given for both nominal internal stability and offset-free action even in the case of plant-model mismatch. The Youla parameterization is recovered as a limiting case with reduced order observers. The simple design methodology is illustrated for a marginally stable plant with delay

Robustness evaluation of different controllers in the presence of flow rate variations

This paper proposes a direct approach to evaluate achievable robustness to flow rate variations of different controllers (PID and advanced algorithms), for typical process dynamics (First Order Plus Time Delay with different time delay/lag ratio), able to represent very common heat exchange equipment. Starting from comparable nominal performance, the effect of flow rate variations on process parameters and consequently on achievable performance is analyzed in simulation up to the onset of marginal stability conditions. Flow rate variations act as “structured” uncertainty on parameters and the proposed procedure is able to indicate maximum allowable variations in a more realistic and efficient way, avoiding the conservatism implicit in most of analytical design techniques available in the literature. The proposed technique evaluates in a straightforward way the Maximum Allowed Changes (MAC) in flow rate. As inverse proportionality between flow rate and process parameters is present, also the efficacy of adopting equal percentage (EP) valves, which allow a local compensation of process gain nonlinearity, is investigated

Offset-free economic mpc based on modifier adaptation: Investigation of several gradient-estimation techniques

Various offset-free economic model predictive control schemes that include a disturbance model and the modifier-adaptation principle have been proposed in recent years. These schemes are able to reach plant optimality asymptotically even in the presence of plant–model mismatch. All schemes are affected by a major issue that is common to all modifier-adaptation formulations, namely, plant optimality (note that convergence per se does not require perfect plant gradients) requires perfect knowledge of static plant gradients, which is a piece of information not known in most practical applications. To address this issue, we present two gradient-estimation techniques, one based on Broyden’s update and the other one on linear regression. We apply these techniques for the estimation of either the plant gradients or the modifiers directly. The resulting economic MPC schemes are tested in a simulation and compared on two benchmark examples of different complexity with respect to both convergence speed and robustness to measurement noise

An automatic system for modeling and controlling color quality of dyed leathers in tanneries

This paper presents an automatic system for modeling and controlling color quality of dyed leathers of an Italian tannery. The proposed software is implemented within the IT company system, and is fully integrated with the machineries of the finishing line, that is, a spraying cabin with a robotic carousel, and an electronic tintometer system. Suitable experimental tests according to the Design of Experiments (DoE) are firstly defined, executed and analyzed for a series of color tones of interest. In order to derive and validate a set of colorimetric models able to evaluate and predict the color rendering of painted leathers, a set of recipes of basic dye pigments and data of light reflection measured by a multispectral camera are used. Principal Component Analysis is applied for dimensionality reduction, and linear least squares regression is employed to identify these data-driven models, which are then used for color control purpose. A color correction feedback strategy is indeed developed in order to converge towards the various target formulations. The control algorithm aims at reaching the multispectral reading values of the reference, that is, the first sample of unknown color recipe starting from the most similar archive base and appropriately updating the recipe of pigments, by using the measurement of leather samples prepared from time to time by the finishing line machineries. A set of company data are used to successfully validate the identified colorimetric models and the proposed color correction strategy

Data-driven Models for Advanced Control of Acid Gas Treatment in Waste-to-energy Plants

This paper presents a study of identification and validation of data-driven models for the description of the acid gas treatment process, a key step of flue gas cleaning in waste-to-energy plants. The acid gas removal line of an Italian plant, based on the injection of hydrated lime, Ca(OH)2, for the abatement of hydrogen chloride, HCl, is investigated. The final goal is to minimize the feed rate of reactant needed to achieve the required HCl removal performance, also reducing as a consequence the production of solid process residues. Process data are collected during dedicated plant tests carried out by imposing Generalized Binary Noise (GBN) sequences to the flow rate of Ca(OH)2. Various input-output and state-space models are identified with success, and related model orders are optimized. The models are then validated on different datasets of routine plant operation. The proposed modeling approach appears reliable and promising for control purposes, once implemented into advanced model-based control structures