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Handbook of model predictive control
Full text linkHandbook 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 performance monitoring algorithm for sustained optimal operation with economic MPC
Full text linkThis paper addresses the problem of performance monitoring for Economic Model Predictive Control (EMPC) in the presence of plant parameter changes. In order to cope with plant-model mismatch, we adopt a recently developed offset-free EMPC algorithm which requires the gradient of the plant input-output steady-state map. A subspace identification method is used in order to estimate this plant gradient from transient measurements. However, when the plant parameters change, this method may fail unless re-identification is performed. Hence, to start a new data collection for the identification an event-triggered mechanism is proposed, based on a suitable performance monitoring strategy. In this case this mechanism investigates a possible, more profitable, steady-state equilibrium and, if convenient, it re-identifies the plant gradient. The proposed monitoring technique is then successfully tested over an illustrative example of a chemical reactor
Toward a Unifying Framework Blending Real-Time Optimization and Economic Model Predictive Control
No full textNowadays, real-time optimization (RTO) and nonlinear as well as linear model predictive control (MPC) are standard methods in operation and process control systems. Hence there exists a good understanding of how to combine RTO and set point tracking MPC schemes. However, recently, there has been substantial progress in analyzing the properties of so-called economic MPC schemes. This paper proposes a conceptual framework to blend ideas from (output) modifier adaptation and offset-free economic MPC with recent results on economic MPC without terminal constraints. Specifically, we leverage recent insights into economic MPC based on turnpike and dissipativity properties of the underlying optimal control problem. Interestingly, the proposed scheme alleviates the need for a dedicated computation of steady-state targets by exploiting the turnpike property in the open-loop predictions. Two detailed simulation examples show that the proposed schemes deliver excellent performance, while being conceptually much simpler
Offset-free IMC with generalized disturbance models
No full textIntegral action is an essential component for offset-free response in many feedback control systems. Standard internal model control design rules ensure an offset-free response for open-loop stable plants. In this paper a linear feedback control structure is proposed that comprises an observer using an augmented plant model, state estimate feedback and disturbance estimate feedback. This allows internal model control design principles to be applied to unstable and marginally stable plants. 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 design is illustrated on a large scale non-square model with marginally stable dynamics
On speeding-up modifier-adaptation schemes for real-time optimization
No full textThe real-time optimization scheme "modifier adaptation"(MA) has been developed to enforce steady-state plant optimality in the presence of model uncertainty. The key feature of MA is its ability to locally modify the model by adding bias and gradient correction terms to the cost and constraint functions or, alternatively, to the outputs. Since these correction terms are static in nature, their computation may require a significant amount of time, especially with slow processes. This paper presents two ways of speeding-up MA schemes for real-time optimization. The first approach proposes to estimate the modifiers from steady-state data via a tailored recursive least-squares scheme. The second approach investigates the estimation of static correction terms during transient operation. The idea is to first develop a calibration model to express the static plant- model mismatch as a function of inputs only. This calibration model can be generated via a single MA run that successively visits various steady states before reaching plant optimality. In addition, to account for process differences between calibration and subsequent operation, bias terms are estimated online from output measurements. Implementation and performance aspects are compared on two pedagogical examples, namely, an unconstrained nonlinear SISO plant and a constrained multivariable CSTR example
MPC based optimization applied to treatment of HCV infections
Full text linkBackground and Objective: The recent introduction of antivirals for the treatment of the hepatitis C virus opens new frontiers but also poses a significant burden on public health systems. This paper presents a simulation study in which model predictive control (MPC) is proposed for optimizing the therapy aiming to obtain a reduction of the costs of therapy, while maintaining the best pharmacological control of the infection.
Methods: A dynamic model describing the evolution of hepatitis C is deployed as internal model for MPC implementation, using nominal values of parameters. Different closed-loop simulations are presented both in nominal and in mismatch conditions. In addition, a more easily implementable treatment is proposed, which is based on a discrete dosage approach, where days on/off therapy are considered instead of continuous therapy modulation.
Results: Results show that therapy modulation allows one to achieve the same infection evolution as with full therapy, with a reduction of drug consumption between 10% and 40%. The alternative discrete dosage approach shows similar results achieved with therapy modulation, both in terms of therapy effectiveness and drug consumption reduction.
Conclusions: The proposed model predictive control therapy optimization strategies appear to be effective, implementable and robust to model errors. It therefore represents a potentially useful approach to alleviate the burden of HCV therapy cost on national health systems
Symbolic dynamics for active sets of a class of constrained nonlinear optimal control and MPC problems
No full textSolutions to optimal control problems are usually understood to provide optimal trajectories. In this paper, we show that the optimal state-space system dynamics induce a dynamics of the active sets. More specifically, given the optimal active set at the solution obtained at the current time, its successor optimal active set (which, in turn, defines the successor solution) can be found with index set operations. These operations do not involve any optimal control (or other optimization or integration) problem, but they can be described with simple rules. These rules constitute the symbolic dynamics for active sets. The present paper treats a particular constrained nonlinear problem class, extending earlier results for the constrained linear-quadratic case. Copyright (C) 2024 The Authors
Enhancing MPC formulations by identification and estimation of valve stiction
Full text linkA common source of poor control performance in industrial processes is represented by stiction in control valves, which often induces offset, oscillating behavior, and even loss of stability. Recent studies have investigated the effectiveness of embedding stiction models into model predictive controller (MPC) schemes, moving from stiction unaware to different stiction aware formulations, which help to remove fluctuations and may guarantee higher set-point tracking ability. To this aim, along with the process model the controller needs to use a dynamic model of sticky valves. This paper proposes an efficient, computational approach to obtain both valve and process dynamics, under the framework of Hammerstein system identification, which is based on nonlinear, gradient-based, numerical optimization. In order to improve the computational behavior and effectiveness of the methodology, a recently proposed smoothed model of stiction is deployed. The proposed methodology is validated in several (single-input single-output, and multivariable) examples, where the effectiveness of the obtained stiction aware MPC regulator is also evaluated against a stiction unaware counterpart
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