Teaching and Practicing Model Predictive Control

Honc, Daniel; Sharma, Karan; Abraham, Anuj; Dušek, František; Pappa, N.

doi:10.1016/j.ifacol.2016.07.149

Cited by 20 publications

(6 citation statements)

References 8 publications

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“…The MPC implemented architecture is the same adopted in previous works of the research group [21], that is, the Matlab MPC Toolbox by Mathworks [43]. It is a multi-input multioutput system operating on two properties: P HS and T FC_IN .…”

Section: Mpc Control Approachmentioning

confidence: 99%

Advanced Control System for Grid-Connected SOFC Hybrid Plants: Experimental Verification in Cyber-Physical Mode

Ferrari

Rossi

Sorce

et al. 2019

Journal of Engineering for Gas Turbines and Power

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This paper presents a model predictive controller (MPC) operating a solid oxide fuel cell (SOFC) gas turbine hybrid plant at end-of-life performance condition. Its performance was assessed with experimental tests showing a comparison with a proportional integral derivative (PID) control system. The hybrid system (HS) operates in grid-connected mode, i.e., at variable speed condition of the turbine. The control system faces a multivariable constrained problem, as it must operate the plant into safety conditions while pursuing its objectives. The goal is to test whether a linearized controller design for normal operating condition is able to govern a system which is affected by strong performance degradation. The control performance was demonstrated in a cyber-physical emulator test rig designed for experimental analyses on such HSs. This laboratory facility is based on the coupling of a 100 kW recuperated microturbine with a fuel cell emulation system based on vessels for both anodic and cathodic sides. The components not physically present in the rig were studied with a real-time model running in parallel with the plant. Model output values were used as set-point data for obtaining in the rig (in real-time mode) the effect of the fuel cell system. The result comparison of the MPC tool against a PID control system was carried out considering several plant properties and the related constraints. Both systems succeeded in managing the plant, still the MPC performed better in terms of smoothing temperature gradient and peaks.

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Section: Mpc Control Approachmentioning

confidence: 99%

Advanced Control System for Grid-Connected SOFC Hybrid Plants: Experimental Verification in Cyber-Physical Mode

Ferrari

Rossi

Sorce

et al. 2019

Journal of Engineering for Gas Turbines and Power

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“…Solving the flash equation with the relative volatility a = 5.68, using Eqs. (1) and (2) gives X F = 0.26095 and Y F = 0.66728. Aschematic diagram of the multivariable nonlinear process considered is shown in figure 1.…”

Section: 1bmentioning

confidence: 99%

“…It is designed to handle complex, constrained, high-dimensional processes with different numbers of controlled and manipulated variables even for non-linear processes. The importance of predictive algorithms in industrial applications includes explicit use of a dynamical process model for controlled variable prediction at a future time horizon and usage of an online optimization tool, which will generate optimal control actions required at every time instance by minimizing an objective function based on predictions as the key features [1].…”

Section: Introductionmentioning

confidence: 99%

Reduced order modelling and predictive control of multivariable nonlinear process

et al. 2018

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In this paper, an efficient model-predictive control strategy that can be applied to complex multivariable process is presented. A reduced order generalized predictive algorithm is proposed for online applications with reduction in complexity and time elapsed. The complex multivariable process considered in this work is a binary distillation column. The reduced order model is developed with a recently proposed hybrid algorithm known as Clustering Dominant Pole Algorithm and is able to compute the full set of dominant poles and their cluster centre efficiently. The controller calculates the optimal control action based on the future reference signals, current state and constraints on manipulated and controlled variables for a high-order dynamic simulated model of nonlinear multivariable binary distillation column process. The predictive control algorithm uses controlled auto-regressive integrated moving average model. The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE.

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“…One possible course for undergraduates was described by Honc et al (2016), with the course focusing on teaching the fundamentals of linear MPC and other topics such as model derivation, controller tuning and offset-free control, with a final project of applying MPC to the control of the water level in a tank. A slightly more advanced course for Masters-level students was described by Keller et al (2020), and consisted of both linear and nonlinear MPC and related topics such as optimization theory, discretization methods, and stability theory.…”

Section: Introductionmentioning

confidence: 99%

Teaching Predictive Control Using Specification-based Summative Assessments

McInerney¹,

Kerrigan²

2022

Preprint

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Including Model Predictive Control (MPC) in the undergraduate/graduate control curriculum is becoming vitally important due to the growing adoption of MPC in many industrial areas. In this paper, we present an overview of the predictive control course taught by the authors at Imperial College London between 2018 and 2021. We discuss how the course evolved from focusing solely on the linear MPC formulation to covering nonlinear MPC and some of its extensions. We also present a novel specification-based summative assessment framework, written in MATLAB, that was developed to assess the knowledge and understanding of the students in the course by tasking them with designing a controller for a real-world problem. The MATLAB assessment framework was designed to provide the students with the freedom to design and implement any MPC controller they wanted. The submitted controllers were then assessed against over 30 variations of the real-world problem to gauge student understanding of design robustness and the MPC topics from the course.

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Teaching and Practicing Model Predictive Control

Cited by 20 publications

References 8 publications

Advanced Control System for Grid-Connected SOFC Hybrid Plants: Experimental Verification in Cyber-Physical Mode

Advanced Control System for Grid-Connected SOFC Hybrid Plants: Experimental Verification in Cyber-Physical Mode

Reduced order modelling and predictive control of multivariable nonlinear process

Teaching Predictive Control Using Specification-based Summative Assessments

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