Author of the model predictive control simulink library and commands. Model predictive control linear convex optimal control. Tutorial overview of model predictive control, ieee control systems magazine, vol. A basic model predictive control mpc tutorial demonstrates the capability of a solver to determine a dynamic move plan. Highperformance model predictive control for process industry pure. Mpc model predictive control also known as dmc dynamical matrix control. Optimization problems in model predictive control stephen wright jim rawlings, matt tenny, gabriele pannocchia university of wisconsinmadison focm 02.
Getting started with model predictive control toolbox. Tutorial overview of model predictive control ieee control. This paper explores the interaction between model predictive control and optimization. Gainscheduled control of nonlinear plants by switching controllers at run time. Research interests include model predictive control, hybrid systems, optimization algorithms, and applications to automotive, aerospace, and energy systems. Rawlings 2000, tutorial overview of model predictive control. Excitingly, in the pharmaceutical industry, the application of the modelpredictive design, optimization, and control is virgin territory, wide open to researchers and technology providers. Introduction in this paper, we provide a formal design. Model predictive control college of engineering uc santa barbara. It is aimed at readers with control expertise, particularly practitioners, who. Decentralized convex optimization via primal and dual decomposition. Some simulation abilities were provided to simulate the closed loop performance of the controlled hybrid system. The success of model predictive control in controlling constrained linear systems is due, in large part, to the fact that the online optimization problem is convex, usually a quadratic programme, for which reliable software is available. Its popularity steadily increased throughout the 1980s.
Theoretical aspects model predictive control mpc is a powerful control design method for constrained dynam ical systems. This can help completion engineers adjust the pumping schedule to optimize completion costs on the fly. Outline 1 estimator module design for offsetfree tracking 2 steadystate optimization module design 3 dynamic optimization module design 4 closedloop implementation and receding horizon principle 5 quick overview of numerical optimization g. How 2 abstract a formulation for model predictive control is presented for application to vehicle maneuvering problems in which the target regions need not contain equilibrium points. The problem of cost effective optimization of system output is taken into account in a multiobjective predictive control formulation and applied on a windmill park case study. Model predictive control mpc is one of the most successful control techniques that can be used with hybrid systems. It also provides standalone e ciently implemented rungekutta and bdf integrators for the simulation of odes and daes. As we will see, mpc problems can be formulated in various ways in yalmip. The basic ideaof the method isto considerand optimizetherelevant variables, not. In this example, a linear dynamic model is used with the excel solver to determine a sequence of manipulated variable mv adjustments that drive the controlled variable cv along a. Ece7850 wei zhang ece7850 lecture 8 nonlinear model predictive control. A strategy is proposed to enable selection of optimality criteria as a function of context conditions of system operating conditions.
Tutorial 12 introduction the model predictive control mpc toolbox is a collection of functions commands developed for the analysis and design of model predictive control mpc systems. Tuning of model predictive control with multiobjective optimization 335 brazilian journal of chemical engineering vol. For example, there is less variation in process variables pvs, which allows set points to be chosen that are closer to performance boundaries, which in turn leads to an increased throughput and a higher profit. Selected applications in areas such as control, circuit design. Mpc uses the model of a system to predict its future behavior, and it solves an optimization problem to select the best control action. Model predictive control of vehicle maneuvers with. Module 09 optimization, optimal control, and model. Publishers pdf, also known as version of record includes final page, issue and. Optimization and model predictive control for overload mitigation in resilient power systems by mads r.
Model predictive control toolbox getting started guide. A widely recognized shortcoming of model predictive control mpc is that it can usually only be used in applications with slow dynamics, where the sample time is measured in seconds or minutes. Model predictive control has had an exceptional history with early intimations in the academic literature coupled with an explosive growth due to its independent adoption by the process industries where it proved to be highly successful in comparison with alternative methods of multivariable control. Adaptive control, realtime optimization, model predictive control 1. To prepare for the hybrid, explicit and robust mpc examples, we solve some standard mpc examples. Nonlinear model predictive controller toolbox masters thesis in the masters programme in systems, control and mechatronics ehsan harati department of signals and systems division of automatic control, automation and mechatronics chalmers university of technology abstract model predictive control mpc is an optimal control method. This article explains the challenges of traditional mpc implementation and introduces a new configurationfree mpc implementation concept. Model predictive control mpc has developed considerably over the last two decades, both within. Model predictive control has had an exceptional history with early intimations in the academic literature coupled with an explosive growth due to its independent adoption by the process industries where it proved to be highly successful in comparison.
Mpc optimization to a quadratic programming problem qp. Model predictive control optimization via genetic algorithm. Model predictive control was conceived in the 1970s primarily by industry. Model predictive control mpc stands for a family of methods that select control actions based on online optimization of an objective function. Manfred morari professor at the automatic control laboratory and former head of department. The basic principles and theoretical results for mpc are almost the same for most nonlinear systems, including discretetime hybrid systems. See this paper for the precise problem formulation and meanings of the. In mpc, a system model and current and historical mea. A complete solution manual more than 300 pages is available for course instructors.
Tutorial overview of model predictive control ieee. Fast model predictive control using online optimization. The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. Model predictive control mpc technology from abb what is new. Mpc brings a structured approach to solutions that would otherwise consist of combinations of feedforward and fee. Course on model predictive control part ii linear mpc design. Excitingly, in the pharmaceutical industry, the application of the model predictive design, optimization, and control is virgin territory, wide open to researchers and technology providers. Urea plant of yara at brunsbuttel germany, where a mpc control system has been set by ipcos. At time k solve an open loop optimal control problem over a predefined. The formulation is shown to be nominally stabilizing in the presence of constraints provided inconsistent state constraints are relaxed. An introduction to modelbased predictive control mpc. This thesis investigates design and implementation of continuous time model predictive control using laguerre polynomials and extends the design approaches proposed in 43 to include intermittent predictive control, as well as to include the case of the nonlinear predictive control.
Mpc model predictive control also known as dmc dynamical matrix control gpc generalized predictive control rhc receding horizon control control algorithms based on numerically solving an optimization problem at each step constrained optimization typically qp or lp receding horizon control. Nlc with predictive models is a dynamic optimization approach that seeks to follow. We focus on formulating mpc as an infinite horizon optimal control strategy with a quadratic performance criterion. At each time step, compute control by solving an open loop optimization problem for the prediction horizon apply the first value of the computed control sequence at the next time step, get the system state and recompute. Dynamic control is also known as nonlinear model predictive control nmpc or simply as nonlinear control nlc. Apr 02, 2015 dynamic control is also known as nonlinear model predictive control nmpc or simply as nonlinear control nlc. A streamlined implementation is presented for constrained linear systems. Systems in the university of michigan 20 doctoral committee. Optimization in model predictive control springerlink. May 15, 2018 mpc uses the model of a system to predict its future behavior, and it solves an optimization problem to select the best control action.
Module 09 optimization, optimal control, and model predictive. Ece7850 lecture 8 nonlinear model predictive control. In this context, advance control strategies to optimize hvac systems are necessary because they can provide significant energy savings whilst maintaining indoor thermal comfort. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control as well as state and parameter estimation. Alexander domahidi inspireifa sunday, february 16, 14 manfred morari. The focus of the thesis is on both the optimization algorithms tailored to exploit the special structure of the model predictive control problem and the implementation thanks to a novel implementation strategy for the dense linear algebra routines in embedded optimization. Simulationbased model predictive control mpc procedures allow an increase in building energy performance through the smart control of hvac systems.
Introduction to model predictive control mpc home pages of esat. Optimization and modelpredictive control for overload mitigation in resilient power systems by mads r. Model predictive control of vehicle maneuvers with guaranteed. Model predictive control mpc is a wellestablished technology for advanced process control apc in many industrial applications like blending, mills, kilns, boilers and distillation columns. The idea behind this approach can be explained using an example of driving a car. Control engineering 1517 mpc as imc mpc with disturbance estimator is a special case of imc plant prediction model reference optimizer output disturbance. Fast model predictive control using precomputed solutions instead of runtime optimization. Multiobjective predictive control optimization with varying.
The model predictive controller qp solvers convert an mpc optimization problem to a general form quadratic programming problem. Almassalkhi a dissertation submitted in partial ful llment of the requirements for the degree of doctor of philosophy electrical engineering. Ee364b convex optimization ii stanford engineering everywhere. Optimization and modelpredictive control for overload. The constrained optimization problem to be solved is min. Lee school of chemical and biomolecular engineering center for process systems engineering georgia inst. Taha module 09 optimization, optimal control, and model predictive control 9 32 intro to optimization intro to model predictive control discrete lmpc formulation constrained mpc empc introduction to mpc example 1. The paper provides a reasonably accessible and selfcontained tutorial exposition on model predictive control mpc. Theoretical aspects model predictive control mpc is a powerful control design method for constrained dynamical systems.
Design model predictive controllers with nonlinear prediction models, costs, and constraints. Using model predictive control brings many benefits. This paper presents a tutorial survey of model predictive control for constrained linear plants and nonlinear plants. Algorithms and methods for fast model predictive control. See this paper for the precise problem formulation and meanings of the algorithm parameters. Linear mpc typically leads to specially structured convex quadratic programs qp that can be solved by structure exploiting active set, interior point, or gradient methods. Mpc has gained wide acceptance in the chemical and other process industries as the basis for advanced multivariable control schemes. Zheng, nonlinear model predictive control, springerverlag. Model predictive control of vehicle maneuvers with guaranteed completion time and robust feasibility arthur richards 1 and jonathan p. Simulationbased optimal tuning of model predictive. Model predictive control mpc is a powerful control design method for constrained dynam ical systems.
Introduction to model predictive control riccardo scattoliniriccardo scattolini. Tutorial overview of model predictive control ieee control systems mag azine author. Control design for linear systems w constraints and hybrid systems on. Model predictive control mpc, also known as receding horizon control or moving horizon control, uses the range of control methods, making the use of an explicit dynamic plant model to predict the effect of future reactions of the manipulated variables on the output and the control signal obtained by minimizing the cost function 7. The control calculations are based on optimizing an ob. Model predictive control mpc technology from abb what. Model predictive controllers compute optimal manipulated variable control moves by solving a quadratic program at each control interval. Model predictive control of hybrid systems ut yt hybrid system reference rt input output measurements controller model.
In this paper, we use model predictive control mpc with system identification to optimize completion cost subject to realtime operational constraints. With most control systems, that is usually a reasonably safe as sumption given that we must rerun the optimization, probably need to ac count for this computational delay di. An introduction to modelbased predictive control mpc by stanislaw h. Model predictive control offers several important advantages. Model predictive control apmonitor optimization suite. Multiobjective predictive control optimization with. A simulation example is used to demonstrate the e ectiveness of the design technique. Optimization algorithms for model predictive control. Tutorial on model predictive control of hybrid systems. These methods have proven to be effective approaches to improve operational efficiency and have been widely used in various process industries. Bellman value function vz is optimal value of control problem as a function of initial state z can show v is convex v satis.