A survey on explicit model predictive control

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A survey on explicit model predictive control

Nonlinear Model Predictive Control pp Cite as. Explicit model predictive control MPC addresses the problem of removing one of the main drawbacks of MPC, namely the need to solve a mathematical program on line to compute the control action.

This computation prevents the application of MPC in several contexts, either because the computer technology needed to solve the optimization problem within the sampling time is too expensive or simply infeasible, or because the computer code implementing the numerical solver causes software certification concerns,especially in safety critical applications. Explicit MPC allows one to solve the optimization problem off-line for a given range of operating conditions of interest.

Such a function is piecewise affine in most cases, so that the MPC controller maps into a lookup table of linear gains. In this paper we survey the main contributions on explicit MPC appeared in the scientific literature. After recalling the basic concepts and problem formulations of MPC, we review the main approaches to solve explicit MPC problems, including a novel and simple suboptimal practical approach to reduce the complexity of the explicit form. The paper concludes with some comments on future research directions.

Unable to display preview. Download preview PDF. Skip to main content. This service is more advanced with JavaScript available. Advertisement Hide. This is a preview of subscription content, log in to check access. Acevedo, J. Adler, I. Alessio, A. Allwright, J. Bemporad, A. In: Proc. IEEE Trans. Besselmann, T. Borrelli, F.

a survey on explicit model predictive control

In: Di Benedetto, M. HSCC Dwivedy, Santosha Kumar, and Peter Eberhard.

Model-predictive Trajectory Tracking for Autonomous Vehicles

Shigang, Yue. Morari, Manfred, and Jay H. Qin, S. Joe, and Thomas A. Wang, Yang, and Stephen Boyd. Wang, Liuping. Hrovat, D. Di Cairano, H. Tseng, and I. Alessio, Alessandro, and Alberto Bemporad.

Bazaraa, M. Mayne, D. Rawlings, C. Rao, and P. Geyer, Tobias, Fabio D. Torrisi, and Manfred Morari. Bemporad, A.

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Lofberg, J. Ital Publication. Contributing Countries:. Toggle navigation. Home Vol 3, No 3 Ettefagh. Abstract This paper experimentally controls a flexible joint via explicit model predictive control Explicit MPC method. The scheme divides the state space into different partitions, then solves the associated multi parametric optimization in off-line computations.

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The result stores in a look-up table to be used in on-line algorithm. First, the state space equations of the flexible joint are derived and linearized around the working point. Finally, the algorithm is applied on the experimental plant.Model predictive control MPC is an advanced method of process control that is used to control a process while satisfying a set of constraints.

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It has been in use in the process industries in chemical plants and oil refineries since the s. In recent years it has also been used in power system balancing models [1] and in power electronics [2].

Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification. The main advantage of MPC is the fact that it allows the current timeslot to be optimized, while keeping future timeslots in account. This is achieved by optimizing a finite time-horizon, but only implementing the current timeslot and then optimizing again, repeatedly, thus differing from Linear-Quadratic Regulator LQR.

Also MPC has the ability to anticipate future events and can take control actions accordingly. PID controllers do not have this predictive ability. MPC is nearly universally implemented as a digital control, although there is research into achieving faster response times with specially designed analog circuitry. The models used in MPC are generally intended to represent the behavior of complex dynamical systems. The additional complexity of the MPC control algorithm is not generally needed to provide adequate control of simple systems, which are often controlled well by generic PID controllers.

Common dynamic characteristics that are difficult for PID controllers include large time delays and high-order dynamics. MPC models predict the change in the dependent variables of the modeled system that will be caused by changes in the independent variables.

In a chemical process, independent variables that can be adjusted by the controller are often either the setpoints of regulatory PID controllers pressure, flow, temperature, etc. Independent variables that cannot be adjusted by the controller are used as disturbances.

Dependent variables in these processes are other measurements that represent either control objectives or process constraints. MPC uses the current plant measurements, the current dynamic state of the process, the MPC models, and the process variable targets and limits to calculate future changes in the dependent variables. These changes are calculated to hold the dependent variables close to target while honoring constraints on both independent and dependent variables.

The MPC typically sends out only the first change in each independent variable to be implemented, and repeats the calculation when the next change is required. While many real processes are not linear, they can often be considered to be approximately linear over a small operating range. Linear MPC approaches are used in the majority of applications with the feedback mechanism of the MPC compensating for prediction errors due to structural mismatch between the model and the process.

In model predictive controllers that consist only of linear models, the superposition principle of linear algebra enables the effect of changes in multiple independent variables to be added together to predict the response of the dependent variables.

This simplifies the control problem to a series of direct matrix algebra calculations that are fast and robust. When linear models are not sufficiently accurate to represent the real process nonlinearities, several approaches can be used.

The process can be controlled with nonlinear MPC that uses a nonlinear model directly in the control application. The nonlinear model may be in the form of an empirical data fit e.

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The nonlinear model may be linearized to derive a Kalman filter or specify a model for linear MPC. An algorithmic study by El-Gherwi, Budman, and El Kamel shows that utilizing a dual-mode approach can provide significant reduction in online computations while maintaining comparative performance to a non-altered implementation.

The proposed algorithm solves N convex optimization problems in parallel based on exchange of information among controllers. MPC is based on iterative, finite-horizon optimization of a plant model. Only the first step of the control strategy is implemented, then the plant state is sampled again and the calculations are repeated starting from the new current state, yielding a new control and new predicted state path.

The prediction horizon keeps being shifted forward and for this reason MPC is also called receding horizon control.About Henri JunttilaHenri writes at Wake Up Cloud, where you can get his free course: 7 Steps to Building a Lifestyle Business Around Your Passion. Please contact us so we can fix it. Did you enjoy this post. Please share the wisdom :) googletag. Nhmutawa Thank you, very helpful points to stay present srinivasan sankar Beautiful post.

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a survey on explicit model predictive control

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a survey on explicit model predictive control

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A Survey on Explicit Model Predictive Control

NewsPublicationsFeatured Stories You can stay in touch with all things ISyE through our news feed, by reading one of our publications, or attending one of our upcoming events. Taps Maiti has been promoted to the endowed and coveted rank of MSU Foundation Professor. President Lou Anna K. Simon and Provost June Pierce Youatt presided over a high-profile ceremony at the Kellogg Conference Center on Friday, September 22, to honor him and all the newly named and endowed professors at MSU.

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Leo completed his Master's-level research on stochastic analysis and Malliavin calculus under the supervision of Prof. Frederi Viens, Chair of STT, while he was visiting the Center for Stochastic Modeling (CIMFAV) at the Universidad de Valparaiso, Chile.

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Leo is also starting a new collaboration with an STT team, as well as Prof.


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