Nonlinear Model Predictive Control

Grancharova, Alexandra; Johansen, Tor Arne

doi:10.1007/978-3-642-28780-0_2

Cited by 16 publications

(15 citation statements)

References 108 publications

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“…The NEPSAC controller uses the explicit nonlinear model of the process described in (3). For this case, a prediction horizon of 15 ( = 15) has been used in order to have a fast response to changes in the setpoint reference and disturbances.…”

Section: B Nepsac Controllermentioning

confidence: 99%

“…Although NMPC theory had reached a certain degree of maturity in the last years, it is still limited to a certain kind of applications. This restriction is in part because of the lack of a guaranteed real-time solution of the resulting nonlinear optimization problem due to the multiple local minima the problem could have, and also because of the difficulty to create a nonlinear model of the system, together with nonlinear state estimators of the same [3].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of model-based and non-model-based strategies for nonlinear control of a three-tank system

Capcha

Ipanaqué

Keyser

2017

2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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To achieve optimal performance in the regulation of nonlinear systems, more advanced control techniques are needed. In this paper, three of these techniques are applied and compared in a system composed of three tanks in cascade. The first one is the Nonlinear Extended Prediction Self-Adaptive Control (NEPSAC). NEPSAC is a nonlinear predictive controller and therefore, is based on solving an optimization problem using the nonlinear model of the process to find a feedback control law. The second one is a gain scheduling controller using an array of autotuned PID controllers. This will be the non-model-based strategy. Finally, the gain scheduling approach is used again but with an array of linear model predictive controllers. In this case the predictive controller is EPSAC, the linear version of NEPSAC controller. Comparing all these control strategies, this work deepens in the characteristics, performance, advantages and disadvantages of these techniques to deal with nonlinear processes. It is intended to be a reference guide to choose-from a practical point of view-the most appropriate controller when dealing with a soft nonlinear system.

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Section: B Nepsac Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of model-based and non-model-based strategies for nonlinear control of a three-tank system

Capcha

Ipanaqué

Keyser

2017

2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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“…In this paper, we apply cascade control strategy to synchronous linear motor including an inner and an outer loop. The offline MPC controller based on MPP method in [15] was proposed for inner loop to make motor current to follow the reference signal from the outer controller. We modify optimization problem in the MPC controller by using a Laguerre Model approach in [13] to reduce the number of optimal variables.…”

Section: Introductionmentioning

confidence: 99%

Multi parametric model predictive control based on laguerre model for permanent magnet linear synchronous motors

Quang¹,

Quang²,

Nam³

et al. 2019

IJECE

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<p>The permanent magnet linear motors are widely used in various industrial applications due to its advantages in comparisons with rotary motors such as mechanical durability and directly creating linear motions without gears or belts. The main difficulties of its control design are that the control performances include the tracking of position and velocity as well as guarantee limitations of the voltage control and its variation. In this work, a cascade control strategy including an inner and an outer loop is applied to synchronous linear motor. Particularly, an offline MPC controller based on MPP method and Laguerre model was proposed for inner loop and the outer controller was designed with the aid of nonlinear damping method. The numerical simulation was implemented to validate performance of the proposed controller under voltage input constraints.</p>

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“…In particular, optimal solution of a linear/quadratic programming problem is known to be a piecewise affine (PWA) function defined over a polyhedral partition of the parameter space. In control theory, this structure of control laws appeared in the last decade as an approximation of the classical nonlinear control laws with respect to a predefined error [1][2][3]. Then, it is shown that this PWA structure is inherited by the exact optimal solution of a linear model predictive control (MPC) problem with respect to a linear/quadratic cost function [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Constructive Solution of Inverse Parametric Linear/Quadratic Programming Problems

Nguyễn

Olaru

Rodríguez-Ayerbe

et al. 2016

J Optim Theory Appl

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Parametric convex programming has received a lot of attention, since it has many applications in chemical engineering, control engineering, signal processing, etc. Further, inverse optimality plays an important role in many contexts, e.g., image processing, motion planning. This paper introduces a constructive solution of the inverse optimality problem for the class of continuous piecewise affine functions. The main idea is based on the convex lifting concept. Accordingly, an algorithm to construct convex liftings of a given convexly liftable partition will be put forward. Following this idea, an important result will be presented in this article: any continuous piecewise affine function defined over a polytopic partition is the solution of a parametric linear/quadratic programming problem. Regarding linear optimal control, it will be shown that any continuous piecewise affine control law can be obtained via a linear optimal control problem with the control horizon at most equal to 2 prediction steps.

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Nonlinear Model Predictive Control

Cited by 16 publications

References 108 publications

Comparison of model-based and non-model-based strategies for nonlinear control of a three-tank system

Comparison of model-based and non-model-based strategies for nonlinear control of a three-tank system

Multi parametric model predictive control based on laguerre model for permanent magnet linear synchronous motors

Constructive Solution of Inverse Parametric Linear/Quadratic Programming Problems

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