A new optimization algorithm with application to nonlinear MPC

Martinsen, Frode; Biegler, L.T.; Foss, Bjarne A.

doi:10.1016/j.jprocont.2004.02.007

Cited by 70 publications

(53 citation statements)

References 21 publications

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“…Meadows and Rawlings [39] present an excellent tutorial on the numerical implementation of OCFE for NMPC. Biegler [40], Cuthrell and Biegler [41], Martinsen et al [42], and Tenny et al [43] provide detailed discussions on specific SQP based algorithmic implementations for NMPC. In the present implementation, each control interval in the prediction horizon was considered a single finite element for the purposes of OCFE.…”

Section: Resultsmentioning

confidence: 99%

Nonlinear MPC of a multi-zone multi-feed LDPE autoclave reactor

Jacob

Dhib

2012

Journal of Industrial and Engineering Chemistry

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Section: Resultsmentioning

confidence: 99%

Nonlinear MPC of a multi-zone multi-feed LDPE autoclave reactor

Jacob

Dhib

2012

Journal of Industrial and Engineering Chemistry

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“…Within SQP methods, the reduced Hessian method is a newly developed algorithm for solving NLP problems subject to equality constraints (c eq (x) = 0) [10,11,14] . It has been shown that the method is robust and less expensive to implement [12,13] .…”

Section: The Reduced Hessian Methodsmentioning

confidence: 99%

Adaptive air-fuel ratio control with MLP network

Wang

2005

Int J Automat Comput

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This paper presents an application of adaptive neural network model-based predictive control (MPC) to the air-fuel ratio of an engine simulation. A multi-layer perceptron (MLP) neural network is trained using two on-line training algorithms: a back propagation algorithm and a recursive least squares (RLS) algorithm. It is used to model parameter uncertainties in the nonlinear dynamics of internal combustion (IC) engines. Based on the adaptive model, an MPC strategy for controlling air-fuel ratio is realized, and its control performance compared with that of a traditional PI controller. A reduced Hessian method, a newly developed sequential quadratic programming (SQP) method for solving nonlinear programming (NLP) problems, is implemented to speed up nonlinear optimization in the MPC.

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“…As shown in [12], the MPC method was carried out with the help of two 900-MHz IMB PPC processors, which actually cannot be accepted commercially. Despite the fact that there exists computationally efficient MPC algorithm with online linearization and quadratic optimization (e.g., [13][14][15]), where the online computational requirement is acceptable, such computational simplicity is generally realized by sacrificing the control performance as the linear approximation is invalid when the system state and input deviate far away from where they are linearized. Meanwhile, all these advanced methods still suffer from tiring tuning process [9].…”

Section: Introductionmentioning

confidence: 99%

Multiple Model Predictive Functional Control for Marine Diesel Engine

Wang

Liu

et al. 2018

Mathematical Problems in Engineering

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A novel control scheme based on multiple model predictive functional control (MMPFC) is proposed to solve the cumbersome and time-consuming parameters tuning of the speed controller for a marine diesel engine. It combines the MMPFC with traditional PID algorithm. In each local linearization, a first-order plus time delay (FOPTD) model is adopted to be the approximate submodel. To overcome the model mismatches under the load disturbance conditions, we introduce a method to estimate the open-loop gain of the speed control model, by which the predictive multimodels are modified online. Thus, the adaptation and robustness of the proposed controller can be improved. A cycle-detailed hybrid nonlinear engine model rather than a common used mean value engine model (MVEM) is developed to evaluate the control performance. In such model, the marine engine is treated as a whole system, and the discreteness in torque generation, the working imbalance among different cylinders, and the cycle delays are considered. As a result, more reliable and practical validation can be achieved. Finally, numerical simulation of both steady and dynamic performances of the proposed controller is carried out based on the aforementioned engine model. A conventional welltuned PID with integral windup scheme is adopted to make a comparison. The results emphasize that the proposed controller is with stable and adaptive ability but without needing complex and tough parameters regulation. Moreover, it has excellent disturbance rejection ability by modifying the predictive multimodels online.

show abstract

A new optimization algorithm with application to nonlinear MPC

Cited by 70 publications

References 21 publications

Nonlinear MPC of a multi-zone multi-feed LDPE autoclave reactor

Nonlinear MPC of a multi-zone multi-feed LDPE autoclave reactor

Adaptive air-fuel ratio control with MLP network

Multiple Model Predictive Functional Control for Marine Diesel Engine

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