Model predictive controller using Laguerre functions for dynamic positioning system

Qian, Xiaobin; Yin, Yixin; Zhang, Xiufeng; Sun, Xiaofeng; Shen, Helong

doi:10.1109/chicc.2016.7554043

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…In this approach, for the case of rapid sampling, complicated process dynamics and/or high demands on closed-loop performance, satisfactory approximation of the control signal ∆u(k) may require a very large number of parameters (large m), leading to poorly numerically conditioned solutions and heavy computational load when implemented on-line. Instead, a more appropriate technique would be to use Laguerre network in the design of MPC presented in [9], [16].…”

Section: Assumptionsmentioning

confidence: 99%

Networked Cooperation-Based Distributed Model Predictive Control Using Laguerre Functions for Large-Scale Systems

Menighed

Chemori

Boussaid

et al. 2020

Lecture Notes in Electrical Engineering

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This paper proposes a novel cooperative distributed control system architecture based on unsupervised and independent Model Predictive Control (MPC) using discretetime Laguerre functions to improve the performance of the whole system. In this distributed framework, local MPCs algorithms might exchange and require information from other sub-controllers via the communication network to achieve their task in a cooperative way. In order to reduce the computational burden in the local rolling optimization with a sufficiently large prediction horizon, the orthonormal Laguerre functions are used to approximate the predicted control trajectory. Simulation results show that the proposed architecture could guarantee satisfactory global performance even under strong interactions among the subsystems.

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Section: Assumptionsmentioning

confidence: 99%

Networked Cooperation-Based Distributed Model Predictive Control Using Laguerre Functions for Large-Scale Systems

Menighed

Chemori

Boussaid

et al. 2020

Lecture Notes in Electrical Engineering

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“…Therefore, the coefficient vector η is optimized and computed in the design procedure. The task is finding the coefficient vector η to minimize the following cost function [21]:…”

Section: X(ki)mentioning

confidence: 99%

Predictive Control Algorithm Using Laguerre Functions for Greenhouse Temperature Control, Mohamed Outanoute, Abdelouahed Selmani

Guerbaoui¹,

Ed-Dahhak²,

Lachhab³

et al. 2018

IJCA

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In this paper, a discrete time predictive control (DMPC) algorithm using Laguerre orthonormal functions based model was proposed for controlling the temperature under the greenhouse system. In order to design the controller, a state-space model of the temperature of the system is identified. The observer can be designed using concepts of Kalman filtering, which consider stochastic disturbance in the process and/or measurements. The efficiency of the DMPC method is established, due to a particular choice of the synthesis parameters (the horizon of prediction, the number of Laguerre functions and its scaling factor and the weighting matrices). The proposed algorithm was applied to a scenario consisting of temperature set-point changes. The system response performances of temperature stabilization were acceptable; through a real-time implementation to confirm the capacity of the developed technique for controlling the greenhouse temperature.

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“…In addition to these relatively mature methods, many scholars improve the computation process for their own control objects to improve the operation rate. Laguerre function was introduced in reference [21] to describe the control incremental signal, thus reducing the dimensions of each coefficient matrix in the optimization problem and improving the operation rate of MPC in marine dynamic positioning control. Reference [22] reduces the quadratic programming operation time of MPC in FPGA through particle swarm optimization algorithm and parallel operation based on FPGA.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control With Disturbance Observer for Marine Diesel Engine Speed Control

Shu

Liu³

et al. 2023

IEEE Access

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The application of model predictive control (MPC) algorithm in the fixed phase control of marine diesel engine speed is studied under the premise of considering model mismatch and external disturbance. Firstly, the steady-state error problem of conventional MPC controller is solved by changing nonlinear model to incremental form. Furthermore, discrete disturbance observer (DO) is introduced in the feedback correction, which can filter out the high-frequency disturbance and reduce the requirement of algorithm on the accuracy of model. Then, considering that nonlinear MPC based on DO (DONMPC) requires a large amount of online computation, the algorithm is simplified by preliminarily converting the nonlinear model to linear model. Through analysis, the controller performance of the two models is similar. Furthermore, considering that the speed of marine diesel engine is usually set to a few fixed reference values, a linear multi-model predictive controller based on DO (DOLMMPC) with less online calculation is proposed. Finally, the designed controllers are verified by experiments. The software simulations of the designed controllers and the PID controller are carried out on the cylinder-by-cylinder mean value engine model (MVEM). It is proved that the algorithm simplification method retains the control performance of the DONMPC algorithm, and the control performance of the designed two controllers is better than the PID controller. Moreover, the DOLMMPC controller and PID controller are tested on the semi-physical simulation platform. The results demonstrate that the DOLMMPC controller can meet the computational power limit of the microprocessor in practical engineering.

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Model predictive controller using Laguerre functions for dynamic positioning system

Cited by 6 publications

References 7 publications

Networked Cooperation-Based Distributed Model Predictive Control Using Laguerre Functions for Large-Scale Systems

Networked Cooperation-Based Distributed Model Predictive Control Using Laguerre Functions for Large-Scale Systems

Predictive Control Algorithm Using Laguerre Functions for Greenhouse Temperature Control, Mohamed Outanoute, Abdelouahed Selmani

Model Predictive Control With Disturbance Observer for Marine Diesel Engine Speed Control

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