scholarly journals A multivariable multiobjective predictive controller

2013 ◽  
Vol 23 (1) ◽  
pp. 35-45 ◽  
Author(s):  
Faten Ben Aicha ◽  
Faouzi Bouani ◽  
Mekki Ksouri
Keyword(s):  
Genetic Algorithm ◽  
Predictive Control ◽  
Mimo System ◽  
Weighting Factor ◽  
Nsga Ii ◽  
Weighted Sum Method ◽  
Prediction Horizon ◽  
Predictive Controller ◽  
Pareto Method ◽  
The Cost

Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the Non- Dominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.

scholarly journals Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019 ◽  
Vol 11 (9) ◽  
pp. 2571
Author(s):  
Xujing Zhang ◽  
Lichuan Wang ◽  
Yan Chen
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Optimal Solution ◽  
Material Distribution ◽  
Low Carbon ◽  
Nsga Ii ◽  
Management Objectives ◽  
Multi Objective Genetic Algorithm ◽  
Distribution Process ◽  
The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

scholarly journals Model Predictive Control Tuning by Inverse Matching for a Wave Energy Converter

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4158 ◽  
Author(s):  
Hancheol Cho ◽  
Giorgio Bacelli ◽  
Ryan G. Coe
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Wave Energy Converter ◽  
Energy Converter ◽  
Feedback Form ◽  
Linear Controller ◽  
Prediction Horizon ◽  
The Cost ◽  
Input And State Constraints

This paper investigates the application of a method to find the cost function or the weight matrices to be used in model predictive control (MPC) such that the MPC has the same performance as a predesigned linear controller in state-feedback form when constraints are not active. This is potentially useful when a successful linear controller already exists and it is necessary to incorporate the constraint-handling capabilities of MPC. This is the case for a wave energy converter (WEC), where the maximum power transfer law is well-understood. In addition to solutions based on numerical optimization, a simple analytical solution is also derived for cases with a short prediction horizon. These methods are applied for the control of an empirically-based WEC model. The results show that the MPC can be successfully tuned to follow an existing linear control law and to comply with both input and state constraints, such as actuator force and actuator stroke.

scholarly journals Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 34 ◽  
Author(s):  
Germán Ramos Ruiz ◽  
Eva Lucas Segarra ◽  
Carlos Fernández Bandera
Keyword(s):  
Genetic Algorithm ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Search Space ◽  
Building Energy ◽  
Energy Performance ◽  
Hvac Systems ◽  
Convergence Time ◽  
Computational Time ◽  
Nsga Ii

There is growing concern about how to mitigate climate change in which the reduction of CO2 emissions plays an important role. Buildings have gained attention in recent years since they are responsible for around 30% of greenhouse gases. In this context, advance control strategies to optimize HVAC systems are necessary because they can provide significant energy savings whilst maintaining indoor thermal comfort. Simulation-based model predictive control (MPC) procedures allow an increase in building energy performance through the smart control of HVAC systems. The paper presents a methodology that overcomes one of the critical issues in using detailed building energy models in MPC optimizations—computational time. Through a case study, the methodology explains how to resolve this issue. Three main novel approaches are developed: a reduction in the search space for the genetic algorithm (NSGA-II) thanks to the use of the curve of free oscillation; a reduction in convergence time based on a process of two linked stages; and, finally, a methodology to measure, in a combined way, the temporal convergence of the algorithm and the precision of the obtained solution.

scholarly journals Multi-objective optimization of retaining wall using genetic algorithm

2021 ◽  
Vol 8 (1-2) ◽  
pp. 58-65
Author(s):  
Filip Dodigović ◽  
Krešo Ivandić ◽  
Jasmin Jug ◽  
Krešimir Agnezović
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Retaining Wall ◽  
Silty Sand ◽  
Embedment Depth ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Definition Of ◽  
The Cost

The paper investigates the possibility of applying the genetic algorithm NSGA-II to optimize a reinforced concrete retaining wall embedded in saturated silty sand. Multi-objective constrained optimization was performed to minimize the cost, while maximizing the overdesign factors (ODF) against sliding, overturning, and soil bearing resistance. For a given change in ground elevation of 5.0 m, the width of the foundation and the embedment depth were optimized. Comparing the algorithm's performance in the cases of two-objective and three objective optimizations showed that the number of objectives significantly affects its convergence rate. It was also found that the verification of the wall against the sliding yields a lower ODF value than verifications against overturning and soil bearing capacity. Because of that, it is possible to exclude them from the definition of optimization problem. The application of the NSGA-II algorithm has been demonstrated to be an effective tool for determining the set of optimal retaining wall designs.

Application of the Headstock of CNC Boring Machine for Tractor Engine Cylinder Block Based on Multi-Objective Genetic Algorithm

2021 ◽  
Vol 37 (2) ◽  
pp. 343-349
Author(s):  
Yahui   Wang ◽  
Ling   Shi ◽  
Yiqi   Dang ◽  
Shengkai   Sun ◽  
Huipeng   Zhang
Keyword(s):  
Genetic Algorithm ◽  
Numerical Control ◽  
Gear Train ◽  
List Type ◽  
Weighted Sum ◽  
Cnc Machine ◽  
Nsga Ii ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Tooth Width

HighlightsThe headstock of the single-sided horizontal CNC boring machine specializing in processing tractor 6-cylinder engine cylinders is optimized.The constraint conditions such as tooth width and modulus are constructed. The model is optimized by the NSGA algorithm, and the optimization results are good.The optimization results of the NSGA algorithm are compared with the results of the weighted sum method and the GA, which highlights the superiority of the NSGA algorithm.ABSTRACT. The tractor is one of the most frequently used equipment in agricultural production, and its mass production is the general trend. With the continuous advancement of the global industrialization process, the importance of Computer Numerical Control (CNC) machine in the entire industrial production has become more and more prominent, and the application of CNC machine in tractor manufacturing has greatly improved production efficiency. This article takes the headstock of a single-sided horizontal CNC boring machine dedicated to processing tractor 6-cylinder engine cylinders as the research objective, takes the key parameters of the gear train in the headstock as the optimization design variables, constructs constraints, such as modulus, tooth width, etc., establishes a multi-objective optimization mathematical model, uses the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to process the model and obtains the Pareto solution set through multiple iterations. The optimization results show that the volume, center distance and the reciprocal of coincidence degree of the main shaft 1 transmission group are reduced in varying degrees. Finally, it is compared with the weighted sum method and genetic algorithm (GA) to highlight the superiority of NSGA-II. Keywords: Headstock, Multi-optimization, Non-dominated Sorting Genetic Algorithm II, Tractor cylinder.

A Non-Dominant Sorting Genetic Algorithm for Optimization of a Product Design and Selection of Its Suppliers

2020 ◽  
Vol 19 (01) ◽  
pp. 167-188
Author(s):  
Oulfa Labbi ◽  
Abdeslam Ahmadi ◽  
Latifa Ouzizi ◽  
Mohammed Douimi
Keyword(s):  
Genetic Algorithm ◽  
Product Design ◽  
Computational Time ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Solution Quality ◽  
Holding Costs ◽  
Weighted Sum Method ◽  
The Impact ◽  
Selection Of

The aim of this paper is to address the problem of supplier selection in a context of an integrated product design. Indeed, the product specificities and the suppliers’ constraints are both integrated into product design phase. We consider the case of improving the design of an existing product and study the selection of its suppliers adopting a bi-objective optimization approach. Considering multi-products, multi-suppliers and multi-periods, the mathematical model proposed aims to minimize supplying, transport and holding costs of product components as well as quality rejected items. To solve the bi-objective problem, an evolutionary algorithm namely, non-dominant sorting genetic algorithm (NSGA-II) is employed. The algorithm provides a set of Pareto front solutions optimizing the two objective functions at once. Since parameters values of genetic algorithms have a significant impact on their efficiency, we have proposed to study the impact of each parameter on the fitness functions in order to determine the optimal combination of these parameters. Thus, a number of simulations evaluating the effects of crossover rate, mutation rate and number of generations on Pareto fronts are presented. To evaluate performance of the algorithm, results are compared to those obtained by the weighted sum method through a numerical experiment. According to the computational results, the non-dominant sorting genetic algorithm outperforms the CPLEX MIP solver in both solution quality and computational time.

Modified Predictive Control System of the DC-DC Boost Converter

2021 ◽  
Vol 26 (6) ◽  
pp. 533-546
Author(s):  
A.A. Cherdintsev ◽  
◽  
A.V. Shagin ◽  
S.A. Lupin ◽  
◽  
...  
Keyword(s):  
Control System ◽  
Objective Function ◽  
Predictive Control ◽  
Control Method ◽  
Reference Level ◽  
Computing Power ◽  
Fast Transient Response ◽  
Prediction Horizon ◽  
The Cost ◽  
High Degree

Nowadays, predictive control systems are becoming more and more popular, which significantly reduce the cost of setting up converters. However, DC-DC converter control problem persists. In this work, a modified model of the predictive control system (MPCS) for step-up DC-DC converters is presented. For its implementation, a nonlinear model of a converter with discrete time switching was derived, which describe a continuous conduction mode of operation. The synthesis of the controller was achieved by formulating the objective function that should be minimized considering the dynamic model of the converter. The proposed predictive control strategy, used as a voltage control system, allows keeping the output voltage at the reference level. The modified system for calculating the objective function makes it possible to significantly reduce the required computing power and expand the prediction horizon. The results of modeling have been presented that demonstrate the advantages of the proposed control method: a fast transient response and a high degree of robustness.

Controlling of Mobile Robot by Using of Predictive Controller

2017 ◽  
Vol 6 (3) ◽  
pp. 207
Author(s):  
Alireza Rezaee
Keyword(s):  
Mobile Robot ◽  
Weighting Factor ◽  
Superior Performance ◽  
Adaptive Controllers ◽  
Prediction Horizon ◽  
Predictive Controller ◽  
Predictive Controllers ◽  
Signal Filter

In this paper implementation of Model Predictive<br />Controller on mobile robot was explained. The conducted<br />experiments show effectiveness of the proposed method on<br />control of the mobile robot. Furthermore the effects of the model<br />parameters such as control horizon, prediction horizon,<br />weighting factor and signal filter band on the controller<br />performance were studied. Finally, a comparison between the<br />designed MPC controller and PID and adaptive controllers was<br />presented demonstrating superior performance of the Model<br />Predictive Controllers.

scholarly journals Power Loss Analysis of Solar Photovoltaic Integrated Model Predictive Control Based On-Grid Inverter

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4669 ◽  
Author(s):  
Amit Kumer Podder ◽  
Md. Habibullah ◽  
Md. Tariquzzaman ◽  
Eklas Hossain ◽  
Sanjeevikumar Padmanaban
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
Power Loss ◽  
Weighting Factor ◽  
Small Scale ◽  
Switching Frequency ◽  
Pv Inverter ◽  
The Cost ◽  
Frequency Term

This paper presents a finite control-set model predictive control (FCS-MPC) based technique to reduce the switching loss and frequency of the on-grid PV inverter by incorporating a switching frequency term in the cost function of the model predictive control (MPC). In the proposed MPC, the control objectives (current and switching frequency) select an optimal switching state for the inverter by minimizing a predefined cost function. The two control objectives are combined with a weighting factor. A trade-off between the switching frequency (average) and total harmonic distortion (THD) of the current was utilized to determine the value of the weighting factor. The switching, conduction, and harmonic losses were determined at the selected value of the weighting factor for both the proposed and conventional FCS-MPC and compared. The system was simulated in MATLAB/Simulink, and a small-scale hardware prototype was built to realize the system and verify the proposal. Considering only 0.25% more current THD, the switching frequency and loss per phase were reduced by 20.62% and 19.78%, respectively. The instantaneous overall power loss was also reduced by 2% due to the addition of a switching frequency term in the cost function which ensures a satisfactory empirical result for an on-grid PV inverter.

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