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.

Control of coagulation process by dual wavelength article analyzer

1997 ◽  
Vol 36 (4) ◽  
pp. 135-142 ◽  
Author(s):  
Norihito Tambo ◽  
Yoshihiko Matsui ◽  
Ken-ichi Kurotani ◽  
Masakazu Kubota ◽  
Hirohide Akiyama ◽  
...  
Keyword(s):  
Water Quality ◽  
Control System ◽  
Predictive Control ◽  
Control Method ◽  
Feedforward Control ◽  
Raw Water ◽  
Floc Size ◽  
Coagulation Process ◽  
Series Of Experiments ◽  
Size Data

A coagulation process for water purification plants mainly uses feedforward control based on raw water quality and empirical data and requires operator's help. We developed a new floc sensor for measuring floc size in a flush mixer to be used for floc control. A control system using model predictive control was developed on the floc size data. A series of experiments was performed to confirm controllability of settled water quality by controlling flush mixer floc size. An automatic control with feedback from the coagulation process was evaluated as practical and reliable. Finally this new control method was applied for actual plant and evaluated as practical.

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 MODEL PREDICTIVE CONTROL TOOLBOX DESIGN FOR NONSTATIONARY PROCESS

2021 ◽  
Author(s):  
Oleksandr V. Stepanets ◽  
Yurii I. Mariiash
Keyword(s):  
Optimal Control ◽  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Complex Structure ◽  
Nonstationary Process ◽  
Actual State ◽  
Quadratic Functional ◽  
Linear Quadratic ◽  
Prediction Horizon

Background. Model predictive control (MPC) approach is the basic feedback scheme, combined with high adaptive properties, which determines its successful use in the practice of design and operation of control systems. These advantages allow managing multidimensional objects with a complex structure, including nonlinearity, optimizing processes in real time within the constraints on controlled and managed variables, taking into account uncertainties in the task of objects and perturbations. Objective. The purpose of the paper is to design and analyse control system of carbon monoxide oxidation in the convector cavity based on MPC with linear-quadratic cost functional with constraint. Methods. The design of MPC is based on mathematical model of an object (relatively simple). At the current step, the prediction of object dynamic response on some final period of time (prediction horizon) is carried out; control optimization is performed, the purpose of which is to approximate the control variables of the prediction model to the corresponding setpoint on the predict horizon. The found optimal control is applied and measurement of an actual state of object at the end of a step is carried out. The prediction horizon is shifted one step further, and this algorithm are repeated. Results. The results of modeling the automatic control system show that the MPC approach provides maintenance of carbon dioxide content when changing oxygen consumption and overshoot caused by introduction bulk does not exceed 0.6 % that meets the technological requirements of the process. Conclusions. A fuse of the MPC and the quadratic functional given the constraints on the input signals is proposed. The problems of control degree of carbon oxidation in the convector cavity include non-stationarity, so the use of classical control methods is difficult. The MPC approach minimizes the cost function that characterizes the quality of the process. The predicted behaviour of a dynamic system will usually differ from its actual motion. The obtained quadratic functional is optimized to find the optimal control of degree of CO oxidation to CO2.

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 Sensitivity Analysis to Define Guidelines for Predictive Control Design

2020 ◽  
Vol 2674 (6) ◽  
pp. 385-398
Author(s):  
M. C. Poelman ◽  
A. Hegyi ◽  
A. Verbraeck ◽  
J. W. C. van Lint
Keyword(s):  
Sensitivity Analysis ◽  
Control System ◽  
Predictive Control ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Information Quality ◽  
Prediction Horizon ◽  
Technological Developments ◽  
And Performance

Signalized traffic control is important in traffic management to reduce congestion in urban areas. With recent technological developments, more data have become available to the controllers and advanced state estimation and prediction methods have been developed that use these data. To fully benefit from these techniques in the design of signalized traffic controllers, it is important to look at the quality of the estimated and predicted input quantities in relation to the performance of the controllers. Therefore, in this paper, a general framework for sensitivity analysis is proposed, to analyze the effect of erroneous input quantities on the performance of different types of signalized traffic control. The framework is illustrated for predictive control with different adaptivity levels. Experimental relations between the performance of the control system and the prediction horizon are obtained for perfect and erroneous predictions. The results show that prediction improves the performance of a signalized traffic controller, even in most of the cases with erroneous input data. Moreover, controllers with high adaptivity seem to outperform controllers with low adaptivity, under both perfect and erroneous predictions. The outcome of the sensitivity analysis contributes to understanding the relations between information quality and performance of signalized traffic control. In the design phase of a controller, this insight can be used to make choices on the length of the prediction horizon, the level of adaptivity of the controller, the representativeness of the objective of the control system, and the input quantities that need to be estimated and predicted the most accurately.

Hot Blast Stove Temperature Control System Based on Neural Network Predictive Control

2014 ◽  
Vol 709 ◽  
pp. 281-284 ◽  
Author(s):  
Yao Wu Tang ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Predictive Control ◽  
Control Method ◽  
Delay System ◽  
Temperature Control System ◽  
Control Effect ◽  
Rolling Optimization ◽  
Hot Blast ◽  
Neural Network Predictive Control

Chain type coal-fired hot blast furnace boiler has a strong coupling, large delay, large inertia characteristics. Control effect of control method of mathematic modeling method and the classical routine of it is very difficult to produce the ideal. The predictive control theory combined with neural network theory. Through the model correction and rolling optimization control method of the system is good to overcome the effects of model error and time-varying process. The experimental results showed that neural network predictive control system is improved effectively the static precision and dynamic characteristic. It has better practicability of boiler temperature of this kind of large time delay system.

scholarly journals Self-Triggered Model Predictive Control for Linear Systems Based on Transmission of Control Input Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Koichi Kobayashi
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Linear Systems ◽  
Communication Networks ◽  
Predictive Control ◽  
Control Method ◽  
Input Sequence ◽  
Networked Control System ◽  
Control Input ◽  
Sampled Data

A networked control system (NCS) is a control system where components such as plants and controllers are connected through communication networks. Self-triggered control is well known as one of the control methods in NCSs and is a control method that for sampled-data control systems both the control input and the aperiodic sampling interval (i.e., the transmission interval) are computed simultaneously. In this paper, a self-triggered model predictive control (MPC) method for discrete-time linear systems with disturbances is proposed. In the conventional MPC method, the first one of the control input sequence obtained by solving the finite-time optimal control problem is sent and applied to the plant. In the proposed method, the first some elements of the control input sequence obtained are sent to the plant, and each element is sequentially applied to the plant. The number of elements is decided according to the effect of disturbances. In other words, transmission intervals can be controlled. Finally, the effectiveness of the proposed method is shown by numerical simulations.

scholarly journals A finite set-model predictive control based on FPGA flatform for eleven-level cascaded H-Bridge inverter fed induction motor drive

2021 ◽  
Vol 12 (2) ◽  
pp. 845
Author(s):  
Mai Van Chung ◽  
Do Tuan Anh ◽  
Phuong Vu
Keyword(s):  
Model Predictive Control ◽  
Induction Motor ◽  
Predictive Control ◽  
Control Method ◽  
Dynamic Responses ◽  
Delay Compensation ◽  
Voltage Vector ◽  
Finite Control ◽  
Finite Set ◽  
The Cost

Model predictive control has been considered as a powerful alternative control method in power converters and electrical drives recently. This paper proposes a novel method for finite control set predictive control algorithm foran induction motor fed by 11-level cascaded H-Bridge converter. To deal with the high computation volume of MPC algorithm applied for CHBconverter, 7-adjacent vectors method is applied for calculating the desired voltage vector which minimizes the cost function. Moreover, by utilizingfield programmable gate array (FPGA) platform with its flexible structure,the total execution time reduces considerably so that the selected voltage vector can be applied immediately without delay compensation. This method improves the dynamic responses and steady-state performance of the system. Finally, experimental results verify the effectiveness of control design

A Study of Predictive Control of Energy-Saving of Heating Process

2011 ◽  
Vol 328-330 ◽  
pp. 1810-1813
Author(s):  
Xue Li Zhu ◽  
Shu Xian Zhu ◽  
Sheng Hui Guo
Keyword(s):  
Control System ◽  
Error Correction ◽  
Energy Saving ◽  
Real Time ◽  
Predictive Control ◽  
Control Method ◽  
Heating System ◽  
Heating Power ◽  
Heating Process ◽  
Power Station

This paper presents a predictive control method of heating system of heating power station. Firstly, the forecast of heating load is introduced using time series analysis, and the obtained result is used as an energy-saving initial value of predictive control system. Secondly, model simplification method is given and immediate control law is derived, the predictive model order is decreased from N to n. Simplification model satisfies the demand of real-time property of the control system. Thirdly, predictive error correction is used to replace error correction to implement the correction of optimum control of the system, which can improve adaptability and robustness of the system. Finally, simulation of heating system of heating power station is conducted and the results prove that the algorithm is effective in ensuring real-time control, improving tracking and robustness property.

The Research on Intelligent Control Method for EHP System

2012 ◽  
Vol 580 ◽  
pp. 12-15 ◽  
Author(s):  
Yi Wan ◽  
Qi Bo Cai ◽  
Huan Wang
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Intelligent Control ◽  
Internal Model ◽  
High Speed ◽  
Control Method ◽  
Internal Model Control ◽  
Support Vector ◽  
Proportional Control ◽  
Self Adaptive

Optimized machine learning algorithm is applied to control modeling of high-speed electric-hydraulic proportional system of high nonlinear in this paper, a identification model of high-speed electric-hydraulic proportional system is built based on support vector machines, fusion intelligent method of dynamic self-adaptive internal model control and predictive control is realized for high-speed electric-hydraulic proportional control system. Internal model and inverse controller model are online adjusted together. Simulation shows the satisfactory tracking effect by intelligent technology of dynamic self-adaptive internal control and predictive control based on the support vector machine, the dynamic characteristic is greatly improved by the intelligent control strategy for high-speed electric-hydraulic proportional control system, good tracking and control effect is reached in condition of high frequency response. It provides a new intelligent control method for high-speed electric-hydraulic proportional system.

Sign in / Sign up

Export Citation Format

Share Document