scholarly journals Research and development of the controller based on the model predictive control

2021 ◽  
Vol 29 (1) ◽  
pp. 36-45
Author(s):  
Aleksey A. Kolodin ◽  
Viktor V. Elshin
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Delay Time ◽  
Pid Controller ◽  
High Performance ◽  
Control Object ◽  
Transition Process ◽  
Manufacturing Execution System ◽  
Differential Control

Modern automated process control systems that use programmable logic controllers use software controllers based on the proportional integral-differential control law, the PID controller. In most cases, this regulator is sufficient for conducting the technological process. It has high performance with configurable and sufficient quality of regulation. However, using a PID controller for non-linear, poorly defined, multi-connected objects with a long delay time can lead to unstable control quality indicators, accumulation of errors, and ultimately to a deterioration in product quality. One of the most promising methods of control is Model Predictive Control - MPC. The method base on predictive models of control objects. The quality of the controller's control depends on how well the system dynamics described by the model used to design the controller. In most cases, MPC-based control use to handle optimal control problems on the Manufacturing Execution System-MES. However, thanks to the development of microprocessors and increased CPU performance, it becomes possible to apply the principles of control with a predictive model at a lower level, and perform real-time operational control in optimal modes. The work presents the algorithm of MPC controller. The control object is a SISO object with a nonlinear characteristic and a long transition process. Studies of the developed MPC regulator showed that the quality of regulation, compared to the PID regulator, increased by more than 20%, the time to get to set point decreased, and there was almost no over-regulation. The most effective application of the MPC controller is seen in processes with long transitions and with a significant delay time.

scholarly journals Model predictive control for autonomous ground vehicles: a review

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Shuyou Yu ◽  
Matthias Hirche ◽  
Yanjun Huang ◽  
Hong Chen ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Performance ◽  
Control Method ◽  
Theoretical Background ◽  
Future Research ◽  
Centralized Control ◽  
Ground Vehicles ◽  
Uncertain Environments ◽  
Autonomous Ground Vehicles

AbstractThis paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

Direct Longitudinal Force Feedback for High-Performance Vehicle Dynamics Control Systems

2021 ◽  
Author(s):  
Giorgio Riva ◽  
Luca Mozzarelli ◽  
Matteo Corno ◽  
Simone Formentin ◽  
Sergio M. Savaresi
Keyword(s):  
Model Predictive Control ◽  
Control Systems ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
High Performance ◽  
Force Feedback ◽  
Longitudinal Force ◽  
Spiral Path ◽  
Comparable Performance ◽  
Vehicle Dynamics Control

Abstract State of the art vehicle dynamics control systems do not exploit tire road forces information, even though the vehicle behaviour is ultimately determined by the tire road interaction. Recent technological improvements allow to accurately measure and estimate these variables, making it possible to introduce such knowledge inside a control system. In this paper, a vehicle dynamics control architecture based on a direct longitudinal tire force feedback is proposed. The scheme is made by a nested architecture composed by an outer Model Predictive Control algorithm, written in spatial coordinates, and an inner longitudinal force feedback controller. The latter is composed by four classical Proportional-Integral controllers in anti-windup configuration, endowed with a suitably designed gain switching logic to cope with possible unfeasible references provided by the outer loop, avoiding instability. The proposed scheme is tested in simulation in a challenging scenario where the tracking of a spiral path on a slippery surface and the timing performance are handled simultaneously by the controller. The performance is compared with that of an inner slip-based controller, sharing the same outer Model Predictive Control loop. The results show comparable performance in presence of unfeasible force references, while higher robustness is achieved with respect to friction curve uncertainties.

scholarly journals A Wind Farm Active Power Dispatch Strategy Considering the Wind Turbine Power-Tracking Characteristic via Model Predictive Control

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 530 ◽  
Author(s):  
Wei Li ◽  
Dean Kong ◽  
Qiang Xu ◽  
Xiaoyu Wang ◽  
Xiang Zhao ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wind Turbines ◽  
Control Strategy ◽  
Wind Farm ◽  
Control Object ◽  
Automatic Generation ◽  
Active Power ◽  
Tracking Performance ◽  
Equivalent Model

In this paper, an industrial application-oriented wind farm automatic generation control strategy is proposed to stabilize the wind farm power output under power limitation conditions. A wind farm with 20 units that are connected beneath four transmission lines is the selected control object. First, the power-tracking dynamic characteristic of wind turbines is modeled as a first-order inertial model. Based on the wind farm topology, the wind turbines are grouped into four clusters to fully use the clusters’ smoothing effect. A method for frequency-domain aggregation and approximation is used to obtain the clusters’ power-tracking equivalent model. From the reported analysis, a model predictive control strategy is proposed in this paper to optimize the rapidity and stability of the power-tracking performance. In this method, the power set-point for the wind farm is dispatched to the clusters. Then, the active power control is distributed from the cluster to the wind turbines using the conventional proportional distribution strategy. Ultra-short-term wind speed prediction is also included in this paper to assess the real-time performance. The proposed strategy was tested using a simulated wind farm based on an industrial wind farm. Good power-tracking performance was achieved in several scenarios, and the results demonstrate that the performance markedly improved using the proposed strategy compared with the conventional strategy.

High-performance small-scale solvers for linear Model Predictive Control

2014 ◽  
Author(s):  
Gianluca Frison ◽  
Hans Henrik Brandenborg Sorensen ◽  
Bernd Dammann ◽  
John Bagterp Jorgensen
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
High Performance ◽  
Small Scale

Modeling and Model Predictive Control of Semi-Batch Chemical Reactor

2013 ◽  
Vol 791-793 ◽  
pp. 822-825
Author(s):  
Lubomír Macků ◽  
David Novosad ◽  
David Sámek
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Chemical Engineering ◽  
Chemical Reactor ◽  
Batch Process ◽  
Online Identification ◽  
Whole Process ◽  
Process Simulations ◽  
Reactor Geometry

The paper presents a control mechanism design for a semi-batch chemical reactor. The data obtained by chemical engineering analysis of real experiments are used to simulate the semi‑batch process. A mathematical model based on the real reactor geometry and size is used to simulate the whole process. The process simulations are implemented in MATLAB / Simulink environment and suitable PID and Model Predictive Control are also proposed. Because of that the chemical reactor is a complex and nonlinear system, the PID controller has to use an online identification to be able to deal with nonlinearities. Results obtained by simulations are compared and discussed.

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