Neural Roll Angle Estimation in a Model Predictive Control System

2019 ◽  
Author(s):  
Sebastian Blume ◽  
Philipp Maximilian Sieberg ◽  
Niko Maas ◽  
Dieter Schramm
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Roll Angle ◽  
Angle Estimation

Model Predictive Control System Based on FPGA and A Case Study

2011 ◽  
Vol 44 (1) ◽  
pp. 9266-9271
Author(s):  
Nan Yang ◽  
Dewei Li ◽  
Jun Zhang ◽  
Yugeng Xi
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Predictive Control

A Model Predictive Control System for Virtual Coupling

2021 ◽  
Author(s):  
Xiaolin Luo ◽  
Tao Tang ◽  
Hongjie Liu ◽  
Ming Chai ◽  
Xiwang Guo
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Virtual Coupling

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.

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