High Performance Model Predictive Control for PMSM by Using Stator Current Mathematical Model Self-Regulation Technique

In recent years, the green aviation technology draws more attention, and more hybrid power units have been applied to the aerial vehicles. To achieve the high performance and long lifetime of components during varied working conditions, the effective regulation of the energy management is necessary for the vehicles with hybrid power unit (HPU). In this paper, power prediction-based model predictive control (P2MPC) for energy management strategy (EMS) is proposed for the vehicle equipped with HPU based on turboshaft engine in order to maintain proper battery’s state of charge (SOC) and decrease turboshaft engine’s exhaust gas temperature (EGT). First, a modeling approach based on data-driven method is adopted to obtain the mathematical model of turboshaft engine considering time delay and inertial of states. An integrated power predictor consisting of the classification of input status and the subpredictors are developed based on the deep learning method to improve the accuracy of the prediction model of the model predictive control (MPC). Subsequently, an EMS based on MPC using the proposed power predictor is introduced to regulate the SOC of battery and the EGT of turboshaft engine. The comparison with experimental results shows the high accuracy of mathematical model of turboshaft engine. The simulation results show the effectiveness of the proposed EMS for the vehicle, and the effects of different weight coefficients of objective function on the proposed EMS are discussed.

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Adaptive-pole selection in the Laguerre parametrisation of model predictive control to achieve high performance

International Journal of Systems Science ◽

10.1080/00207721.2021.1933252 ◽

2021 ◽

pp. 1-17

Author(s):

Massoud Hemmasian Ettefagh ◽

Jose De Dona ◽

Farzad Towhidkhah ◽

Mahyar Naraghi

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

High Performance

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HPIPM: a high-performance quadratic programming framework for model predictive control

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.073 ◽

2020 ◽

Vol 53 (2) ◽

pp. 6563-6569 ◽

Cited By ~ 1

Author(s):

Gianluca Frison ◽

Moritz Diehl

Keyword(s):

Model Predictive Control ◽

Quadratic Programming ◽

Predictive Control ◽

High Performance ◽

Programming Framework

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Model predictive control for autonomous ground vehicles: a review

Autonomous Intelligent Systems ◽

10.1007/s43684-021-00005-z ◽

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.

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

10.1115/detc2021-69432 ◽

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.

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A High Performance Model for Task Allocation in Distributed Computing System Using K-Means Clustering Technique

International Journal of Distributed Systems and Technologies ◽

10.4018/ijdst.2018070101 ◽

2018 ◽

Vol 9 (3) ◽

pp. 1-23 ◽

Cited By ~ 5

Author(s):

Harendra Kumar ◽

Nutan Kumari Chauhan ◽

Pradeep Kumar Yadav

Keyword(s):

Mathematical Model ◽

Distributed Computing ◽

High Performance ◽

Computing System ◽

Performance Model ◽

Communication Costs ◽

Clustering Technique ◽

Proposed Model ◽

Two Stages ◽

The Mathematical Model

Tasks allocation is an important step for obtaining high performance in distributed computing system (DCS). This article attempts to develop a mathematical model for allocating the tasks to the processors in order to achieve optimal cost and optimal reliability of the system. The proposed model has been divided into two stages. Stage-I, makes the ‘n' clusters of set of ‘m' tasks by using k-means clustering technique. To use the k-means clustering techniques, the inter-task communication costs have been modified in such a way that highly communicated tasks are clustered together to minimize the communication costs between tasks. Stage-II, allocates the ‘n' clusters of tasks onto ‘n' processors to minimize the system cost. To design the mathematical model, executions costs and inter tasks communication costs have been taken in the form of matrices. To test the performance of the proposed model, many examples are considered from different research papers and results of examples have compared with some existing models.

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Mathematical modelling and model predictive controller design of a quad tiltrotor UAV

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406220971330 ◽

2020 ◽

pp. 095440622097133

Author(s):

Jingxian Liao ◽

Xiaodong Song

Keyword(s):

Mathematical Model ◽

Model Predictive Control ◽

Predictive Control ◽

Trajectory Tracking ◽

Controller Design ◽

Control Input ◽

Random Disturbance ◽

Effect Analysis ◽

Input Variables ◽

Induced Velocity

A novel convertible unmanned aerial vehicle (UAV) with four tiltable rotors and a tandem-wing system has been developed. Considering the aerodynamic effect caused by the rotor-induced velocity, a mathematical model that contains the traditional free airstream analysis and rotor-induced effect analysis is proposed, from which the precise equilibrium point of the control inputs and states can be derived. Moreover, a control allocation algorithm is designed to provide the mapping relationship between traditional input variables and specific input variables of the UAV, so that the complicated mathematical model can be linearized for the design of model predictive control (MPC) system. In order to handle the control input constraints of the UAV system, an MPC system is applied for the trajectory tracking during the cruising phase. The simulation results demonstrate that the proposed model predictive control system has stability, accuracy without a random disturbance and quick response capabilities with a random disturbance during cruising trajectory tracking, which are in high demand for the quick UAV flight system.

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