scholarly journals Compensating the performance of permanent magnet synchronous machines for fully electric vehicle using LPV control

2021 ◽  
Vol 10 (4) ◽  
pp. 1923-1929
Author(s):  
Hassam Muazzam ◽  
Mohamad Khairi Ishak ◽  
Athar Hanif
Keyword(s):  
Mathematical Model ◽  
Permanent Magnet ◽  
Electric Vehicle ◽  
Control Technique ◽  
Synchronous Machines ◽  
Linear Parameter ◽  
Rotor Speed ◽  
Permanent Magnet Synchronous Machines ◽  
Linear Parameter Varying ◽  
Parameter Varying

The state-of-the-art robust H∞ linear parameter-varying controller is designed for wide speed operating range for non-linear mathematical model of permanent magnet synchronous machines (PMSM) in d-q reference frame for fully electric vehicle. This study propose polytopic approach using rotor speed as scheduling variable to reformulate mathematical model of PMSM into linear parameter varying (LPV) form. The weights were optimized for sensitivity and complementary sensitivity function. The simulation results illustrate fast tracking and enhanced performance of the proposed control technique over wide range of rotor speed. Moreover, as part of this work, the results of H∞ linear parameter varying controller is validated by comparing it with linear quadratic integrator and proportional integral derivative (PID) control techniques to show the effectiveness of the proposed control technique.

Robust gain-scheduled linear parameter-varying control algorithm for a lab helicopter: A linear matrix inequality–based approach

2018 ◽  
Vol 232 (5) ◽  
pp. 558-571 ◽  
Author(s):  
Adnan Jafar ◽  
Aamer Iqbal Bhatti ◽  
SM Ahmad ◽  
Nisar Ahmed
Keyword(s):  
External Disturbance ◽  
Parameter Variation ◽  
Control Technique ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Linear Parameter ◽  
Dynamic Coupling ◽  
Linear Parameter Varying ◽  
Linear Parameter Varying Control ◽  
Parameter Varying

The control performance of aerial vehicles can be easily affected by measurement error in sensor output, dynamic model error, model parameter variation, parametric uncertainty, external disturbance, and dynamic coupling. This article presents a design of robust linear parameter-varying control technique with induced L2-norm performance combined with linear matrix inequality pole region constraints for a lab-scale helicopter. A linear parameter-varying disturbance rejection observer is constructed that characterizes the L2-norm performance of the linear parameter-varying system, which enables to estimate state information not only in the presence of external disturbance but also in case of fault occurrence or unavailability of some sensor output. Therefore, the proposed robust linear parameter-varying control scheme has the tendency to provide an adaptive control solution for stability proof and robust tracking performance. The performance of the proposed technique is confirmed both in simulation and in real time. Compared to conventional output feedback H∞ control technique, the proposed control technique yields a good tracking performance in the presence of disturbance, parameter variation, and dynamic coupling.

Linear Parameter-Varying Modeling of Electric Vehicle Air Conditioning System

2011 ◽  
Vol 148-149 ◽  
pp. 318-325
Author(s):  
Xiao Ming Wang ◽  
Alois Steiner ◽  
Jan Fiala
Keyword(s):  
Electric Vehicle ◽  
Air Conditioning ◽  
Performance Estimation ◽  
Coefficient Of Performance ◽  
Model Structure ◽  
Linear Parameter ◽  
Air Conditioning System ◽  
Linear Parameter Varying ◽  
Non Linear ◽  
Parameter Varying

This article presents the approach of quasi LPV (Linear Parameter-Varying) modeling techniques for an air conditioning system of an electric vehicle. Vehicle air conditioning systems are strongly non-linear systems and it is a challenging task to get a precise real time model for control purposes. Therefore, an LPV method is first introduced to estimate the air conditioning system. Experimental results show that the LPV model delivers a very high accuracy for the COP (Coefficient Of Performance) estimation, that can’t be reached by traditional identification methods. Some discussion about the model structure and its application are presented and a non-linear LPV model structure similar to the Hammerstein structure is proposed.

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