A new control strategy for induction motor based on non-linear predictive control and feedback linearization

2000 ◽  
Vol 14 (2-3) ◽  
pp. 313-329 ◽  
Author(s):  
M. K. Maaziz ◽  
P. Boucher ◽  
D. Dumur
Keyword(s):  
Induction Motor ◽  
Predictive Control ◽  
Control Strategy ◽  
Feedback Linearization ◽  
Non Linear

MPC Based Feedback-Linearization Strategy of a Fixed-Wing UAV

2020 ◽  
Author(s):  
Leonardo A. A. Pereira ◽  
Luciano C. A. Pimenta ◽  
Guilherme V. Raffo
Keyword(s):  
Predictive Control ◽  
Control Strategy ◽  
Feedback Linearization ◽  
Degrees Of Freedom ◽  
Vehicle Control ◽  
Control Allocation ◽  
Linearization Technique ◽  
Aerial Vehicle ◽  
The Relationship ◽  
Curve Tracking

This work proposes a xed-wing UAV (Ummaned Aerial Vehicle) control strategy based on feedback-linearization and model predictive control (MPC). The strategy makes use of the relationship between the applied control inputs of the UAV and the generalized forces and moments actuating on it. A linear model is obtained by the exact feedback-linearization technique, followed by the use of MPC to solve the trajectory tracking and the control allocation problems. The proposed controller is capable of actuating on the 6 DOF (Degrees of Freedom) of the UAV, avoiding inherited restrictions when the model is decoupled. The proposed strategy is applied in a curve tracking task. Simulations are performed using MATLAB software, and the results show the eciency of the proposed control strategy.

Robust generalized predictive control of the Orthoglide robot

2014 ◽  
Vol 41 (3) ◽  
pp. 275-285 ◽  
Author(s):  
Fabian Andres Lara-Molina ◽  
João Maurício Rosário ◽  
Didier Dumur ◽  
Philippe Wenger
Keyword(s):  
Computational Complexity ◽  
Predictive Control ◽  
Control Strategy ◽  
Feedback Linearization ◽  
Generalized Predictive Control ◽  
Robot Dynamics ◽  
Tracking Accuracy ◽  
Content Type ◽  
Control Loops ◽  
Youla Parameterization

Purpose – The purpose of this paper is to address the synthesis and experimental application of a generalized predictive control (GPC) technique on an Orthoglide robot. Design/methodology/approach – The control strategy is composed of two control loops. The inner loop aims at linearizing the nonlinear robot dynamics using feedback linearization. The outer loop tracks the desired trajectory based on GPC strategy, which is robustified against measurement noise and neglected dynamics using Youla parameterization. Findings – The experimental results show the benefits of the robustified predictive control strategy on the dynamical performance of the Orthoglide robot in terms of tracking accuracy, disturbance rejection, attenuation of noise acting on the control signal and parameter variation without increasing the computational complexity. Originality/value – The paper shows the implementation of the robustified predictive control strategy in real time with low computational complexity on the Orthoglide robot.

A new robust non-linear self-tuning predictive control using neural networks

1997 ◽  
Vol 211 (6) ◽  
pp. 439-446 ◽  
Author(s):  
K Y Zhu ◽  
X F Qin ◽  
T Y Chai
Keyword(s):  
Predictive Control ◽  
Feedback Linearization ◽  
Control Algorithm ◽  
Linear Part ◽  
Taylor Series Expansion ◽  
Recursive Least Squares ◽  
Non Linear ◽  
Self Tuning ◽  
Non Linear System ◽  
Robust Predictive Control

An adaptive version of a novel robust predictive control for a class of non-linear systems is presented. The non-linear system is separated into linear and non-linear parts by Taylor series expansion and then the latter part is identified by a neural network, which is then compensated in the control algorithm such that feedback linearization can be achieved. Thus the influence of the non-linearity and model uncertainties may be eliminated or reduced. In the case of time-varying or unknown systems the linear part of the system model is estimated by an RLS (recursive least-squares) algorithm. Simulation results show that the proposed scheme may improve the system performance.

scholarly journals Performance enhancement in a multilevel inverter fed PTC induction motor drive by optimal voltage vector selection

2019 ◽  
Vol 10 (2) ◽  
pp. 801
Author(s):  
Himabindu T ◽  
A.V. Ravi Teja ◽  
G Bhuvaneswari ◽  
Bhim Singh
Keyword(s):  
Induction Motor ◽  
Predictive Control ◽  
Control Strategy ◽  
Performance Enhancement ◽  
Torque Control ◽  
Multilevel Inverter ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Switching Losses ◽  
Voltage Vector

<p>This paper proposes a novel predictive control strategy for a multilevel inverter fed Induction Motor Drive (IMD) with optimal voltage vector selection at every sampling interval. The proposed predictive control strategy, apart from enhancing the dynamic speed and torque responses of the drive, strives to reduce the number of switching transitions by choosing optimal voltage vector, thereby reducing the switching losses significantly. The algorithm put forth here chooses the most suitable switching state among the redundant switching combinations, such that minimum number of switches change their states from the previous switching combination to the present one. This results in perceptible reduction in the switching losses thereby increasing the efficiency of the converter. The proposed Predictive Torque Control (PTC) strategy is modeled and simulated in Matlab/ Simulink environment and the results are reported for a 2-level and 3-level inverter fed IMD configurations. The results demonstrate the effectiveness of the proposed PTC for both 2-level and 3-level inverter fed IMDs.</p>

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