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.

The Research of Air Precooling System Based on Generalize Predictive Control Strategy

2013 ◽  
Vol 433-435 ◽  
pp. 1091-1098
Author(s):  
Wei Bo Yu ◽  
Cui Yuan Feng ◽  
Ting Ting Yang ◽  
Hong Jun Li
Keyword(s):  
Predictive Control ◽  
Control Strategy ◽  
Control Algorithm ◽  
Exchange Process ◽  
Generalized Predictive Control ◽  
Matlab Simulation ◽  
Control Effect ◽  
Heat Exchange Process ◽  
Complex Control System ◽  
And Control

The air precooling system heat exchange process is a complex control system with features such as: nonlinear, lag and random interference. So choose Generalized Predictive Control Algorithm that has low model dependence, good robustness and control effect, as well as easy to implement. But due to the large amount of calculation of traditional generalized predictive control and can't juggle quickness and overshoot problem, an improved generalized predictive control algorithm is proposed, then carry out the MATLAB simulation, the experimental results show that the algorithm can not only greatly reduce the amount of computation, but also can restrain the overshoot and its rapidity.

A robustification of the two degree-of-freedom controller based upon multivariable generalized predictive control law and robust H∞ control for a doubly-fed induction generator

2016 ◽  
Vol 40 (3) ◽  
pp. 1005-1017 ◽  
Author(s):  
Mohammed Aidoud ◽  
Moussa Sedraoui ◽  
Abderrazek Lachouri ◽  
Abdelhalim Boualleg
Keyword(s):  
Wind Turbine ◽  
Predictive Control ◽  
Degree Of Freedom ◽  
Induction Generator ◽  
Generalized Predictive Control ◽  
Doubly Fed Induction Generator ◽  
Wind Turbine System ◽  
Youla Parameterization ◽  
Doubly Fed ◽  
Two Degree Of Freedom

A robustification method of primary two degree-of-freedom (2-DOF) controllers is proposed in this paper to control the wind turbine system equipped with a doubly-fed induction generator DFIG. The proposed robustification method should follow the following three step-procedures. First, the primary 2-DOF controller is designed through the initial form of the multivariable generalized predictive control MGPC law to ensure a good tracking dynamic of reference trajectories. Second, the robust [Formula: see text] controller is independently designed for the previous system to ensure good robustness properties of the closed-loop system against model uncertainties, neglecting dynamics and sensor noises. Finally, both above mentioned controllers are combined to design the robustified 2-DOF-MGPC controller using Youla parameterization method. Therefore, the obtained controller conserves the same good tracking dynamic that is provided by the primary 2-DOF-MGPC controller. It ensures the same good robustness properties which are produced by the robust [Formula: see text] controller. A wind turbine system equipped with a DFIG is controlled by the robustified 2-DOF-MGPC controller. Its dynamic behaviour is modelled by an unstructured-output multiplicative uncertainty plant. The controller performances are valid by comparison with those given through both controllers, which are primary 2-DOF-MGPC and robust [Formula: see text] controllers in time and frequency domains.

Field oriented predictive control strategy for induction machine drives

2017 ◽  
Vol 37 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Xin Qi ◽  
Lin Wu ◽  
Xiaomin Zhou ◽  
Xianghua Ma
Keyword(s):  
Predictive Control ◽  
Control Strategy ◽  
Induction Machine ◽  
Sampling Frequency ◽  
Current Control ◽  
Switching Frequency ◽  
Frequency Noise ◽  
Boundary Circle ◽  
Content Type ◽  
Simulation Step

Purpose This study aims to drive the induction machine system with a low switching frequency. Design/methodology/approach An unconventional inverter control strategy – field-oriented predictive control (FOPC) – is presented. The strategy limits current distortion by setting a boundary circle. The voltage vector, which could keep current trajectories in boundary, is selected to obtain a low switching frequency. Findings A dual simulation step technique is developed to investigate the influence of sampling frequency on current distortion control and switching frequency. Current control distortion can be improved, i.e. reduced, by increasing the sampling frequency; however, the switching frequency will also increase. Such a law is discovered by the dual simulation step technique and finally verified by experiments. Originality/value A new predictive control method, FOPC, is derived from the rotor filed coordinate machine model and presented in this paper. FOPC circumvents derivative calculations, and thus avoids high-frequency noise amplification.

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.

scholarly journals A Deep-Sea Pipeline Skin Effect Electric Heat Tracing System

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2466 ◽  
Author(s):  
Ding ◽  
Zhang ◽  
Lin
Keyword(s):  
Predictive Control ◽  
Control Strategy ◽  
Control Algorithm ◽  
Skin Effect ◽  
Distribution Model ◽  
Temperature Control System ◽  
Generalized Predictive Control ◽  
Oil Pipeline ◽  
Power Frequency ◽  
Electric Heat

In order to ensure deep-water flowline safety, this paper combined the axial temperature distribution model of the submarine pipeline and the distributed parameter circuit model of the skin effect electric heat tracing system; such work is conducive to proving that the heating effect of the skin effect electric heat tracing system depends on the distributed circuit parameters and power frequency of the system. Due to the complexity of the power supply device, the frequency cannot be increased indefinitely. Therefore, for the case that the input of the skin electric heat tracing system is constrained, a generalized predictive control algorithm introducing the input softening factor is proposed, and the constrained generalized predictive control strategy is applied to the electric heating temperature control system of the submarine oil pipeline. Simulation results demonstrated that the control quantity of the skin effect electric heat tracing system is effectively controlled within a constraint range, and also the values of heating power and power frequency are obtained by theoretical calculations rather than empirical estimations. Moreover, compared with the conventional control algorithm, the proposed constrained generalized predictive algorithm unfolds more significant dynamic response and better adaptive adjustment ability, which verifies the feasibility of the proposed control strategy.

scholarly journals Least Squares Support Vector Machine-Based Multivariate Generalized Predictive Control for Parabolic Distributed Parameter Systems with Control Constraints

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 453
Author(s):  
Ling Ai ◽  
Yang Xu ◽  
Liwei Deng ◽  
Kok Lay Teo
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Predictive Control ◽  
Control Strategy ◽  
Distributed Parameter Systems ◽  
Diffusion Reaction ◽  
Support Vector ◽  
Distributed Parameter ◽  
Generalized Predictive Control ◽  
Parabolic Distributed Parameter Systems

This manuscript addresses a new multivariate generalized predictive control strategy using the least squares support vector machine for parabolic distributed parameter systems. First, a set of proper orthogonal decomposition-based spatial basis functions constructed from a carefully selected set of data is used in a Galerkin projection for the building of an approximate low-dimensional lumped parameter systems. Then, the temporal autoregressive exogenous model obtained by the least squares support vector machine is applied in the design of a multivariate generalized predictive control strategy. Finally, the effectiveness of the proposed multivariate generalized predictive control strategy is verified through a numerical simulation study on a typical diffusion-reaction process in radical symmetry.

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