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Journal articles on the topic 'Quasi-LPV'

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Published: 1 June 2024

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1

Medero, Ariel, and Vicenç Puig. "LPV Control and Virtual-Sensor-Based Fault Tolerant Strategies for a Three-Axis Gimbal System." Sensors 22, no. 17 (September 3, 2022): 6664. http://dx.doi.org/10.3390/s22176664.

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This paper deals with the LPV control of a three-axis gimbal including fault-tolerant capabilities. First, the derivation of an analytical model for the considered system based on the robotics Serial-Link (SL) theory is derived. Then, a series of simplifications that allow obtaining a quasi-LPV model for the considered gimbal is proposed. Gain scheduling LPV controllers with PID structure are designed using pole placement by means of linear matrix inequalities (LMIs). Moreover, exploiting the sensor redundancy available in the gimbal, a virtual-sensor-based fault tolerant control (FTC) strategy is proposed. This virtual sensor uses a Recursive Least Square (RLS) estimation algorithm and an LPV observer for fault detection and estimation. Finally, the proposed LPV control scheme including the virtual sensor strategy is tested in simulation in several scenarios.

2

Wang, Xiao Ming, Alois Steiner, and Jan Fiala. "Linear Parameter-Varying Modeling of Electric Vehicle Air Conditioning System." Applied Mechanics and Materials 148-149 (December 2011): 318–25. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.318.

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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.

3

Sala, Antonio. "Generalising quasi-LPV and CDI models to Quasi-Convex Difference Inclusions." IFAC-PapersOnLine 50, no. 1 (July 2017): 7560–65. http://dx.doi.org/10.1016/j.ifacol.2017.08.1192.

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4

Pérez-Estrada, A. J., G. L. Osorio-Gordillo, M. Darouach, and V. H. Olivares-Peregrino. "Generalized dynamic observer design for quasi-LPV systems." at - Automatisierungstechnik 66, no. 3 (March 26, 2018): 225–33. http://dx.doi.org/10.1515/auto-2017-0060.

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Abstract This paper presents a new generalized dynamic observer (GDO) for quasi-linear parameter varying (LPV) systems. It generalises the structures of the proportional observer (PO) and proportional integral observer (PIO). The design of the GDO is derived from the solution of linear matrix inequalities (LMIs) and the solution of the algebraic constraints obtained from the estimation error analysis. The efficiency of the proposed approach is illustrated by a numerical example.

5

Lu, Yaohui, and Yaman Arkun. "Quasi-Min-Max MPC algorithms for LPV systems." Automatica 36, no. 4 (April 2000): 527–40. http://dx.doi.org/10.1016/s0005-1098(99)00176-4.

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6

Huang, Yun, and Ali Jadbabaie. "Nonlinear H∞ control: an enhanced quasi-LPV approach." IFAC Proceedings Volumes 32, no. 2 (July 1999): 2754–59. http://dx.doi.org/10.1016/s1474-6670(17)56469-1.

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7

Grimble, M. J., Pawel Majecki, and M. R. Katebi. "Extended NGMV Predictive Control of Quasi-LPV Systems." IFAC-PapersOnLine 50, no. 1 (July 2017): 4101–7. http://dx.doi.org/10.1016/j.ifacol.2017.08.795.

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8

Robles, Ruben, Antonio Sala, and Miguel Bernal. "Performance-oriented quasi-LPV modeling of nonlinear systems." International Journal of Robust and Nonlinear Control 29, no. 5 (December 21, 2018): 1230–48. http://dx.doi.org/10.1002/rnc.4444.

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9

He, ZeFang, and Long Zhao. "Quadrotor Trajectory Tracking Based on Quasi-LPV System and Internal Model Control." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/857291.

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Internal model control (IMC) design method based on quasi-LPV (Linear Parameter Varying) system is proposed. In this method, the nonlinear model is firstly transformed to the linear model based on quasi-LPV method; then, the quadrotor nonlinear motion function is transformed to transfer function matrix based on the transformation model from the state space to the transfer function; further, IMC is designed to control the controlled object represented by transfer function matrix and realize quadrotor trajectory tracking. The performance of the controller proposed in this paper is tested by tracking for three reference trajectories with drastic changes. The simulation results indicate that the control method proposed in this paper has stronger robustness to parameters uncertainty and disturbance rejection performance.

10

Polat, İ., İ. E. Köse, and E. Eşkinat. "Dynamic output feedback control of quasi-LPV mechanical systems." IET Control Theory & Applications 1, no. 4 (July 1, 2007): 1114–21. http://dx.doi.org/10.1049/iet-cta:20060326.

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11

White, B. A., L. Bruyere, and A. Tsourdos. "Missle autopilot design using quasi-LPV polynomial eigenstructure assignment." IEEE Transactions on Aerospace and Electronic Systems 43, no. 99 (2007): 1470–83. http://dx.doi.org/10.1109/taes.2007.4407471.

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12

White, B. A., L. Bruyere, and A. Tsourdos. "Missile autopilot design using quasi-LPV polynomial eigenstructure assignment." IEEE Transactions on Aerospace and Electronic Systems 43, no. 4 (October 2007): 1470–83. http://dx.doi.org/10.1109/taes.2007.4441752.

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13

Loiseau, Paul, Chaouki Nacer Eddine Boultifat, Philippe Chevrel, Fabien Claveau, Stéphane Espié, and Franck Mars. "Rider model identification: neural networks and quasi-LPV models." IET Intelligent Transport Systems 14, no. 10 (October 1, 2020): 1259–64. http://dx.doi.org/10.1049/iet-its.2020.0088.

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14

Machala, Dawid, Simona Dobre, Marie Albisser, Floriane Collin, and Marion Gilson. "Quasi-LPV modelling of a projectile’s behaviour in flight." IFAC-PapersOnLine 51, no. 15 (2018): 1080–85. http://dx.doi.org/10.1016/j.ifacol.2018.09.050.

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15

Goyal, Jitendra Kumar, Shubham Aggarwal, Sandip Ghosh, Shyam Kamal, and Pawel Dworak. "Quasi‐LPV PI control of TRMS subject to actuator saturation." IET Control Theory & Applications 14, no. 19 (December 2020): 3157–67. http://dx.doi.org/10.1049/iet-cta.2020.0361.

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16

Coutinho, Pedro H. S., Márcia L. C. Peixoto, Miguel Bernal, Anh-Tu Nguyen, and Reinaldo M. Palhares. "Local Sampled-Data Gain-Scheduling Control of quasi-LPV Systems." IFAC-PapersOnLine 54, no. 4 (2021): 86–91. http://dx.doi.org/10.1016/j.ifacol.2021.10.015.

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17

YAMAMOTO, Shunya, and Nobutaka WADA. "Tracking control of a robot manipulator by quasi-LPV NMPC." Proceedings of Conference of Chugoku-Shikoku Branch 2020.58 (2020): 09c7. http://dx.doi.org/10.1299/jsmecs.2020.58.09c7.

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18

Huang, Hua, De Feng He, and Qiu Xia Chen. "Quasi-min-max dynamic output feedback MPC for LPV systems." International Journal of System Control and Information Processing 1, no. 3 (2014): 233. http://dx.doi.org/10.1504/ijscip.2014.059675.

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19

Bendtsen, J. D., and K. Trangbaek. "Robust quasi-LPV control based on neural state-space models." IEEE Transactions on Neural Networks 13, no. 2 (March 2002): 355–68. http://dx.doi.org/10.1109/72.991421.

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20

Arezki, H., A. Alessandri, and A. Zemouche. "Robust Moving-Horizon Estimation for Quasi-LPV Discrete-Time Systems*." IFAC-PapersOnLine 56, no. 2 (2023): 6771–76. http://dx.doi.org/10.1016/j.ifacol.2023.10.384.

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21

Oehlschlägel, Thimo, Christian Heise, Stephan Theil, and Thomas Steffen. "Stability Analysis of Closed Loops of non-linear Systems and LPV Controllers designed using approximated Quasi-LPV Systems." IFAC Proceedings Volumes 45, no. 13 (2012): 349–54. http://dx.doi.org/10.3182/20120620-3-dk-2025.00165.

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22

Cisneros, Pablo S. G., Adwait Datar, Patrick Göttsch, and Herbert Werner. "Data-Driven quasi-LPV Model Predictive Control Using Koopman Operator Techniques." IFAC-PapersOnLine 53, no. 2 (2020): 6062–68. http://dx.doi.org/10.1016/j.ifacol.2020.12.1676.

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23

Cisneros, Pablo S. G., and Herbert Werner. "Wide Range Stabilization of a Pendubot using quasi-LPV Predictive Control." IFAC-PapersOnLine 52, no. 28 (2019): 164–69. http://dx.doi.org/10.1016/j.ifacol.2019.12.367.

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24

Su, Yang, Kok Kiong Tan, and Tong Heng Lee. "Tube based Quasi-min-max Output Feedback MPC for LPV Systems1." IFAC Proceedings Volumes 45, no. 15 (2012): 186–91. http://dx.doi.org/10.3182/20120710-4-sg-2026.00010.

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25

Ding, Baocang, Xubin Ping, and Hongguang Pan. "On dynamic output feedback robust MPC for constrained quasi-LPV systems." International Journal of Control 86, no. 12 (December 2013): 2215–27. http://dx.doi.org/10.1080/00207179.2013.809796.

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26

Cisneros, Pablo S. G., Aadithyan Sridharan, and Herbert Werner. "Constrained Predictive Control of a Robotic Manipulator using quasi-LPV Representations." IFAC-PapersOnLine 51, no. 26 (2018): 118–23. http://dx.doi.org/10.1016/j.ifacol.2018.11.158.

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27

Pérez-Estrada, A. J., G. L. Osorio-Gordillo, M. Darouach, M. Alma, and V. H. Olivares-Peregrino. "Generalized dynamic observers for quasi-LPV systems with unmeasurable scheduling functions." International Journal of Robust and Nonlinear Control 28, no. 17 (August 27, 2018): 5262–78. http://dx.doi.org/10.1002/rnc.4309.

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28

Ping, Xubin, and Ning Sun. "Dynamic Output Feedback Robust Model Predictive Control via Zonotopic Set-Membership Estimation for Constrained Quasi-LPV Systems." Journal of Applied Mathematics 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/875850.

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For the quasi-linear parameter varying (quasi-LPV) system with bounded disturbance, a synthesis approach of dynamic output feedback robust model predictive control (OFRMPC) is investigated. The estimation error set is represented by a zonotope and refreshed by the zonotopic set-membership estimation method. By properly refreshing the estimation error set online, the bounds of true state at the next sampling time can be obtained. Furthermore, the feasibility of the main optimization problem at the next sampling time can be determined at the current time. A numerical example is given to illustrate the effectiveness of the approach.

29

Degtyarev, G. L., R. N. Faizutdinov, and I. O. Spiridonov. "Multiobjective Robust Controller Synthesis for Nonlinear Mechanical System." Mekhatronika, Avtomatizatsiya, Upravlenie 19, no. 11 (November 8, 2018): 691–98. http://dx.doi.org/10.17587/mau.19.691-698.

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In the paper multiobjective robust controller synthesis problem for nonlinear mechanical system described by Lagrange’s equations of the second kind is considered. Such tasks have numerous practical applications, for example in controller design of robotic systems and gyro-stabilized platforms. In practice, we often have to use uncertain mathematical plant models in controller design. Therefore, ensuring robustness in presence of parameters perturbations and unknown external disturbances is an important requirement for designed systems. Much of modern robust control theory is linear. When the actual system exhibits nonlinear behavior, nonlinearities are usually included in the uncertainty set of the plant. A disadvantage of this approach is that resulting controllers may be too conservative especially when nonlinearities are significant. The nonlinear H∞ optimal control theory developed on the basis of differential game theory is a natural extension of the linear robust control theory. Nonlinear theory methods ensure robust stability of designed control systems. However, to determine nonlinear H∞-control law, the partial differential equation have to be solved which is a rather complicated task. In addition, it is difficult to ensure robust performance of controlled processes when using this method. In this paper, methods of linear parameter-varying (LPV) systems are used to synthesize robust control law. It is shown, that Lagrange system may be adequately represented in the form of quasi-LPV model. From the computational point of view, the synthesis procedure is reduced to convex optimization techniques under constraints expressed in the form of linear matrix inequalities (LMIs). Measured parameters are incorporated in the control law, thus ensuring continuous adjustment of the controller parameters to the current plant dynamics and better performance of control processes in comparison with H∞-regulators. Furthermore, the use of the LMIs allows to take into account the transient performance requirements in the controller synthesis. Since the quasi-LPV system depends continuously on the parameter vector, the LMI system is infinite-dimensional. This infinitedimensional system is reduced to a finite set of LMIs by introducing a polytopic LPV representation. The example of multiobjective robust control synthesis for electro-optical device’s line of sight pointing and stabilization system suspended in two-axes inertially stabilized platform is given.

30

Grimble, Mike J., and Pawel Majecki. "Observer Based Restricted Structure Generalized Predictive Control for quasi-LPV Nonlinear Systems." IFAC-PapersOnLine 53, no. 2 (2020): 4264–71. http://dx.doi.org/10.1016/j.ifacol.2020.12.2480.

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31

Alessandri, A., M. Zasadzinski, and A. Zemouche. "Optimistic vs Pessimistic Moving-Horizon Estimation for Quasi–LPV Discrete-Time Systems." IFAC-PapersOnLine 53, no. 2 (2020): 5004–9. http://dx.doi.org/10.1016/j.ifacol.2020.12.1096.

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32

Meng Zhao, Canchen Jiang, Xiaoming Tang, and Minghong She. "Interpolation Model Predictive Control of Nonlinear Systems Described by Quasi-LPV Model." Automatic Control and Computer Sciences 52, no. 5 (September 2018): 354–64. http://dx.doi.org/10.3103/s0146411618050085.

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33

Coutinho, Pedro H. S., Márcia L. C. Peixoto, Iury Bessa, and Reinaldo Martinez Palhares. "Dynamic event-triggered gain-scheduling control of discrete-time quasi-LPV systems." Automatica 141 (July 2022): 110292. http://dx.doi.org/10.1016/j.automatica.2022.110292.

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34

Rotondo, Damiano, Vicenç Puig, Fatiha Nejjari, and Marcin Witczak. "Automated generation and comparison of Takagi–Sugeno and polytopic quasi-LPV models." Fuzzy Sets and Systems 277 (October 2015): 44–64. http://dx.doi.org/10.1016/j.fss.2015.02.002.

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35

Rotondo, Damiano, Fatiha Nejjari, and Vicenç Puig. "Quasi-LPV modeling, identification and control of a twin rotor MIMO system." Control Engineering Practice 21, no. 6 (June 2013): 829–46. http://dx.doi.org/10.1016/j.conengprac.2013.02.004.

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36

Rotondo, Damiano, Fatiha Nejjari, and Vicenç Puig. "Model Reference Switching Quasi-LPV Control of a Four Wheeled Omnidirectional Robot." IFAC Proceedings Volumes 47, no. 3 (2014): 4062–67. http://dx.doi.org/10.3182/20140824-6-za-1003.00054.

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37

Vinco, G. M., S. Theodoulis, O. Sename, and G. Strub. "Quasi-LPV Modeling of Guided Projectile Pitch Dynamics through State Transformation Technique." IFAC-PapersOnLine 55, no. 35 (2022): 43–48. http://dx.doi.org/10.1016/j.ifacol.2022.11.288.

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38

Dehak, Amine, Anh-Tu Nguyen, Antoine Dequidt, Laurent Vermeiren, and Michel Dambrine. "A Reduced-Complexity Polytopic Control Approach for Uncertain quasi-LPV Descriptor Systems." IFAC-PapersOnLine 56, no. 2 (2023): 2176–81. http://dx.doi.org/10.1016/j.ifacol.2023.10.1124.

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39

López-Estrada, Francisco-Ronay, Damiano Rotondo, and Guillermo Valencia-Palomo. "A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems." Processes 7, no. 11 (November 4, 2019): 814. http://dx.doi.org/10.3390/pr7110814.

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This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).

40

Zhao, Min, and Ping Ping Song. "Quasi-Min-Max MPC for Nonlinear System via Embedding Approach." Advanced Materials Research 320 (August 2011): 481–86. http://dx.doi.org/10.4028/www.scientific.net/amr.320.481.

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A quasi-min-max model predictive control (MPC) algorithm is proposed for constrained nonlinear system via an embedding approach. The nonlinear system can be approximated by a linear parameter varying (LPV) model. And a method based on invariant set is proposed for the embedding model to reduce the computational complexity. The proposed method constructs a one-step invariant set comprises an interpolation between several pre-computed invariant sets at each time instant. Then control law is obtained by solving a constrained QP problem, which is also useful for the nonlinear system. The performances of the approach are presented via an example.

41

Coutinho, Pedro Henrique Silva, and Reinaldo Martínez Palhares. "Dynamic periodic event-triggered gain-scheduling control co-design for quasi-LPV systems." Nonlinear Analysis: Hybrid Systems 41 (August 2021): 101044. http://dx.doi.org/10.1016/j.nahs.2021.101044.

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42

Fouka, Majda, Chouki Sentouh, and Jean-Christophe Popieul. "Quasi-LPV Interconnected Observer Design for Full Vehicle Dynamics Estimation With Hardware Experiments." IEEE/ASME Transactions on Mechatronics 26, no. 4 (August 2021): 1763–72. http://dx.doi.org/10.1109/tmech.2021.3074743.

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43

Rahmanian, Farnoosh, and Mohammad Hassan Asemani. "Quasi-LPV positive observer-based control of closed-loop deep brain stimulation systems." Biomedical Signal Processing and Control 86 (September 2023): 105238. http://dx.doi.org/10.1016/j.bspc.2023.105238.

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44

Cisneros, Pablo Gonzalez, and Herbert Werner. "Fast Nonlinear MPC for Reference Tracking Subject to Nonlinear Constraints via Quasi-LPV Representations." IFAC-PapersOnLine 50, no. 1 (July 2017): 11601–6. http://dx.doi.org/10.1016/j.ifacol.2017.08.1650.

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45

Rotondo, Damiano, Fatiha Nejjari, and Vicenç Puig. "Robust Quasi–LPV Model Reference FTC of a Quadrotor Uav Subject to Actuator Faults." International Journal of Applied Mathematics and Computer Science 25, no. 1 (March 1, 2015): 7–22. http://dx.doi.org/10.1515/amcs-2015-0001.

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Abstract A solution for fault tolerant control (FTC) of a quadrotor unmanned aerial vehicle (UAV) is proposed. It relies on model reference-based control, where a reference model generates the desired trajectory. Depending on the type of reference model used for generating the reference trajectory, and on the assumptions about the availability and uncertainty of fault estimation, different error models are obtained. These error models are suitable for passive FTC, active FTC and hybrid FTC, the latter being able to merge the benefits of active and passive FTC while reducing their respective drawbacks. The controller is generated using results from the robust linear parameter varying (LPV) polytopic framework, where the vector of varying parameters is used to schedule between uncertain linear time invariant (LTI) systems. The design procedure relies on solving a set of linear matrix inequalities (LMIs) in order to achieve regional pole placement and H∞ norm bounding constraints. Simulation results are used to compare the different FTC strategies.

46

Kim, Tae-Hyoung, and Ho-Woon Lee. "Quasi-min-max output-feedback model predictive control for LPV systems with input saturation." International Journal of Control, Automation and Systems 15, no. 3 (May 22, 2017): 1069–76. http://dx.doi.org/10.1007/s12555-016-0378-y.

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47

Bianchi, F. D., R. J. Mantz, and C. F. Christiansen. "Gain scheduling control of variable-speed wind energy conversion systems using quasi-LPV models." Control Engineering Practice 13, no. 2 (February 2005): 247–55. http://dx.doi.org/10.1016/j.conengprac.2004.03.006.

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48

Park, Jee-Hun, Tae-Hyoung Kim, and Toshiharu Sugie. "Output feedback model predictive control for LPV systems based on quasi-min–max algorithm." Automatica 47, no. 9 (September 2011): 2052–58. http://dx.doi.org/10.1016/j.automatica.2011.06.015.

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49

Cherifi, Abdelmadjid, Kevin Guelton, and Laurent Arcese. "Quadratic Design of D-stabilizing Non-PDC Controllers for Quasi-LPV/T-S Models." IFAC-PapersOnLine 48, no. 26 (2015): 164–69. http://dx.doi.org/10.1016/j.ifacol.2015.11.131.

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50

Mattei, Massimiliano. "An LPV approach to the robust control of a class of quasi-linear propagation processes." Journal of Process Control 14, no. 6 (September 2004): 651–60. http://dx.doi.org/10.1016/j.jprocont.2004.01.001.

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