Academic literature on the topic 'Differential Flatness-Based Control'
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Journal articles on the topic "Differential Flatness-Based Control":
Rigatos, Gerasimos G. "Differential flatness theory-based control and filtering for a mobile manipulator." Cybernetics and Physics, Volume 9, 2020, Number 1 (June 30, 2020): 57–68. http://dx.doi.org/10.35470/2226-4116-2020-9-1-57-68.
Hagenmeyer, Veit, and Emmanuel Delaleau. "Exact feedforward linearization based on differential flatness." International Journal of Control 76, no. 6 (January 2003): 537–56. http://dx.doi.org/10.1080/0020717031000089570.
Lu, Wen-Chi, Lili Duan, Fei-Bin Hsiao, and Félix Mora-Camino. "Neural Guidance Control for Aircraft Based on Differential Flatness." Journal of Guidance, Control, and Dynamics 31, no. 4 (July 2008): 892–98. http://dx.doi.org/10.2514/1.33276.
Liang, Dingkun, Ning Sun, Yiming Wu, and Yongchun Fang. "Differential Flatness-Based Robust Control of Self-balanced Robots." IFAC-PapersOnLine 51, no. 31 (2018): 949–54. http://dx.doi.org/10.1016/j.ifacol.2018.10.058.
An, Ningbo, Qishao Wang, Xiaochuan Zhao, and Qingyun Wang. "Differential flatness-based distributed control of underactuated robot swarms." Applied Mathematics and Mechanics 44, no. 10 (September 30, 2023): 1777–90. http://dx.doi.org/10.1007/s10483-023-3040-8.
Elango, P., and R. Mohan. "Trajectory optimisation of six degree of freedom aircraft using differential flatness." Aeronautical Journal 122, no. 1257 (November 2018): 1788–810. http://dx.doi.org/10.1017/aer.2018.99.
Silva-Ortigoza, Ramón, Magdalena Marciano-Melchor, Rogelio Ernesto García-Chávez, Alfredo Roldán-Caballero, Victor Manuel Hernández-Guzmán, Eduardo Hernández-Márquez, José Rafael García-Sánchez, Rocío García-Cortés, and Gilberto Silva-Ortigoza. "Robust Flatness-Based Tracking Control for a “Full-Bridge Buck Inverter–DC Motor” System." Mathematics 10, no. 21 (November 4, 2022): 4110. http://dx.doi.org/10.3390/math10214110.
Mounier, Hugues, Silviu-Iulian Niculescu, Arben Cela, and Marcel Stefan Geamanu. "Flatness-based longitudinal vehicle control with embedded torque constraint." IMA Journal of Mathematical Control and Information 36, no. 3 (September 6, 2018): 729–44. http://dx.doi.org/10.1093/imamci/dny005.
Mahadevan, Radhakrishnan, Sunil K. Agrawal, and Francis J. Doyle III. "Differential flatness based nonlinear predictive control of fed-batch bioreactors." Control Engineering Practice 9, no. 8 (August 2001): 889–99. http://dx.doi.org/10.1016/s0967-0661(01)00054-5.
Rauniyar, Shyam, Sameer Bhalla, Daegyun Choi, and Donghoon Kim. "EKF-SLAM for Quadcopter Using Differential Flatness-Based LQR Control." Electronics 12, no. 5 (February 24, 2023): 1113. http://dx.doi.org/10.3390/electronics12051113.
Dissertations / Theses on the topic "Differential Flatness-Based Control":
Hermosillo, Valadez Jorge. "Motion planning & feedback control of bi-steerable robots : an approach based on differential flatness." Grenoble INPG, 2003. http://www.theses.fr/2003INPG0044.
Bekcheva, Maria. "Flatness-based constrained control and model-free control applications to quadrotors and cloud computing." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS218.
The first part of the thesis is devoted to the control of differentially flat systems with constraints. Two types of systems are studied: non-linear finite dimensional systems and linear time-delay systems. We present an approach to embed the input/state/output constraints in a unified manner into the trajectory design for differentially flat systems. To that purpose, we specialize the flat outputs (or the reference trajectories) as Bézier curves. Using the flatness property, the system’s inputs/states can be expressed as a combination of Bézier curved flat outputs and their derivatives. Consequently, we explicitly obtain the expressions of the control points of the inputs/states Bézier curves as a combination of the control points of the flat outputs. By applying desired constraints to the latter control points, we find the feasible regions for the output Bézier control points i.e. a set of feasible reference trajectories. This framework avoids the use of generally high computing cost optimization schemes. To resolve the uncertainties arising from imprecise model identification and the unknown pertubations, we employ the Model-Free Control (MFC) and in the second part of the thesis we present two applications demonstrating the effectiveness of our approach: 1. We propose a controller design that avoids the quadrotor’s system identification procedures while staying robust with respect to the endogenous (the control performance is independent of any mass change, inertia, gyroscopic or aerodynamic effects) and exogenous disturbances (wind, measurement noise). To reach our goal, based on the cascaded structure of a quadrotor, we divide the system into positional and attitude subsystems each controlled by an independent Model-Free controller of second order dynamics. We validate our control approach in three realistic scenarios: in presence of unknown measurement noise, with unknown time-varying wind disturbances and mass variation while tracking aggressive manoeuvres. 2. We employ the Model-Free Control to control (maintain) the “horizontal elasticity” of a Cloud Computing system. When compared to the commercial “Auto-Scaling” algorithms, our easily implementable approach behaves better, even with sharp workload fluctuations. This is confirmed by experiments on the Amazon Web Services (AWS) public cloud
Sriprang, Songklod. "High-Performance Nonlinear Control for Permanent Magnet Assisted Synchronous Reluctance Motor." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0269.
The electrification of transportation is one of the relevant solutions to reduce greenhouse gas emissions. Indeed, new European standards impose increasingly restrictive limits on CO₂ emissions per km. This context is an essential industrial issue for automobile manufacturers. Therefore, the industries are moving towards electric vehicles (EVs) in which an electric powertrain unit is present. This unit consists of an electrical machine powered by a static power electronic converter connected to an electrical energy source and storage. Different topologies have been studied for more than two decades for electric traction, and several solutions have been marketed. As a result, these products are increasingly light, reliable, and efficient while respecting the constraints of the automobile manufacturers on the costs.Recently, permanent magnet assisted (PMa)-synchronous reluctance motors (SynRM) have been considered a rare-earth-free machine possible alternative motor drive for high-performance applications suitable for EV powertrain units. However, in order to have an efficient motor drive, performing three steps in the design of the overall drive is not inevitable. These steps are design optimization of the motor, identifying the motor parameter, and implementing an advanced control system to ensure optimum operation. Therefore, this dissertation deal with high-performance nonlinear control of PMa-SynRM to find out the limitation of exiting nonlinear control system. The differential flatness-based control is first developed for the PMa-SynRM drive system. As it is a model-based control, the system performance relies on system model parameters, i.e., resistance, inertia, and external torque disturbance. Next step, model-free control is presented to be used in the control of both the SPMSM and PMa-SynRM. Finally, this thesis has achieved the main objective of finding out the high-performance nonlinear control of PMa-SynRM. Using a prototype PMa-SynRM drive as a test bench provided by GREEN Lab. at Université de Loraine, this paper gives an exhaustive description of an MFC's design procedure applied to the combined control of the motor speed and current. After a brief introduction of the MPC fundamentals, the design is illustrated in detail, giving a step-by-step discussion of the main critical points and the hints for their successful handling. Suggestions for extending the design to different drive controllers are also included. Simulations and numerous experimental results highlight the promising features and characteristics of MFC applied to PMSM drives. As the last contribution, the MFC potentials pointed out in this dissertation should stimulate further exploration and study on this type of controller to achieve the familiarity required to transfer the results to practical applications
Suryawan, Fajar. "Constrained trajectory generation and fault tolerant control based on differential flatness and B-splines." Thesis, 2011. http://hdl.handle.net/1959.13/927247.
This thesis provides a unified treatment of the notions of differential flatness, for the characterisation of continuous-time linear systems, and B-splines, a mathematical concept commonly used in computer graphics. Differential flatness is a property of some controlled (linear or nonlinear) dynamical systems, often encountered in applications, which allows for a complete parameterisation of all system variables (inputs and states) in terms of a finite number of variables, called flat outputs, and a finite number of their time derivatives. The notion of differential flatness for a system is especially useful in situations when explicit trajectory generation is required. In fact, under the differential flatness formalism the motion planning problem, as far as the differential equation is concerned, is trivialised. However, a very important limitation, ubiquitous in all practical applications, is the presence of constraints. The problem of constrained trajectory generation is intimately related to that of optimal control, where one wants to achieve certain objectives with limited resources, and time-optimal control, in which one seeks to perform a task as fast as possible while, at the same time, satisfying all system constraints. In the literature, trajectory generation and [time-] optimal control often use some parameterisation to represent the system's signals. Polynomials and B-splines are a natural choice since they have several desirable properties. However, there has not been much work exploiting the combined properties of differential flatness for linear systems and B-splines. The first focus of this thesis is, hence, to investigate the use of B-splines for constrained trajectory generation of continuous-time linear flat systems in such a way that their respective properties are jointly exploited and complemented. This synthesis offers new methods and insights to the fields of constrained trajectory optimisation, optimal control, and minimum-time trajectory generation. The differential flatness parameterisation also offers analytical redundancy relations. That is, the value of some variables can be algebraically inferred from some other measured variables. This fact can be used to perform algebraic estimation and fault detection in linear and nonlinear systems. The second focus of this thesis is, thus, to develop a method to perform algebraic estimation and fault detection, based structurally on the differential flatness notion, for linear and nonlinear systems, and using a numerical method based on B-splines. The methodology to tackle the focal problems of constrained trajectory generation and fault tolerant control, based on differential flatness and B-splines, is primarily developed for linear systems. Then, experimental validations of the methods, using a laboratory-scale magnetic levitation system, are provided. Finally, some extensions of the ideas to nonlinear systems are discussed.
Book chapters on the topic "Differential Flatness-Based Control":
Rigatos, Gerasimos G. "Differential Flatness Theory and Flatness-Based Control." In Nonlinear Control and Filtering Using Differential Flatness Approaches, 47–101. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16420-5_2.
Rigatos, Gerasimos G. "Nonlinear Adaptive Control Based on Differential Flatness Theory." In Nonlinear Control and Filtering Using Differential Flatness Approaches, 103–39. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16420-5_3.
Rigatos, Gerasimos G. "Nonlinear Kalman Filtering Based on Differential Flatness Theory." In Nonlinear Control and Filtering Using Differential Flatness Approaches, 141–81. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16420-5_4.
Tsiu, Lintle, and Elisha Didam Markus. "Multiple Mobile Robotic Formation Control Based on Differential Flatness." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 113–28. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35883-8_8.
Sun, Jiali, Yushu Yu, and Bin Xu. "Towards Flying Carpet: Dynamics Modeling, and Differential-Flatness-Based Control and Planning." In Communications in Computer and Information Science, 351–70. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0617-8_24.
Finta, Barnabás, and Bálint Kiss. "Equivalent Control of a 2D Crane and a 2D Drone Using Exact Linearization Based on Differential Flatness." In 25th International Symposium on Measurements and Control in Robotics, 131–41. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51085-4_12.
Conference papers on the topic "Differential Flatness-Based Control":
Wang, Yuxiao, Tao Chao, Songyan Wang, and Ming Yang. "Trajectory tracking control based on differential flatness." In 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7555072.
Niazi, Yasaman, Azadeh Gholaminejad, Diego Fernando Valencia Garcia, Sumedh Dhale, and Babak Nahid-Mobarakeh. "Differential Flatness-Based Control of Switched Reluctance Motors." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2210.
Kandler, Christoph, Steven X. Ding, Tim Koenings, Nick Weinhold, and Matthias Schultalbers. "A differential flatness based model predictive control approach." In 2012 IEEE International Conference on Control Applications (CCA). IEEE, 2012. http://dx.doi.org/10.1109/cca.2012.6402435.
Yoon, Jonghyun, Sung Wook Hwang, Jeong-Hyeon Bak, and Jong Hyeon Park. "Vibration Suppression of CDPRs Based on Differential Flatness." In 2018 IEEE Conference on Control Technology and Applications (CCTA). IEEE, 2018. http://dx.doi.org/10.1109/ccta.2018.8511527.
Melchior, Pierre, Mikae¨l Cugnet, Jocelyn Sabatier, and Alain Oustaloup. "Flatness Control: Application to a Fractional Thermal System." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85624.
Abadi, Amine, Anis Ben Hadj Brahim, Hassen Mekki, Adnen El Amraoui, and Nacim Ramdani. "Sliding Mode Control of Quadrotor based on Differential Flatness." In 2018 International Conference on Control, Automation and Diagnosis (ICCAD). IEEE, 2018. http://dx.doi.org/10.1109/cadiag.2018.8751334.
Markus, Elisha D. "Differential Flatness Based Synchronization Control of Multiple Heterogeneous Robots." In IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2018. http://dx.doi.org/10.1109/iecon.2018.8591428.
Lina Geng, Weimeng Sun, and Zhiqiang Zheng. "Trajectory optimization for guided bombs based on differential flatness." In 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5192065.
Noda, Yoshiyuki, Michael Zeitz, Oliver Sawodny, and Kazuhiko Terashima. "Flow rate control based on differential flatness in automatic pouring robot." In Control (MSC). IEEE, 2011. http://dx.doi.org/10.1109/cca.2011.6044508.
Ogunbodede, Oladapo, Souransu Nandi, and Tarunraj Singh. "Periodic Control of Unmanned Aerial Vehicles based on Differential Flatness." In 2018 Annual American Control Conference (ACC). IEEE, 2018. http://dx.doi.org/10.23919/acc.2018.8430793.