The Experts below are selected from a list of 88440 Experts worldwide ranked by ideXlab platform
Angela P Schoellig - One of the best experts on this subject based on the ideXlab platform.
-
provably robust learning based approach for high accuracy tracking control of lagrangian systems
International Conference on Robotics and Automation, 2019Co-Authors: Mohamed K Helwa, Adam Heins, Angela P SchoelligAbstract:Lagrangian systems represent a wide range of robotic systems, including manipulators, wheeled and legged robots, and quadrotors. Inverse dynamics control and feedforward linearization are typically used to convert the complex nonlinear dynamics of Lagrangian systems to a set of decoupled double integrators, and then a standard, outer-Loop Controller can be used to calculate the commanded acceleration for the linearized system. However, these methods typically depend on having a very accurate system model, which is often not available in practice. While this challenge has been addressed in the literature using different learning approaches, most of these approaches do not provide safety guarantees in terms of stability of the learning-based control system. In this letter, we provide a novel, learning-based control approach based on Gaussian processes (GPs) that ensures both stability of the closed-Loop system and high-accuracy tracking. We use GPs to approximate the error between the commanded and the actual acceleration of the system, and then use the predicted mean and variance of the GP to calculate an upper bound on the uncertainty of the linearized model. This uncertainty bound is then used in a robust, outer-Loop Controller to ensure stability of the overall system. Moreover, we show that the tracking error converges to a ball with a radius that can be made arbitrarily small. Finally, we verify the effectiveness of our approach via simulations on a 2 degree-of-freedom (DOF) planar manipulator and experimentally on a 6 DOF industrial manipulator.
-
provably robust learning based approach for high accuracy tracking control of lagrangian systems
arXiv: Robotics, 2018Co-Authors: Mohamed K Helwa, Adam Heins, Angela P SchoelligAbstract:Lagrangian systems represent a wide range of robotic systems, including manipulators, wheeled and legged robots, and quadrotors. Inverse dynamics control and feedforward linearization techniques are typically used to convert the complex nonlinear dynamics of Lagrangian systems to a set of decoupled double integrators, and then a standard, outer-Loop Controller can be used to calculate the commanded acceleration for the linearized system. However, these methods typically depend on having a very accurate system model, which is often not available in practice. While this challenge has been addressed in the literature using different learning approaches, most of these approaches do not provide safety guarantees in terms of stability of the learning-based control system. In this paper, we provide a novel, learning-based control approach based on Gaussian processes (GPs) that ensures both stability of the closed-Loop system and high-accuracy tracking. We use GPs to approximate the error between the commanded acceleration and the actual acceleration of the system, and then use the predicted mean and variance of the GP to calculate an upper bound on the uncertainty of the linearized model. This uncertainty bound is then used in a robust, outer-Loop Controller to ensure stability of the overall system. Moreover, we show that the tracking error converges to a ball with a radius that can be made arbitrarily small. Furthermore, we verify the effectiveness of our approach via simulations on a 2 degree-of-freedom (DOF) planar manipulator and experimentally on a 6 DOF industrial manipulator.
Houria Siguerdidjane - One of the best experts on this subject based on the ideXlab platform.
-
nonlinear control with wind estimation of a dfig variable speed wind turbine for power capture optimization
Energy Conversion and Management, 2009Co-Authors: Boubekeur Boukhezzar, Houria SiguerdidjaneAbstract:A cascaded nonlinear Controller is designed for a variable speed wind turbine equipped with a Doubly Fed Induction Generator (DFIG). The main objective of the Controller is wind energy capture optimization while avoiding strong transients in the turbine components and specially in the drive train. The inner Loop Controller ensures an efficient tracking of both generator torque and stator flux, while the outer Loop Controller achieves a close tracking of the optimal blade rotor speed to optimize wind energy capture. It is combined to a wind speed estimator that provides an estimation of the wind speed and the aerodynamic torque involved in the Controller. The global Controller is firstly tested with a simplified mathematical model of the aeroturbine and DFIG for a high-turbulence wind speed profile. Secondly, the aeroturbine Controller is validated upon a flexible wind turbine simulator. These new control strategies are compared to other existing Controllers based on tests upon an aeroelastic wind turbine simulator. The obtained results show better performance in comparison with the existing Controllers.
Mohamed K Helwa - One of the best experts on this subject based on the ideXlab platform.
-
provably robust learning based approach for high accuracy tracking control of lagrangian systems
International Conference on Robotics and Automation, 2019Co-Authors: Mohamed K Helwa, Adam Heins, Angela P SchoelligAbstract:Lagrangian systems represent a wide range of robotic systems, including manipulators, wheeled and legged robots, and quadrotors. Inverse dynamics control and feedforward linearization are typically used to convert the complex nonlinear dynamics of Lagrangian systems to a set of decoupled double integrators, and then a standard, outer-Loop Controller can be used to calculate the commanded acceleration for the linearized system. However, these methods typically depend on having a very accurate system model, which is often not available in practice. While this challenge has been addressed in the literature using different learning approaches, most of these approaches do not provide safety guarantees in terms of stability of the learning-based control system. In this letter, we provide a novel, learning-based control approach based on Gaussian processes (GPs) that ensures both stability of the closed-Loop system and high-accuracy tracking. We use GPs to approximate the error between the commanded and the actual acceleration of the system, and then use the predicted mean and variance of the GP to calculate an upper bound on the uncertainty of the linearized model. This uncertainty bound is then used in a robust, outer-Loop Controller to ensure stability of the overall system. Moreover, we show that the tracking error converges to a ball with a radius that can be made arbitrarily small. Finally, we verify the effectiveness of our approach via simulations on a 2 degree-of-freedom (DOF) planar manipulator and experimentally on a 6 DOF industrial manipulator.
-
provably robust learning based approach for high accuracy tracking control of lagrangian systems
arXiv: Robotics, 2018Co-Authors: Mohamed K Helwa, Adam Heins, Angela P SchoelligAbstract:Lagrangian systems represent a wide range of robotic systems, including manipulators, wheeled and legged robots, and quadrotors. Inverse dynamics control and feedforward linearization techniques are typically used to convert the complex nonlinear dynamics of Lagrangian systems to a set of decoupled double integrators, and then a standard, outer-Loop Controller can be used to calculate the commanded acceleration for the linearized system. However, these methods typically depend on having a very accurate system model, which is often not available in practice. While this challenge has been addressed in the literature using different learning approaches, most of these approaches do not provide safety guarantees in terms of stability of the learning-based control system. In this paper, we provide a novel, learning-based control approach based on Gaussian processes (GPs) that ensures both stability of the closed-Loop system and high-accuracy tracking. We use GPs to approximate the error between the commanded acceleration and the actual acceleration of the system, and then use the predicted mean and variance of the GP to calculate an upper bound on the uncertainty of the linearized model. This uncertainty bound is then used in a robust, outer-Loop Controller to ensure stability of the overall system. Moreover, we show that the tracking error converges to a ball with a radius that can be made arbitrarily small. Furthermore, we verify the effectiveness of our approach via simulations on a 2 degree-of-freedom (DOF) planar manipulator and experimentally on a 6 DOF industrial manipulator.
Boubekeur Boukhezzar - One of the best experts on this subject based on the ideXlab platform.
-
nonlinear control with wind estimation of a dfig variable speed wind turbine for power capture optimization
Energy Conversion and Management, 2009Co-Authors: Boubekeur Boukhezzar, Houria SiguerdidjaneAbstract:A cascaded nonlinear Controller is designed for a variable speed wind turbine equipped with a Doubly Fed Induction Generator (DFIG). The main objective of the Controller is wind energy capture optimization while avoiding strong transients in the turbine components and specially in the drive train. The inner Loop Controller ensures an efficient tracking of both generator torque and stator flux, while the outer Loop Controller achieves a close tracking of the optimal blade rotor speed to optimize wind energy capture. It is combined to a wind speed estimator that provides an estimation of the wind speed and the aerodynamic torque involved in the Controller. The global Controller is firstly tested with a simplified mathematical model of the aeroturbine and DFIG for a high-turbulence wind speed profile. Secondly, the aeroturbine Controller is validated upon a flexible wind turbine simulator. These new control strategies are compared to other existing Controllers based on tests upon an aeroelastic wind turbine simulator. The obtained results show better performance in comparison with the existing Controllers.
Ehsan M Siavashi - One of the best experts on this subject based on the ideXlab platform.
-
a closed Loop Controller to improve the stability of cascaded dc dc converters
arXiv: Signal Processing, 2018Co-Authors: Maziar Isapour Chehardeh, Ehsan M SiavashiAbstract:Study of the buck converter and cascaded system considering the voltage mode Controller has been done. First the small signal analysis of a buck dc/dc converter is presented and its mathematical representation has been showed. Then, the cascaded converter model regarding close Loop impedances and voltage gain has been studied. The Controller for this converter is proposed to stabilize the performance of the plant. The effectiveness of the proposed Controller has been tested on a typical buck converter.
