The Experts below are selected from a list of 33396 Experts worldwide ranked by ideXlab platform
Mehdi Mirzaei - One of the best experts on this subject based on the ideXlab platform.
-
A new strategy for minimum usage of external Yaw moment in vehicle dynamic control system
Transportation Research Part C: Emerging Technologies, 2010Co-Authors: Mehdi MirzaeiAbstract:Due to the loss of vehicle directional stability in emergency maneuvers, a new complete desired model for vehicle handling based on the linear two-degrees-of-freedom (2DOF) model and tire/road conditions is presented to be tracked by the direct Yaw moment control (DYC) system. In order to maintain the vehicle actual motions, Yaw rate and side-slip angle, close to the proposed desired responses without excessively large external Yaw moment, a complete linear quadratic (LQ) optimal problem is formulated and its analytical solution is obtained. Here, the derived control law is evaluated and its different versions are discussed. It is shown that the side-slip tracking by DYC is more effective than the Yaw rate control to stabilize vehicle motions in nonlinear regimes. Also, optimal property of the control law provides the possibility of reducing the external Yaw moment as low as possible, at the cost of some admissible tracking errors. Simulation studies of vehicle handling, with and without control, have been conducted using a full nonlinear vehicle dynamic model. The results, obtained during various maneuvers, indicate that when the proposed optimal controller is engaged with the model, improvements in the handling performance through a reduced external Yaw moment can be acquired.
-
Optimization-based non-linear Yaw moment control law for stabilizing vehicle lateral dynamics
Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering, 2007Co-Authors: M Eslamian, Ghasem Alizadeh, Mehdi MirzaeiAbstract:AbstractFor a direct Yaw moment control (DYC) system, using an external Yaw moment that is as low as possible for stabilizing the vehicle-handling dynamics in the non-linear regimes can be consider...
Kristofer S. J. Pister - One of the best experts on this subject based on the ideXlab platform.
-
Nonholonomic Yaw Control of an Underactuated Flying Robot With Model-Based Reinforcement Learning
IEEE Robotics and Automation Letters, 2021Co-Authors: Nathan O. Lambert, Craig B. Schindler, Daniel S. Drew, Kristofer S. J. PisterAbstract:Nonholonomic control is a candidate to control nonlinear systems with path-dependant states. We investigate an underactuated flying micro-aerial-vehicle, the ionocraft, that requires nonholonomic control in the Yaw-direction for complete attitude control. Deploying an analytical control law involves substantial engineering design and is sensitive to inaccuracy in the system model. With specific assumptions on assembly and system dynamics, we derive a Lie bracket for Yaw control of the ionocraft. As a comparison to the significant engineering effort required for an analytic control law, we implement a data-driven model-based reinforcement learning Yaw controller in a simulated flight task. We demonstrate that a simple model-based reinforcement learning framework can match the derived Lie bracket control - in Yaw rate and chosen actions - in a few minutes of flight data, without a pre-defined dynamics function. This letter shows that learning-based approaches are useful as a tool for synthesis of nonlinear control laws previously only addressable through expert-based design.
-
nonholonomic Yaw control of an underactuated flying robot with model based reinforcement learning
arXiv: Robotics, 2020Co-Authors: Nathan Lambert, Craig B. Schindler, Daniel S. Drew, Kristofer S. J. PisterAbstract:Nonholonomic control is a candidate to control nonlinear systems with path-dependant states. We investigate an underactuated flying micro-aerial-vehicle, the ionocraft, that requires nonholonomic control in the Yaw-direction for complete attitude control. Deploying an analytical control law involves substantial engineering design and is sensitive to inaccuracy in the system model. With specific assumptions on assembly and system dynamics, we derive a Lie bracket for Yaw control of the ionocraft. As a comparison to the significant engineering effort required for an analytic control law, we implement a data-driven model-based reinforcement learning Yaw controller in a simulated flight task. We demonstrate that a simple model-based reinforcement learning framework can match the derived Lie bracket control (in Yaw rate and chosen actions) in a few minutes of flight data, without a pre-defined dynamics function. This paper shows that learning-based approaches are useful as a tool for synthesis of nonlinear control laws previously only addressable through expert-based design.
Yifan Dai - One of the best experts on this subject based on the ideXlab platform.
-
coordinated path following and direct Yaw moment control of autonomous electric vehicles with sideslip angle estimation
Mechanical Systems and Signal Processing, 2018Co-Authors: Jinghua Guo, Yugong Luo, Yifan DaiAbstract:Abstract This paper presents a novel coordinated path following system (PFS) and direct Yaw-moment control (DYC) of autonomous electric vehicles via hierarchical control technique. In the high-level control law design, a new fuzzy factor is introduced based on the magnitude of longitudinal velocity of vehicle, a linear time varying (LTV)-based model predictive controller (MPC) is proposed to acquire the wheel steering angle and external Yaw moment. Then, a pseudo inverse (PI) low-level control allocation law is designed to realize the tracking of desired external moment torque and management of the redundant tire actuators. Furthermore, the vehicle sideslip angle is estimated by the data fusion of low-cost GPS and INS, which can be obtained by the integral of modified INS signals with GPS signals as initial value. Finally, the effectiveness of the proposed control system is validated by the simulation and experimental tests.
Nathan O. Lambert - One of the best experts on this subject based on the ideXlab platform.
-
Nonholonomic Yaw Control of an Underactuated Flying Robot With Model-Based Reinforcement Learning
IEEE Robotics and Automation Letters, 2021Co-Authors: Nathan O. Lambert, Craig B. Schindler, Daniel S. Drew, Kristofer S. J. PisterAbstract:Nonholonomic control is a candidate to control nonlinear systems with path-dependant states. We investigate an underactuated flying micro-aerial-vehicle, the ionocraft, that requires nonholonomic control in the Yaw-direction for complete attitude control. Deploying an analytical control law involves substantial engineering design and is sensitive to inaccuracy in the system model. With specific assumptions on assembly and system dynamics, we derive a Lie bracket for Yaw control of the ionocraft. As a comparison to the significant engineering effort required for an analytic control law, we implement a data-driven model-based reinforcement learning Yaw controller in a simulated flight task. We demonstrate that a simple model-based reinforcement learning framework can match the derived Lie bracket control - in Yaw rate and chosen actions - in a few minutes of flight data, without a pre-defined dynamics function. This letter shows that learning-based approaches are useful as a tool for synthesis of nonlinear control laws previously only addressable through expert-based design.
Junmin Wang - One of the best experts on this subject based on the ideXlab platform.
-
vehicle lateral dynamics control through afs dyc and robust gain scheduling approach
IEEE Transactions on Vehicular Technology, 2016Co-Authors: Hui Zhang, Junmin WangAbstract:In this paper, we investigate the combined active front-wheel steering/direct Yaw-moment control for the improvement of vehicle lateral stability and vehicle handling performance. A more practical assumption in this work is that the longitudinal velocity is not constant but varying within a range. Both the nonlinear tire model and the variation of longitudinal velocity are considered in vehicle system modeling. A linear-parameter-varying model with norm-bounded uncertainties is obtained. To track the system reference, a generalized proportional-integral (PI) control law is proposed. Since it is difficult to get the analytic solution for the PI gains, an augmented system is developed, and the PI control is then converted into the state-feedback control for the augmented system. Both the stability and the energy-to-peak performance of the augmented system are explored. Based on the analysis results, the controller-gain tuning method is proposed. The proposed control law and controller design method are illustrated via an electric vehicle model.
