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- Salt Ducaju, Julian M., et al.
(author)
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Application Specific System Identification for Model-Based Control in Self-Driving Cars
- 2020
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In: ; , s. 384-390
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Conference paper (peer-reviewed)abstract
- Linear Parameter Varying (LPV) models can be used to describe the vehicular lateral dynamic behavior of self-driving cars. They are particularly suitable for model-based control schemes such as model predictive control (MPC) applied to real-time trajectory tracking control, since they provide a proper trade-off between accuracy in different scenarios and reduced computation cost compared to nonlinear models. The MPC control schemes use the model for a long prediction horizon of the states, therefore prediction errors for a long time horizon should be minimized in order to increase the accuracy of the tracking. For this task, this work presents a system identification procedure for the lateral dynamics of a vehicle that combines a LPV model with a learning algorithm that has been successfully applied to other dynamic systems in the past. Simulation results show the benefits of the identified model in comparison to other well-known vehicular lateral dynamic models.
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- Salt Ducajú, Julián M., et al.
(author)
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Autonomous ground vehicle lane-keeping LPV model-based control : Dual-rate state estimation and comparison of different real-time control strategies
- 2021
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In: Sensors. - : MDPI AG. - 1424-8220. ; 21:4
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Journal article (peer-reviewed)abstract
- In this contribution, we suggest two proposals to achieve fast, real-time lane-keeping control for Autonomous Ground Vehicles (AGVs). The goal of lane-keeping is to orient and keep the vehicle within a given reference path using the front wheel steering angle as the control action for a specific longitudinal velocity. While nonlinear models can describe the lateral dynamics of the vehicle in an accurate manner, they might lead to difficulties when computing some control laws such as Model Predictive Control (MPC) in real time. Therefore, our first proposal is to use a Linear Parameter Varying (LPV) model to describe the AGV’s lateral dynamics, as a trade-off between computational complexity and model accuracy. Additionally, AGV sensors typically work at different measurement acquisition frequencies so that Kalman Filters (KFs) are usually needed for sensor fusion. Our second proposal is to use a Dual-Rate Extended Kalman Filter (DREFKF) to alleviate the cost of updating the internal state of the filter. To check the validity of our proposals, an LPV model-based control strategy is compared in simulations over a circuit path to another reduced computational complexity control strategy, the Inverse Kinematic Bicycle model (IKIBI), in the presence of process and measurement Gaussian noise. The LPV-MPC controller is shown to provide a more accurate lane-keeping behavior than an IKIBI control strategy. Finally, it is seen that Dual-Rate Extended Kalman Filters (DREKFs) constitute an interesting tool for providing fast vehicle state estimation in an AGV lane-keeping application.
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- Salt Ducaju, Julian M., et al.
(author)
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Joint Stiction Avoidance with Null-Space Motion in Real-Time Model Predictive Control for Redundant Collaborative Robots
- 2021
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In: 2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021. - 1944-9445 .- 1944-9437. - 9781665404921 ; , s. 307-314
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Conference paper (peer-reviewed)abstract
- Model Predictive Control (MPC) is an efficient point-to-point trajectory-generation method for robots that can be used in situations that occur under time constraints. The motion plan can be recalculated online to increase the accuracy of the trajectory when getting close to the goal position. We have implemented this strategy in a Franka Emika Panda robot, a redundant collaborative robot, by extending previous research that was performed on a 6-DOF robot. We have also used null-space motion to ensure a continuous movement of all joints during the entire trajectory execution as an approach to avoid joint stiction and allow accurate kinesthetic teaching. As is conventional for collaborative and industrial robots, the Panda robot is equipped with an internal controller, which allows to send position and velocity references directly to the robot. Therefore, null-space motion can be added directly to the MPC-generated velocity references. The observed trajectory deviation caused by discretization approximations of the Jacobian matrix when implementing null-space motion has been corrected experimentally using sensor feedback for the real-time velocity-reference recalculation and by performing a fast sampling of the null-space vector. Null-space motion has been experimentally seen to contribute to reducing the friction torque dispersion present in static joints.
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