Although deep reinforcement learning (deep RL) methods have lots of strengths that are favorable if applied to autonomous driving, real deep RL applications in autonomous driving have been slowed down by the modeling gap between the source (training) domain and the target (deployment) domain. Unlike current policy transfer approaches, which generally limit to the usage of uninterpretable neural network representations as the transferred features, we propose to transfer concrete kinematic quantities in autonomous driving. The proposed robust-control-based (RC) generic transfer architecture, which we call RL-RC, incorporates a transferable hierarchical RL trajectory planner and a robust tracking controller based on disturbance observer (DOB). The deep RL policies trained with known nominal dynamics model are transfered directly to the target domain, DOB-based robust tracking control is applied to tackle the modeling gap including the vehicle dynamics errors and the external disturbances such as side forces. We provide simulations validating the capability of the proposed method to achieve zero-shot transfer across multiple driving scenarios such as lane keeping, lane changing and obstacle avoidance.
DOB for Vehicle Lateral Control. Vehicle lateral control for trajectory tracking has been well-established with many existing methods @cite_15 . DOB is a robust control technique to reject disturbances with guaranteed robust stability for linear system @cite_0 . Moreover, given a stabilizing nominal controller, Q-filter in DOB can be an arbitrary stable filter to make the sensitivity function shaped as desired @cite_12 . It has been applied to robust lateral trajectory tracking and its effectiveness has been verified by experiments @cite_5 .
@cite_0 @cite_5 @cite_15 @cite_12
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In this contribution a structure for high level lateral vehicle tracking control is presented. It is based on the two degrees of freedom structure that allows to separately define the command response and disturbance attenuation. The application of the disturbance observer guarantees robust compensation of the disturbances. An advantage of the presented structure is its robustness against variable vehicle parameters. Only a considerably reduced model is necessary which significantly simplifies the application process. Moreover the presented approach is characterized by its extensibility and its modularity. This paper proposes a robust PD control scheme for flexible-joint robots based on a disturbance observer (DOB). In this paper, the DOB is applied only to the motor-side dynamics of the robot, and the uncertainties on the motor-side are successfully eliminated. It is shown that the proposed DOB-based approach guarantees global asymptotic stability. To this end, two special treatments are required. First, unlike the typical configuration of the DOB, nominal states of the motor-side are fed back to the PD controller. Second, a control input that makes the nominal states stable is additionally introduced. The proposed approach was verified using multi-degree-of-freedom experiments. This article addresses the trajectory tracking problem for a wheeled mobile base, considering the presence of disturbances that violate the nonholonomic constraint, and using an approximated discrete-time model for the vehicle. The proposed solution is based on discrete-time sliding mode control, in order to ensure that the controller is both robust and implementable. The asymptotic boundedness of the discrete-time tracking errors is theoretically proved, and experimental results are reported, showing the effectiveness of the proposed control law. © 2002 Wiley Periodicals, Inc. This paper addresses the trajectory tracking problem of an autonomous tractor-trailer system by using a decentralized control approach. A fully decentralized model predictive controller is designed in which interactions between subsystems are neglected and assumed to be perturbations to each other. In order to have a robust design, a tube-based approach is proposed to handle the differences between the nominal model and real system. Nonlinear moving horizon estimation is used for the state and parameter estimation after each new measurement, and the estimated values are fed to robust tube-based decentralized nonlinear model predictive controller. The proposed control scheme is capable of driving the tractor-trailer system to any desired trajectory ensuring high control accuracy and robustness against neglected subsystem interactions and environmental disturbances. The experimental results show an accurate trajectory tracking performance on a bumpy grass field.