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Model predictive control (MPC) has become more relevant to vehicle dynamics control due to its inherent capacity of treating system constraints. However, online optimization from MPC introduces an extensive computational burden for today’s onboard microprocessors. To alleviate MPC computational load, several methods have been proposed. Among them, online successive system linearization and the resulting linear time-varying model predictive controller (LTVMPC) is one of the most popular options. Nevertheless, such online successive linearization commonly approximates the original (nonlinear) system by a linear one, which inevitably introduces extra modeling errors and therefore reduces MPC performance. Actually, if the controlled system possesses the “differential flatness” property, then it can be exactly linearized and an equivalent linear model will appear. This linear model maintains all the nonlinear features of the original system and can be utilized to design a flatness-based model predictive controller (FMPC). CarSim-Simulink joint simulations demonstrate that the proposed FMPC substantially outperforms a classical LTVMPC in terms of the path-tracking performance for autonomous vehicles.
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Hybrid Reinforcement Learning based controller for autonomous navigation
Safe operations of autonomous mobile robots in close proximity to humans, creates a need for enhanced trajectory tracking (with low tracking errors). Linear optimal control techniques such as Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) have been used successfully for low-speed applications while leveraging their model-based methodology with manageable computational demands. However, model and parameter uncertainties or other unmodeled nonlinearities may cause poor control actions and constraint violations. Nonlinear MPC has emerged as an alternate optimal-control approach but needs to overcome real-time deployment challenges (including fast sampling time, design complexity, and limited computational resources). In recent years, the optimal control-based deployments have benefitted enormously from the ability of Deep Neural Networks (DNNs) to serve as universal function approximators. This has led to deployments in a plethora of previously inaccessible applications – but many aspects of generalizability, benchmarking, and systematic verification and validation coupled with benchmarking have emerged. This paper presents a novel approach to fusing Deep Reinforcement Learning-based (DRL) longitudinal control with a traditional PID lateral controller for autonomous navigation. Our approach follows (i) Generation of an adequate fidelity simulation scenario via a Real2Sim approach; (ii) training a DRL agent within this framework; (iii) Testing the performance and generalizability on alternate scenarios. We use an initial tuned set of the lateral PID controller gains for observing the vehicle response over a range of velocities. Then we use a DRL framework to generate policies for an optimal longitudinal controller that successfully complements the lateral PID to give the best tracking performance for the vehicle.
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- PAR ID:
- 10357358
- Date Published:
- Journal Name:
- 2022 IEEE 95th Vehicular Technology Conference, VTC2022-Spring
- Page Range / eLocation ID:
- 1-6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/VTC2022-Spring54318.2022.9861014
