A systematic approach to cooperative driving systems based on optimal control allocation

Zhang, Dong (2019) A systematic approach to cooperative driving systems based on optimal control allocation. PhD thesis, University of Lincoln.

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A systematic approach to cooperative driving systems based on optimal control allocation
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Item Type:Thesis (PhD)
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Abstract

This dissertation proposes a systematic approach to vehicle dynamic control, where
interaction between the human driver and on-board automated driving systems is considered a fundamental part of the overall control design. The hierarchical control system
is to address motion control in three regions. First is normal driving, where the vehicle
stays within the linear region of the tyre. Second is limit driving, where the vehicle stays
within the nonlinear region of the tyre. Third is over-limit driving, where the driver demands go beyond the tyre force limits. The third case is addressed by a proposed control
moderator (CM). The aim is to consider all three cases within a consistent hierarchical
chassis control framework. The upper-level of the hierarchical control structure relates
to both optimal vehicle control under normal and limit driving, and saturating driver
demands for over-limit driving, these corresponding to a fully autonomous controller
and driver assistance controller respectively.
Model Predictive Control (MPC) is used as the core control technique for path following
under normal driving conditions, and a Moderated Particle Reference (MPR) control
strategy is proposed for the road departure mitigation during limit and over-limit driving.
The MPR model is validated to ensure predictable and stable operation near the friction
limits, maintaining controllability for curvature and speed tracking, which effectively
limits demands on the vehicle while preserving the control interaction of the driver.
In the next level of the hierarchical control structure, a novel control allocation (CA) approach based on pseudo-inverse method is proposed, while a general linearly constrained
quadratic programming (CQP) approach is considered as a benchmark. From extended simulation experiments, it is found that the proposed Pseudo-Inverse CA (PICA)
method can achieve a close match to CQP performance in normal driving conditions.
This applies for multiple control targets (including path tracking, energy-efficient, etc.)
and PICA is found to achieve improved performance in limit and over-limit driving,
again addressing multiple control targets (including road departure mitigation, energyefficient, etc.). Furthermore, the PICA method shows its inherent advantages of achieving the same control performance with much less computational cost and is guaranteed
to provide a feasible control target for the actuators to track during the highly dynamic
driving scenarios. In addition, it can effectively solve the constrained optimal control
problem with additional mechanical and electronic actuator constraints. Thus, the proposed PICA method, which uses Control Re-Allocation (making multiple calls to the
pseudo-inverse operator) can be considered a feasible and novel alternative approach to
control allocation, with advantages over the standard CQP method.
Finally, in the lower-level of the hierarchical control structure, the desired tyre control
variables are obtained through an analytical inverse tyre model and a sliding mode
controller (SMC) is employed for the actuators to track the control target. The proposed
hierarchical control system is validated with both driving simulator studies and from
testing a real vehicle, considering a wide range of driving scenarios, from low-speed path
tracking to safety-critical vehicle dynamic control. It therefore opens up a systematic
approach to extended vehicle control applications, from fully autonomous driving to
driver assistance systems and control objects from passenger cars to vehicles with higher
centre of gravity (CoG) like SUVs, trucks and etc. . . .

Divisions:College of Science > School of Engineering
ID Code:39313
Deposited On:23 Dec 2019 16:31

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