A systematic approach to cooperative driving systems based on optimal control allocation
Creators: name: Author, Unknown
Publisher: name: University of Lincoln
Year: 2023

description: This dissertation proposes a systematic approach to vehicle dynamic control, whereinteraction between the human driver and on-board automated driving systems is considered a fundamental part of the overall control design. The hierarchical control systemis to address motion control in three regions. First is normal driving, where the vehiclestays within the linear region of the tyre. Second is limit driving, where the vehicle stayswithin 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 controlmoderator (CM). The aim is to consider all three cases within a consistent hierarchicalchassis control framework. The upper-level of the hierarchical control structure relatesto both optimal vehicle control under normal and limit driving, and saturating driverdemands for over-limit driving, these corresponding to a fully autonomous controllerand driver assistance controller respectively.Model Predictive Control (MPC) is used as the core control technique for path followingunder normal driving conditions, and a Moderated Particle Reference (MPR) controlstrategy 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 frictionlimits, maintaining controllability for curvature and speed tracking, which effectivelylimits 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 constrainedquadratic 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 guaranteedto provide a feasible control target for the actuators to track during the highly dynamicdriving scenarios. In addition, it can effectively solve the constrained optimal controlproblem with additional mechanical and electronic actuator constraints. Thus, the proposed PICA method, which uses Control Re-Allocation (making multiple calls to thepseudo-inverse operator) can be considered a feasible and novel alternative approach tocontrol allocation, with advantages over the standard CQP method.Finally, in the lower-level of the hierarchical control structure, the desired tyre controlvariables are obtained through an analytical inverse tyre model and a sliding modecontroller (SMC) is employed for the actuators to track the control target. The proposedhierarchical control system is validated with both driving simulator studies and fromtesting a real vehicle, considering a wide range of driving scenarios, from low-speed pathtracking to safety-critical vehicle dynamic control. It therefore opens up a systematicapproach to extended vehicle control applications, from fully autonomous driving todriver assistance systems and control objects from passenger cars to vehicles with highercentre of gravity (CoG) like SUVs, trucks and etc. . . .