| Advanced Vehicle Control System (AVCS) design is an integral part of the rapidly growing national and international initiatives on Intelligent Transportation Systems (ITS) and Automated Highway Systems (AHS), which aim at significantly increasing the traffic capacity of existing highways through vehicle and roadway automation. In the past few years, AVCS research and development has been primarily focused on passenger vehicles, while commercial heavy vehicles (CHVs) such as heavy-duty freight trucks and commuter buses have been largely ignored.; We present adaptive nonlinear schemes for longitudinal control of CHVs. One of the most visible proposed strategies for highway automation is to group automatically controlled vehicles in platoons, i.e., tightly spaced vehicle group formations. An important control objective is string stability, which ensures that errors decrease as they propagate upstream through the platoon. It is well known that string stability requires intervehicle communication if a constant spacing policy is adopted. When vehicles operate autonomously, string stability can be achieved if speed-dependent spacing with constant time headway is used. This, however, results in larger steady-state spacings, which increase the platoon length hence decreasing traffic throughput. This disadvantage is even more pronounced in CHVs, which require larger time headways due to their low actuation-to-weight ratio.; We develop two new nonlinear spacing policies, variable time headway and variable separation error gain, which all but eliminate this undesirable side effect. The first policy significantly reduces the transient errors and allows us to use much smaller spacings in autonomous platoon operation, while the second one results in smoother and more robust longitudinal control. Furthermore, the two can be combined to yield even better robustness with respect to maneuvers. In order to achieve robustness with respect to significant actuator delays (present in existing CHVs) as well, we design a new controller using the backstepping methodology. A predictor is also added to the control loop for control smoothness enhancement. |
