Page 66 - Read Online

Page 372 Zhang et al. Intell Robot 2022;2(4):371­90 I http://dx.doi.org/10.20517/ir.2022.26

1. INTRODUCTION
Advanced driver assistance systems (ADASs) play a critical role in the automobile industry [1] by significantly
decreasing drivers’ workload while considerably improving driving safety and comfort [2–5] . A few examples of
common applications of ADASs in automobiles in recent years are lane-keeping assist (LKA), adaptive cruise
[6]
control (ACC), electronic stability control (ESC), and the precrash system (PCS) . The ACC system is one
[7]
of the first ADAS technologies for maintaining a safe distance between an ego car and a preceding vehicle .
Radar sensors detect the velocity of the preceding vehicle, which the ACC system uses to automatically modify
[8]
the speed of the driving vehicle by managing the throttle opening or brake pedal levels .

Many adaptive cruise control strategies can be found in the literature to achieve longitudinal car-following and
enhance driving performance . A fuzzy logic control technique is described in Ref. [9] that executes the ACC
function on an AIT intelligent vehicle using the distance error and relative velocity information. In Ref. [10] , a
control system is presented that decreases vehicle waiting time at stop lights, as well as fuel consumption, by
utilizingupcomingtrafficsignalinformationandshort-rangeradarforoptimalvelocitytrajectoryplanning. In
Ref. [11] ,asafeandcomfortablelongitudinalautomationsystemwithahuman-in-the-loopstrategyisintegrated
into an ACC system. In Ref. [12] , the use of a longitudinal controller for a smart and green ACC system is
investigated to minimize energy expenditure and maximize energy regeneration.

Modelpredictivecontrol(MPC)isatraditionalcontrolapproachwithdemonstratedutilityforsolvingmultiob-
jective optimization problems under a variety of system constraints [13,14] . In the past few years, MPC has been
widely applied to the design of ACC systems. A few examples are presented here: in Ref. [15] , MPC is applied to
the design of spacing-control laws for transitional vehicle manoeuvres. A fuel economy-oriented ACC system
is developed in Ref. [16] to minimize vehicle fuel consumption, and a generic scale reduction framework is for-
mulated to alleviate computational loads induced by the MPC optimization solution. In Ref. [17] , a benchmark
setting for the MPC on a piecewise affine system is presented for the design of ACC algorithms, and differ-
ent methods are implemented and evaluated to assess their main attributes, characteristics, and strong/weak
points. A stochastic MPC approach for minimizing vehicle fuel consumption is investigated in Ref. [18] . An
MPC method for increasing vehicle tracking accuracy and reducing fuel consumption is developed [19] by tak-
ingintoaccountexternalroadinformation, spatiotemporalconstraintsandnonlinearpowertraindynamics. In
Ref. [20] , a personalized ACC system based on driving style identification is proposed to accommodate various
driving types within an MPC framework.

The Takagi-Sugeno (T-S) fuzzy system consists of a cluster of linear subsystems as an approximation for a non-
linear system. Extensive studies have been performed on this system in recent decades [21–24] . In vehicle con-
trol, vehicle dynamics are typically regarded as linear parameter varying (LPV) systems because of inevitable
variations in parameters, such as longitudinal and lateral velocities. T-S fuzzy systems are constructed to
model the vehicle dynamics and address parameter variations in the system. For example, in Ref. [25] , a fuzzy
path-tracking controller is designed considering uncertain lateral tire forces, a time-varying vehicle speed,
steering-input saturation and vehicle state conditions. In Ref. [26] , a fuzzy-model-based H ∞ control algorithm
isproposed consideringconstraintsontheamplitudeandrateofsteering. InRef. [27] , a path tracking controller
basedonoutputfeedbackisdevelopedconsideringthetransientbehaviourofthesystem. However, fewstudies
have been performed on integrating T-S fuzzy modelling into ACC systems, and this subject requires further
investigation.

The aforementioned literature review shows that substantial progress has been made in both theoretical formu-
lations and practical applications of ACC design for car-following within the model-based predictive control
framework. Notably, cars may lose lateral stability when employing a cruise controller in some emergency
situations, such as rapid braking on roads with low friction coefficients. Thus, vehicle lateral stability needs to
be considered when developing ACC strategies. In some studies, a linear force relationship is utilized between