TY - JOUR
T1 - ASG version of integral sliding mode robust controller for AV nonholonomic 2D models avoiding obstacles
AU - Vargas, Hector
AU - Meda, Jesús A.
AU - Poznyak, Alexander
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature B.V.
PY - 2022/6
Y1 - 2022/6
N2 - In this paper, a new robust controller is developed and analyzed for an autonomous vehicle (AV) with nonholonomic dynamics driving in a 2D plane that can avoid colliding with a set of obstacles despite the presence of uncertainties in the mathematical model of the AV. The state variables and their velocities are considered to be measurable (two simple coordinates and three angles). The controller is based on the integral sliding mode (ISM) concept, which aims to minimize the current state’s convex (but not necessarily strongly convex) function. A cost function’s subgradient is likewise designed to be measured online. The averaged subgradient (ASG) technique is used to build and analyze an optimization algorithm. The major findings show that the intended regime (non-stationary analogue of sliding surface) can be reached from the start of the process and deriving an explicit upper bound for the cost function decrement, i.e., proving functional convergence and estimating the rate of convergence, thereby allowing for multiple obstacle avoidance. The proposed strategy is shown to perform effectively with a numerical example.
AB - In this paper, a new robust controller is developed and analyzed for an autonomous vehicle (AV) with nonholonomic dynamics driving in a 2D plane that can avoid colliding with a set of obstacles despite the presence of uncertainties in the mathematical model of the AV. The state variables and their velocities are considered to be measurable (two simple coordinates and three angles). The controller is based on the integral sliding mode (ISM) concept, which aims to minimize the current state’s convex (but not necessarily strongly convex) function. A cost function’s subgradient is likewise designed to be measured online. The averaged subgradient (ASG) technique is used to build and analyze an optimization algorithm. The major findings show that the intended regime (non-stationary analogue of sliding surface) can be reached from the start of the process and deriving an explicit upper bound for the cost function decrement, i.e., proving functional convergence and estimating the rate of convergence, thereby allowing for multiple obstacle avoidance. The proposed strategy is shown to perform effectively with a numerical example.
KW - Averaged subgradient method
KW - Car obstacle avoidance
KW - Integral sliding mode control
KW - Nonholonomic dynamic model
UR - http://www.scopus.com/inward/record.url?scp=85128687320&partnerID=8YFLogxK
U2 - 10.1007/s11071-022-07408-4
DO - 10.1007/s11071-022-07408-4
M3 - Artículo de revisión
AN - SCOPUS:85128687320
SN - 0924-090X
VL - 108
SP - 2875
EP - 2887
JO - Nonlinear Dynamics
JF - Nonlinear Dynamics
IS - 4
ER -