Open AccessAdaptive fuzzy optimal control for a class of active suspension systems with full‐state constraints
Author(s) -
Min Xiao,
Li Yongming,
Tong Shaocheng
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0187
Subject(s) - control theory (sociology) , active suspension , fuzzy logic , lyapunov function , optimal control , controller (irrigation) , backstepping , fuzzy control system , computer science , inverse dynamics , lyapunov stability , adaptive control , mathematical optimization , actuator , mathematics , nonlinear system , control (management) , artificial intelligence , physics , kinematics , quantum mechanics , classical mechanics , agronomy , biology
In this study, an adaptive fuzzy inverse optimal control problem is investigated for a class of vehicle active suspension systems. Since active suspension systems have dynamic characteristics of complexities and spring non‐linearities, the fuzzy logic systems are utilised to learn the unknown non‐linear dynamics. In addition, there exist the constraints of the displacements of the sprung and unsprung masses, vertical vibration speeds, and current intensity in the considered suspension system, therefore, the Barrier Lyapunov functions are introduced into the control design to ensure that the full‐state constraints are not overstepped. The inverse optimal control method is adopted by constructing an auxiliary system, which circumvents the assignment of solving a Hamilton–Jacobi–Bellman equation and brings about an inverse optimal controller associated with a meaningful objective functional. Based on Lyapunov stability theory and backstepping recursive design algorithm, a fuzzy adaptive optimal control scheme is developed. It is proved that the proposed control scheme not only guarantees that the vertical vibration of the vehicle is stabilised by the electromagnetic actuator but also achieves the goal of inverse optimality with regard to the cost functional. Finally, the simulation studies check the validity of the presented control strategy.