A New Approach to the Robust Control Design of Fuzzy Automated Highway Systems

Abstract

In this paper, a new approach to the decentralized control design for vehicle platooning for uncertain automated highway systems is proposed. The uncertainty in the system, which is nonlinear and (possibly) fast time-varying, is bounded. The bound is assumed to be within a prescribed fuzzy set. A creative transformation is made to the system, which converts a local problem to a global problem. Based on the fuzzy description of the uncertainty bound and the transformation, a class of decentralized control is proposed in which each vehicle only needs the knowledge of its preceding vehicle in the platoon. No acceleration feedback or the information of the leading vehicle is required. Both the vehicle platooning system and the control are deterministic, hence not if-then fuzzy rule-based. The performance of the resulting controlled system is twofold. First, the collision avoidance performance is guaranteed under any safe initial conditions regardless of the value of the uncertainty. Second, the minimization of a fuzzy-based performance index is guaranteed based on an optimal choice of a control design parameter. Numerical simulations are conducted to validate the efficiency of the proposed algorithm.

Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

“…In recent years, the distributed coordinate control theories of multi‐agent systems (MASs) are widely developed, and many of them have been successfully applied to practice areas, for example, satellite formation, 1 automated highway systems, 2 autonomous underwater vehicles, 3 and so forth 4,5 . The distributed consensus problem is the basic and significant issue of cooperative control, and it is also an extensively investigated problem.…”

“…This model is suitable for heterogeneous and time‐varying parameters MASs. Compared with the existing MBC approaches, 1‐26 the model structure is simpler and computation burdens can be significantly reduced. (ii) Only the input and saturated output data are utilized to design the DMFACTC approach, which can reduce each agent's tracking errors to guarantee the tracking performance of MASs under fixed and switching topologies.…”