title: MCMAC-cVT: a novel on-line associative memory based CVT transmission control system.
abstract: This paper describes a novel application of an associative memory called the Modified Cerebellar Articulation Controller (MCMAC) (Int. J. Artif. Intell. Engng, 10 (1996) 135) in a continuous variable transmission (CVT) control system. It allows the on-line tuning of the associative memory and produces an effective gain-schedule for the automatic selection of the CVT gear ratio. Various control algorithms are investigated to control the CVT gear ratio to maintain the engine speed within a narrow range of efficient operating speed independently of the vehicle velocity. Extensive simulation results are presented to evaluate the control performance of a direct digital PID control algorithm with auto-tuning (Trans. ASME, 64 (1942)) and anti-windup mechanism. In particular, these results are contrasted against the control performance produced using the MCMAC (Int. J. Artif. Intell. Engng, 10 (1996) 135) with momentum, neighborhood learning and Averaged Trapezoidal Output (MCMAC-ATO) as the neural control algorithm for controlling the CVT. Simulation results are presented that show the reduced control fluctuations and improved learning capability of the MCMAC-ATO without incurring greater memory requirement. In particular, MCMAC-ATO is able to learn and control the CVT simultaneously while still maintaining acceptable control performance.
authors.affiliation: Delphi Automotive Systems, Singapore Private Limited, Singapore.
authors.forename: K K
authors.initials: KK
authors.lastname: Ang
authors.forename: C
authors.initials: C
authors.lastname: Quek
authors.forename: A
authors.initials: A
authors.lastname: Wahab
mesh_terms: D000465:Algorithms; D002531:Cerebellum / Q000502:physiology*; D003198:Computer Simulation / Q000706:statistics & numerical data; D019563:Mechanics; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological