A robust active control system for shimmy damping in the presence of free play and uncertainties. (1st February 2017)

• Record Type: Journal Article
• Authors: Orlando, Calogero; Alaimo, Andrea
• Is Part Of: Mechanical systems and signal processing. Volume 84:Part A(2017)
• Page Start: 551
• Page End: 569
• Publication Date: 2017-02-01
• Subjects: Shimmy vibration -- Active control -- Stochastic robustness -- Swarm optimization; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.07.038
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Shimmy vibration is the oscillatory motion of the fork–wheel assembly about the steering axis. It represents one of the major problem of aircraft landing gear because it can lead to excessive wear, discomfort as well as safety concerns. Based on the nonlinear model of the mechanics of a single wheel nose landing gear (NLG), electromechanical actuator and tire elasticity, a robust active controller capable of damping shimmy vibration is designed and investigated in this study. A novel Decline Population Swarm Optimization ( P D SO ) procedure is introduced and used to select the optimal parameters for the controller. The P D SO procedure is based on a decline demographic model and shows high global search capability with reduced computational costs. The open and closed loop system behavior is analyzed under different case studies of aeronautical interest and the effects of torsional free play on the nose landing gear response are also studied. Plant parameters probabilistic uncertainties are then taken into account to assess the active controller robustness using a stochastic approach. Abstract : Highlights: A nonlinear model based active nose landing gear shimmy damping system is proposed. Torsional free play and electromechanical actuator dynamics are taken into account. A novel optimization procedure called Population Decline Swarm Optimization is proposed. Two probabilistic models for plant uncertainties are proposed. Stochastic robustness analysis is used to assess the controller robustness.