Nonlinear Model Predictive Tracking Control With C/GMRES Method for Heavy-Duty AGVs
Xingjie Liu, Guolei Wang, Ken Chen
Abstract
To ensure trajectory tracking accuracy and driving safety, this paper presents a real time nonlinear model predictive control (NMPC) method for autonomous guided vehicles (AGVs) to avoid dangerous load transfer effect in carrying heavy equipment. The AGV with a four-wheel-steering four-wheel-driving (4WS4WD) structure is controlled with a steer and velocity input for each wheel. The tracking problem is implemented by the standard NMPC framework and the referenced trajectory is planned by the Reeds-Shepp algorithm. A Large load creates a considerable load transfer effect which is dangerous for the wheels; hence, the controller limits the lateral acceleration to reduce load transfer. To realize online numerical computation for NMPC, the continuation/generalized minimal residual (C/GMRES) method is applied. A semismooth transformation based on the Chen-Harker-Kanzow-Smale (CHKS) function is introduced to handle inequality constraints since C/GMRES only solves standard equality forms. Trajectory tracking simulations are carried out in contrast to the quadratic programming method without lateral load transfer optimization, and practical experiments on an AGV carrying 13 tons of steel are also performed. The results indicate the accuracy, efficiency, safety and applicability of the proposed method on heavy-duty AGVs.