Fuzzy Backing Control of Truck and Two Trailers

Abstract

For a number of years, truck backer-upper problem has served as a benchmark for control among the practitioners of computational intelligence. In our works from the past we have shown how decomposition of the control problem and subsequent segmentation of the control system leads to substantial control performance improvement. In current paper, this control principle is extended to the much more complex two-trailer backing problem with great success.

Control Design and Assessment for a Reversing Tractor–Trailer System Using a Cascade Controller

“…To achieve such objectives, the vehicle model will be developed in Frenet-Serret Frame {F}, as performed in [14,26], by using the Frenet-Serret formulas. The system equation describing the motion of point M in ( 7) and ( 8) on the tractor and trailer will be rewritten relative to the moving point in {F}, unlike the fixed target developed in [10,12]. At first, kinematics of the moving point P(a) with respect to the path will be explained.…”

“…Figure 3 shows a path 𝐶 with point 𝑃(𝑎) as a target to be tracked by point M, 𝑇(𝑎) is the tangent of the curve at point 𝑃(𝑎), 𝜃 is the angle of the path tangent with respect to the x-axis of the cartesian coordinate, 𝑁(𝑎) is the normal of the curve at point 𝑃(𝑎) and V is the velocity of point M. The rate at which point 𝑃(𝑎) (target point to be tracked) progresses along the path 𝐶 is presented in Equation (11), where 𝑎 denotes the rate of change of curvilinear abscissa of curve 𝑐(𝑎) and 𝑘 is a tunable gain. 𝜃 (t) is the difference between the heading of point M and the tangent of point 𝑃(𝑎) and is also considered as the heading error which is given in Equation (12). The rate at which point P(a) (target point to be tracked) progresses along the path C is presented in Equation (11), where .…”

“…The governor was tested on three different controllers (proportional integral controller, a sliding mode controller and a neural network controller) in [10]. Fuzzy controllers were used in [11][12][13] to control a tractor-trailer, a tractor and two trailers system and a mobile robot with three trailers, respectively, in reverse motion. A simple control law for backing a tractor-trailer used in path following was proposed in [14].…”

Vision-Based Hierarchical Fuzzy Controller and Real Time Results for a Wheeled Autonomous Robot

“…14. Results of the parking maneuver corresponding to the initial configurations (a) x=-20, y=18.4, φ=120°, t=93 steps, (b) x=17.5, y=8, φ=252°, t=86 steps, (Li & Li, 2007) An advantage of this approach is that the rules are linguistically interpretable and the controller generates paths with 8 rules compared with 35 used by (Riid & Rustern, 2002).…”

“…Riid & Rustern (Riid & Rustern, 2001) presented a fuzzy supervisory control system over the PID controller to reduce the complexity of the control problem and enhance the control performance. Riid & Rustern in (Riid & Rustern, 2002) demonstrate that problem decomposition leads to more effective knowledge acquisition and improved control performance in fuzzy control. The methodology allows solving complex control problems (truck backer-upper) without loss of functionality that is very difficult with all-in-one approaches and saves design expenses.…”