Modelling and Dynamic Tracking Control of Industrial Vehicles with Tractor-trailer Structure

Abstract

Existing works on control of tractor-trailers systems only consider the kinematics model without taking dynamics into account. Also, most of them treat the issue as a pure control theory problem whose solutions are difficult to implement. This paper presents a trajectory tracking control approach for a full-scale industrial tractor-trailers vehicle composed of a carlike tractor and arbitrary number of passive full trailers. To deal with dynamic effects of trailing units, a force sensor is innovatively installed at the connection between the tractor and the first trailer to measure the forces acting on the tractor. The tractor's dynamic model that explicitly accounts for the measured forces is derived. A tracking controller that compensates the pulling/pushing forces in real time and simultaneously drives the system onto desired trajectories is proposed. The propulsion map between throttle opening and the propulsion force is proposed to be modeled with a fifth-order polynomial. The parameters are estimated by fitting experimental data, in order to provide accurate driving force. Stability of the control algorithm is rigorously proved by Lyapunov methods. Experiments of full-size vehicles are conducted to validate the performance of the control approach. arXiv:1909.11897v1 [eess.SY]

Model Predictive Control Based Path Tracking and Velocity Control with Rollover Prevention Function for Autonomous Electric Road Sweeper

“…In this paper, the speed of the front body is used to design the PID controller for the required torque decision. The low-level controllers for steering and velocity tracking are designed as (32). In (32), u γ and T r are the steering moment applied at the center joint and the traction/braking torque, respectively.…”

“…The low-level controllers for steering and velocity tracking are designed as (32). In (32), u γ and T r are the steering moment applied at the center joint and the traction/braking torque, respectively. Since the desired states for the PID controller are bounded and defined by considering the current state of the AERS, the output of the PID controller is calculated without causing a sudden change in the behavior of the vehicle.…”

“…This adopted the Dugoff tire model in calculating the longitudinal and lateral tire forces, which were used to derive the equation of motion for the front and rear bodies [21]. The steering moment generated by the hydraulic cylinder at the center joint was determined by (32), with the consideration of the torsional stiffness and damping. The traction/braking torque of the rear wheel obtained from (32) was applied through the wheel dynamics [21].…”

“…An algorithm for articulated vehicle stability was proposed that determines the target yaw moment with linear Model Predictive Control (MPC) and distributes control effort to brake actuators through another optimization [29]. In contrast to tractor-trailers, the AERS can control the articulation angle and has non-steerable wheels [30][31][32]. Therefore, it is difficult to utilize a controller based on FWS.…”

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