Adaptive model-based velocity control by a robotic driver for vehicles on roller dynamometers

Abstract

This paper presents an adaptation algorithm for a model-based velocity control of a vehicle driven by a robotic driver. In order to ensure that a robotic driver follows a desired velocity trajectory with an arbitrary vehicle, the overlaid controls must be robust as well as highly accurate.Controllers of existing robotic drivers must be adjusted manually or several learning cycles have to be driven. As each learning cycle is very time-consuming and the vehicle has to be conditioned again, a self-adaptation of the applied controls during normal cycle driving is proposed in this paper. This adaptive control depends only on little previous knowledge for initialization and yields a significant improvement of the accuracy already after a short time of driving.Results of the adaptive control both from simulations and from measurements on a roller dynamometer are shown.

Research of individual factors affecting the engine power while a passenger car operation

“…One cylinder on a wheel enables to execute a long-lasting test without any risk of wheel damage. While testing, a car is in considerably higher position compared to the laboratory surface, and due to this aspect car may be cool down by flowing air more effectively [9,13].…”

Driving Robot for Reproducible Testing: A Novel Combination of Pedal and Steering Robot on a Steerable Vehicle Test Bench

“…Another advantage is to save costs, since no human test drivers are necessary. Further advantages are the relief of the human during demanding tests, such as endurance or climatic tests [13][14][15], or the improved ability to analyze data due to more reproducible driving [16]. In 1998, the German and US automotive industries defined requirements for pedal robots.…”

“…Since teach-in phases are time-consuming [1], the authors in [16] implemented a self-learning system for learning the vehicle behavior. The authors in [13,[17][18][19] alternatively designed a universal vehicle model for the longitudinal control. The required parameterization process with common vehicle parameters reduces the teach-in phase to referencing the actuators when the motor is switched off [12].…”

“…The required parameterization process with common vehicle parameters reduces the teach-in phase to referencing the actuators when the motor is switched off [12]. In this vehicle model [13,[17][18][19]], the braking system was not modeled, since the behavior of the composition of braking force support, ABS and ESP is different for each vehicle. However, the authors noted that brake pedals always have an idle distance and decelerations <0.5 m/s 2 mostly result from drag torque.…”

Derivation of the Reference Speed Waveform Considering the Low Fuel Consumption for a Robotic Driver

“…) = v(t − ∆t) + a(t)∆t(6) ここで，a(t) は加速度，∆t はサンプル時間である． 本研究で提案する評価関数 J(t) を (7) 式に示す．B(t) は (8) 式で示す許容範囲からの逸脱を防ぐためのバリア 関数であり，B est (t) は (10) 式に示す算出した加速度に おいて 1 秒先の車速(以下，先読み車速と記す)を見た 場合，逸脱しないようにするためのバリア関数である． バリア関数を設定することで，許容領域の境界へ近づく ほど評価関数値が増加し逸脱を防止することができる． J(t) = W (t) 2 + B(t)+ B est (t)…”