B2, an alternative two wheeled vehicle for an automated urban transportation system

Pedestrian controlled uniaxial vehicles for goods transport such as hand trucks offer intuitive manoeuvrability with little space requirements. The disadvantage of this class of statically underdetermined vehicles is the need for the user to apply force for both stabilisation and propulsion. Removing this disadvantage is the key to enable their use for convenient transportation of heavy goods over long distances. For this our solution approach is to equip the vehicle with controlled drives providing force for both balancing and propulsion. Different to uniaxial vehicles without payload or for passenger transportation controller's adaptation to kinematic and dynamic parameters is required here after every reload. The frame's pitch angle must be adapted with every change of load whereby the COG's position is not measurable with acceptable effort. Dynamic parameters vary in a wide range. We applied an adaptive controller based on backstepping combined with Kalman state estimation for stable balancing with adaptation to changed payload without need for external support from the user. The manoeuvre concept based on low interaction forces applied from the user to the vehicle frame is taking advantage of the underconstrained dynamics of the vehicle, and offers similar driving behaviour to the user with different load situations. Operating control levers for setting speed is not required. To allow for cheap production a design goal was not requiring sensor information of both load's absolute weight and user interaction forces. The adaptive control and state estimation concept was simulated based on the kinematic and dynamic model of our new uniaxial vehicle system designed for urban parcel distribution on foot. Simulation results show the correct operation of the approach

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“…Beside relatively specific solutions such as adaptive fuzzy logic [7], [6] or adaptive neural network [5] backstepping is a common approach.…”

Section: Approaches For Parameter Adaptive Stabilisation Of Uniaxmentioning

confidence: 99%

Adaptive backstepping controller with Kalman state estimator for stabilisation and manoeuvre of pedestrian controlled uniaxial transport vehicles

Brüning

Thiele

Schönewolf

et al. 2015

2015 IEEE International Conference on Mechatronics (ICM)

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“…These are for instance based on adaptive neural network [3], H ∞ control [4], state feedback control [5] as well as support vector machine (SVM) [6] and adaptive fuzzy logic [7], [5], often based on Lyapunov stability analysis.…”

Section: B Experimental Vehiclementioning

confidence: 99%

Freight transport system for urban shipment and delivery

Brüning

Schönewolf

2011

2011 IEEE Forum on Integrated and Sustainable Transportation Systems

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Many problems with present conventional delivery scenarios in urban areas are caused by use of conventional vehicles. These problems are amongst others limited efficiency, ecological damages, disturbance of moving traffic and pedestrians and difficulties to comply with environmental and traffic regulations, affecting local communities, business people and their customers as well as the logistic companies. Courier, Express & Parcel (CEP) providers are obliged to use conventional cars and vans due to the lack of specialized solutions. Therefore our approach is improvement of urban shipment and delivery based on existing infrastructure by means of an innovative vehicle concept. Core of the developed transport system is a uniaxial transport unit, designed for application both as part of a multiple trailer vehicle and for use as separate hand-guided transportation unit. The concept was developed based on a catalog of requirements for a vehicle for urban shipment and delivery, a result from the EU research project FIDEUS. The developed concept satisfies all formulated demands. A test vehicle of the transport system was built. Current research focuses on controller synthesis and implementation. The next step intended is execution of field tests involving logistic companies to prove the vehicle system's suitability for daily use and to discover potential for improvement

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“…Thus, controlling navigation is not the only requirement; balancing the pendulum of the MIP is also needed. Successful studies on the mobile pendulum systems have been presented [5][6][7][8][9][10]. Control algorithms have been designed based on the modeling of the MIP.…”

Section: Introductionmentioning

confidence: 99%

Position control of a mobile inverted pendulum system using radial basis function network

Noh

Lee

Jung

2010

Int. J. Control Autom. Syst.

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This article presents the implementation of position control of a mobile inverted pendulum (MIP) system by using the radial basis function (RBF) network. The MIP has two wheels to move on the plane and to balance the pendulum. The MIP is a nonlinear system whose dynamics is nonholonomic. The goal of this study was to control the MIP to maintain the balance of the pendulum while tracking a desired position of the cart. The reference compensation technique scheme is used as a neural network control method for the MIP. The back-propagation learning algorithm of the RBF network is derived for online learning and control. The control algorithm has been embedded on a DSP 2812 board to achieve real-time control. Experimental results are conducted and show successful control performances of both balancing and tracking the desired position of the MIP.

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B2, an alternative two wheeled vehicle for an automated urban transportation system

Cited by 41 publications

References 13 publications

Adaptive backstepping controller with Kalman state estimator for stabilisation and manoeuvre of pedestrian controlled uniaxial transport vehicles

Adaptive backstepping controller with Kalman state estimator for stabilisation and manoeuvre of pedestrian controlled uniaxial transport vehicles

Freight transport system for urban shipment and delivery

Position control of a mobile inverted pendulum system using radial basis function network

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