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This paper addresses the problem of stabilizing an electric cargo bike. For most control objectives, it suffices to consider a cargo bike as a two-wheeler. However, in addition to the challenges posed to the control of traditional two-wheelers, electric cargo bikes also have the issue of the cargo load, which can significantly influence the driving behaviour. Hence, detection and estimation of the mass, position and inertial properties of the cargo load become important. Here, a Kalman filter based algorithm which estimates these parameters online is presented. For the estimation, measurements of the force exerted by the load are recorded using force sensors installed under the load. Along with these, roll angle and roll acceleration are also measured. The estimated values are then used by an adaptive model predictive controller (MPC) to adjust the model-parameters and stabilize a cargo bike while following a set trajectory.

Adaptive model predictive stabilization of an electric cargo bike using a cargo load moment of inertia estimator