METHOD FOR CONTROLLING A TORQUE OF AT LEAST ONE WHEEL

Abstract

A method for controlling a torque of at least one wheel of a mobile platform. The method includes: providing at least one current slip value of the wheel and at least one current wheel acceleration of the wheel as input values; providing a trained radial basis function network designed to determine, by means of the input values, at least one torque change as an output value for control of the at least one wheel; and determining a current torque change, by means of the trained radial basis function network and the provided input values, for control of the torque.

Claims

1-15. (canceled)

16. A method for controlling a torque of at least one wheel of a mobile platform, comprising the following steps: providing, as input values, at least one current slip value of the wheel and at least one current wheel acceleration of the wheel; providing a trained radial basis function network configured to determine, using the input values, at least one torque change as an output value for control of the at least one wheel; and determining a current torque change for controlling the torque, using the trained radial basis function network and the provided input values.

17. The method according to claim 16, wherein the provided input values for the trained radial basis function network to determine the current torque change additionally include a first sequence of previous values of a normal force of the wheel and a second sequence of previous torque values.

18. The method according to claim 16, wherein the provided input values for the trained radial basis function network include the current slip value of the wheel and the current wheel acceleration of the wheel and a first sequence of previous values of a normal force of the wheel and a second sequence of previous torque values, and wherein the current torque change is determined by using the radial basis function network trained using the same types of values as the current slip value of the wheel and the current wheel acceleration of the wheel and the first sequence of previous values of a normal force of the wheel and the second sequence of previous torque values.

19. The method according to claim 16, wherein the input values include: a current coefficient of friction of the wheel and/or a current torque of the wheel and/or a running average of the torque of the wheel and/or a current change in torque over time and/or a gradient of the torque of the wheel; and/or an average of the torque of the wheel and/or a current torque of at least one other wheel of the mobile platform and/or a normal force of at least the wheel and a normal force of at least one other wheel of the platform and/or a difference between the current slip and a target slip and/or dynamic values of the mobile platform and/or a current change in wheel acceleration over time and/or a normal force of at least one other wheel of the mobile platform and/or a current slip value and at least one current wheel acceleration of another wheel of the platform.

20. The method according to claim 16, wherein the provided trained radial basis function network is configured to additionally determine, using the input values, a change in engine torque as the output value for control of the torque of the at least one wheel, and the trained radial basis function network also determines the change in the engine torque as the output value for control of the torque of the at least one wheel.

21. A method for training a radial basis function network for estimating a torque change of at least one wheel of a mobile platform, the method comprising the following steps: providing a slip value of the wheel; and providing a wheel acceleration of the wheel associated with the slip value; forming input values for the radial basis function network, using the slip value and the associated wheel acceleration; providing and assigning a target torque change associated with the input values; training the radial basis function network with a plurality of different input values and the assigned target torque change for estimating the torque change using the input values.

22. The method according to claim 21, wherein the provided input values for training the radial basis function network additionally include a first sequence of previous values of a normal force of the wheel and a second sequence of previous torque values.

23. The method according to claim 21, wherein the provided input values for training the radial basis function network include a current slip value of the wheel and a current wheel acceleration of the wheel and a first sequence of previous values of a normal force of the wheel and a second sequence of previous torque values.

24. The method according to claim 22, wherein each of the input values are additionally assigned an associated target change in an engine torque, and the radial basis function network is trained with a plurality of different input values and the assigned target torque changes and the assigned target change in the engine torque.

25. The method according to claim 22, wherein: the radial basis function network is provided with at least some of the same types of values as a current coefficient of friction of the wheel and/or a current torque of the wheel and/or a running average of the torque of the wheel and/or a current change in torque over time and/or a gradient of the torque of the wheel; and/or an average of the torque of the wheel and/or a current torque of at least one other wheel of the mobile platform and/or a normal force of at least the wheel and a normal force of at least one other wheel of the platform and/or a difference between the current slip and a target slip and/or dynamic values of the mobile platform and/or a current change in wheel acceleration over time and/or a normal force of at least one other wheel of the mobile platform and/or a current slip value and at least one current wheel acceleration of another wheel of the platform; and the radial basis function network is trained with a plurality of the input values and the assigned target torque changes and/or target change in the engine torques to estimate the torque change and/or the change in the engine torque.

26. The method according to claim 22, wherein the radial basis function network is configured based on expert knowledge and/or physical boundary values prior to training to achieve improved training.

27. The method according to claim 26, wherein the radial basis function network is configured on the expert knowledge using an arrangement of centers of the radial basis function network in a state space and/or using a weighting of a plurality of centers of the radial basis function network.

28. The method as recited in claim 16, wherein the method is used to calibrating a torque controller of a mobile platform.

29. A control device configured to control a torque of at least one wheel of a mobile platform, the control device configured to: provide, as input values, at least one current slip value of the wheel and at least one current wheel acceleration of the wheel; provide a trained radial basis function network configured to determine, using the input values, at least one torque change as an output value for control of the at least one wheel; and determine a current torque change for controlling the torque, using the trained radial basis function network and the provided input values.

30. The control device as recited in claim 29, wherein the control device is configured for traction control of at least one wheel of the mobile platform.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Exemplary embodiments of the present invention are shown with reference to FIG. 1 and will be explained in more detail in the following.

[0065] FIG. 1 shows a structure of a radial basis function network, according to an example embodiment of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0066] FIG. 1 is a schematic sketch of a structure of a radial basis function network 100 comprising an input layer 110a-d, a layer of hidden neurons 120a-f and an RBF activation function or transfer function 130a-f and an output layer 150. The different neurons 120a-f contribute to the output layer 150 according to weights wl to w6 140a-f.

[0067] Such a radial basis function network 100 can be used for controlling a torque in that the radial basis function network 100 estimates a torque change 160 with input values.

[0068] To control a torque of at least one wheel of a mobile platform, a current slip value of the wheel and a current wheel acceleration of the wheel and a first sequence of previous values of a normal force of the wheel and a second sequence of previous torque values can be applied to the input layer 110a-d of the trained radial basis function network 100 as input values. The radial basis function network 100 trained with the input values 110a-d uses the applied input values to estimate a current torque change 160 for controlling the torque.

[0069] For this purpose, all of the input values 110a-d are normalized to a range of zero to one in one step.

[0070] In a further step, the distance between the signals of the input values and the neurons is calculated, wherein each neuron represents a specific point in a state space. A Euclidean distance metric is used to determine the distance. Alternatively or additionally, other distance metrics, such as an L1 distance metric, can be used as well.

[0071] The output of each neuron is a distance between a current state, namely the current input signal at time t, and the specific center of the neuron.

[0072] In a further step, the distance is transferred to the output layer 160 by means of the radial basis function f. This radial basis function f can be any function. The radial basis function can be a Gaussian distribution function, for example, that returns a high value when the input is close to zero and a low value when the input is high. The input in this case is the distance to the center. In other words, an output of each neuron is transformed with the radial basis function and, according to a linear regression, multiplied by a specific weight w.sub.n 140a-f and summed to estimate the torque change 160.