Multiple model active suspension control
An active suspension control system for a vehicle includes a mathematical model based on a modal expansion of the vehicle. Model parameters of the vehicle can be extracted from the modal expansion using sensor data generated on the vehicle, e.g., on demand and/or in real time. The model parameters and the modal expansion can be used to determine a vehicle state, predict future vehicle states, and control aspects of an active suspension system based on the predicted future vehicle states. The model parameters may also be used to update the mathematical model, e.g., to account for component wear over time, and/or to detect anomalies or defects in the active suspension system.
1. A vehicle comprising:
an active suspension system;
sensors;
one or more processors; and
non-transitory computer readable media storing one or more instructions implemented by the one or more processors to perform actions comprising:
receiving a first mathematical model of the vehicle, the first mathematical model being based at least in part on a modal expansion of the vehicle;
receiving a second mathematical model of the vehicle, the second mathematical model being based at least in part on a kinematic model of the vehicle;
receiving, from a sensor on the vehicle, sensor data about the vehicle;
determining, based at least in part on the sensor data, an estimation of a current state of the vehicle;
determining, based at least in part on the estimation and the first mathematical model, a first predicted future state of the vehicle;
determining, based at least in part on the estimation and the second mathematical model, a second predicted future state of the vehicle;
determining a command for controlling the active suspension system based on the first predicted future state and the second predicted future state; and
controlling the active suspension system based at least in part on the command.
2. The vehicle of claim 1 , wherein:
the determining the command for controlling the active suspension system comprises determining a global optimization based on the first predicted future state over a first time period and determining a local optimization based on the second predicted future state over a second time period shorter than the first time period.
3. The vehicle of claim 2 , wherein the second time period corresponds to an actuation cycle of the vehicle and the first time period is equal to five or more actuation cycles.
4. The vehicle of claim 1 , the actions further comprising:
determining a first weighting factor for the first predicted future state and a second weighting factor for the second predicted future state; and
determining the command based at least in part on the first weighting factor and the second weighting factor.
5. The vehicle of claim 4 , wherein the first weighting factor corresponds to a periodic excitation of the active suspension system and the second weighting factor corresponds to an impulse or steady state excitation of the active suspension system.
6. The vehicle of claim 1 , wherein the first predicted future state or the second predicted future state is determined based at least in part on an expected excitation of the active suspension system as a result of a future state of the vehicle resulting from use of an autonomous planning component of the vehicle.
7. A method comprising:
receiving a first mathematical model of a vehicle, the first mathematical model being based at least in part on a modal expansion of the vehicle;
receiving a second mathematical model of the vehicle;
determining, based at least in part on the first mathematical model and a current state of the vehicle, a first predicted future state of the vehicle;
determining, based at least in part on the second mathematical model and the current state of the vehicle, a second predicted future state of the vehicle,
determining a command for controlling an active suspension system based on the first predicted future state and the second predicted future state; and
controlling the active suspension system based at least in part on the command.
8. The method of claim 7 , wherein:
the determining the first predicted future state comprises propagating the current state of the vehicle for a first time period; and
the determining the second predicted future state comprises propagating the current state of the vehicle for a second time period shorter than the first time period.
9. The method of claim 8 , wherein:
the second time period corresponds to an actuation cycle of the active suspension of the vehicle; and
the first time period corresponds to five or more actuation cycles of the active suspension system of the vehicle.
10. The method of claim 7 , further comprising:
determining a first weighting factor for the first predicted future state;
determining a second weighting factor for the second predicted future state; and
determining the command based at least in part on the first weighting factor and the second weighting factor.
11. The method of claim 10 , wherein the determining the first weighting factor corresponds to a periodic excitation of the vehicle and the second weighting factor corresponds to an impulse or steady state excitation of the vehicle.
12. The method of claim 11 , wherein:
the first weighting factor is relatively higher corresponding to an increased periodic excitation of the vehicle; and
the second weighting factor is relatively higher corresponding to an increased impulse or steady state excitation of the vehicle.
13. The method of claim 11 , further comprising:
receiving information about at least one of a road characteristic or vehicle input data associated with at least one of a torque command, a braking command, or a steering command,
wherein the periodic excitation or the impulse or steady state excitation is an expected excitation based at least in part on the road characteristic or the vehicle input data.
14. The method of claim 11 , further comprising:
receiving information about an object in an environment of the vehicle,
wherein the second weighting factor is relatively higher when the object in the environment impacts travel of the vehicle.
15. The method of claim 11 , wherein the modal expansion of the vehicle comprises at least one of node shape vectors associated with natural modes of the vehicle or time-dependent contribution vectors associated with the natural modes.
16. The method of claim 15 , further comprising:
determining the node shape vectors or the time-dependent contribution vectors; and
extracting model parameters associated with the at least one of a mass, damping characteristics, or stiffness characteristics of the vehicle based on the node shape vectors or the time-dependent contribution vectors.
17. A vehicle comprising:
an active suspension system;
one or more processors; and
non-transitory computer readable media storing one or more instructions implemented by the one or more processors to perform actions comprising:
receiving a first mathematical model of the vehicle, the first mathematical model being based at least in part on a modal expansion of the vehicle;
receiving a second mathematical model of the vehicle;
determining, based at least in part on the first mathematical model and a current state of the vehicle, a first predicted future state of the vehicle;
determining, based at least in part on the second mathematical model and the current state of the vehicle, a second predicted future state of the vehicle,
determining a command for controlling an active suspension system of the vehicle based on the first predicted future state and the second predicted future state; and
controlling the active suspension system of the vehicle based at least in part on the command.
18. The vehicle of claim 17 , wherein:
the determining the first predicted future state comprises propagating the current state of the vehicle for a first time period; and
the determining the second predicted future state comprises propagating the current state of the vehicle for a second time period shorter than the first time period.
19. The vehicle of claim 17 , the actions further comprising:
determining a first weighting factor for the first predicted future state;
determining a second weighting factor for the second predicted future state; and
determining the command based at least in part on the first weighting factor and the second weighting factor.
20. The vehicle of claim 19 , wherein the determining the first weighting factor corresponds to a periodic excitation of the vehicle and the second weighting factor corresponds to an impulse or steady state excitation of the vehicle.