# US 11,993,122 B1 · Updating vehicle models for improved suspension control > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,993,122 B1 - Document ID: US11993122B1 - Status: Granted - Kind: B1 - Application number: 17/671,217 - Filed: 2022-02-14 - Granted: 2024-05-28 - Earliest claimed: 2022-02-14 - Assignees: Zoox, Inc. - Applicants: Zoox, Inc. - Inventors: Alexandre Nunes; Johannes Edren - Web page: https://patentgrep.com/patent/US11993122B1 ## CPC - B60G 17/0182: {involving parameter estimation, e.g. observer, Kalman filter} - B60G 17/0165: to an external condition, e.g. rough road surface, side wind - B60G 17/0185: for failure detection - B60G 17/019: characterised by the type of sensor or the arrangement thereof {(B60G17/01941 takes precedence)} - B60G 17/0195: characterised by the regulation being combined with other vehicle control systems {(conjoint control of vehicle sub-units including control of suspension systems B60W10/22)} - B60G 17/06: Characteristics of dampers {, e.g. mechanical dampers}(B60G17/015 takes precedence) - G05B 13/042: {in which a parameter or coefficient is automatically adjusted to optimise the performance} - G07C 5/0808: {Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005)} - B60G 2400/204: Vehicle speed - B60G 2400/252: vertical - B60G 2400/512: in spring - B60G 2400/518: in damper - B60G 2400/60: Load - B60G 2400/821: Uneven, rough road sensing affecting vehicle body vibration - B60G 2500/10: Damping action or damper - B60G 2800/162: Reducing road induced vibrations - B60G 2800/70: Estimating or calculating vehicle parameters or state variables - B60G: VEHICLE SUSPENSION ARRANGEMENTS (air-cushion vehicles B60V; {cycle suspensions B62K25/00}) - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE ## Abstract 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. ## Claims ### Claim 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 mathematical model of the vehicle, the mathematical model being based at least in part on a modal expansion of the vehicle; receiving, from the sensors, sensor data associated with operation of the vehicle; determining, based on the sensor data, one or more updated parameters of the mathematical model of the vehicle, the one or more updated parameters being associated with at least one of mass, damping, or stiffness of the vehicle; and controlling the active suspension system based at least in part on the one or more updated parameters. ### Claim 2 (depends on 1) The vehicle of claim 1, 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. ### Claim 3 (depends on 1) The vehicle of claim 1, the actions further comprising: determining that the one or more updated parameters are within an operational threshold; and operating the vehicle based at least in part on an updated mathematical model of the vehicle determined based at least in part on the updated parameters. ### Claim 4 (depends on 1) The vehicle of claim 1, the actions further comprising: determining that the one or more updated parameters are outside an operational threshold; and identifying an anomalous driving condition based on the one or more updated parameters being outside the operational threshold. ### Claim 5 (depends on 1) The vehicle of claim 1, wherein: the determining the one or more updated parameters of the mathematical model comprises performing a running mode analysis using the sensor data; and the sensor data is generated while the vehicle is travelling on a road surface. ### Claim 6 A method comprising: receiving a mathematical model of a vehicle, the mathematical model being based at least in part on a modal expansion of the vehicle; receiving, from one or more sensors, sensor data associated with operation of the vehicle; determining, based on the sensor data, one or more updated parameters of the mathematical model of the vehicle, the one or more updated parameters being associated with at least one of mass, damping, or stiffness of the vehicle; and controlling the suspension system based at least in part on the one or more updated parameters. ### Claim 7 (depends on 6) The method of claim 6, further comprising: comparing the updated parameters to an operational threshold. ### Claim 8 (depends on 7) The method of claim 7, further comprising: generating, based at least in part on the updated parameters being within the operational threshold, an updated mathematical model of the vehicle using the updated parameters. ### Claim 9 (depends on 8) The method of claim 8, further comprising: determining, during operation of the vehicle, a state of the vehicle; and generating, based at least in part on the updated mathematical model of the vehicle and the state of the vehicle, a predicted future state of the vehicle. ### Claim 10 (depends on 9) The method of claim 9, further comprising: determining, based at least in part on the predicted future state of the vehicle, a control command for a suspension system of the vehicle; and controlling an aspect of the suspension system of the vehicle based at least in art on the control command. ### Claim 11 (depends on 7) The method of claim 7, further comprising: identifying, based at least in part on the updated parameters being outside of the operational threshold, an anomalous condition for the vehicle. ### Claim 12 (depends on 11) The method of claim 11, further comprising: diagnosing the anomalous condition as at least one of a malfunctioning or worn component associated with a suspension system of the vehicle. ### Claim 13 (depends on 6) The method of claim 6, wherein the determining at least one of the model parameters or the updated parameters comprises performing a running mode analysis. ### Claim 14 (depends on 13) The method of claim 13, wherein the running mode analysis is based at least in part on the sensor data generated during operation of the vehicle. ### Claim 15 (depends on 6) The method of claim 6, 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. ### Claim 16 A vehicle comprising: a 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 mathematical model of the vehicle, the mathematical model being based at least in part on a modal expansion of the vehicle; receiving, from the sensors, sensor data associated with operation of the vehicle; determining, based on the sensor data, one or more updated parameters of the mathematical model of the vehicle, the one or more updated parameters being associated with at least one of mass, damping, or stiffness of the vehicle; and controlling the suspension system based at least in part on the one or more updated parameters. ### Claim 17 (depends on 16) The vehicle of claim 16, wherein the one or more updated parameters are associated with at least one of a mass of the vehicle, damping characteristics of the vehicle, or stiffness characteristics of the vehicle, the actions further comprising: comparing the updated parameters to an operational threshold. ### Claim 18 (depends on 17) The vehicle of claim 17, the actions further comprising: generating, based at least in part on the one or more updated parameters being within the operational threshold, an updated mathematical model of the vehicle using the one or more updated parameters. ### Claim 19 (depends on 18) The vehicle of claim 18, the actions further comprising: determining, during operation of the vehicle, a state of the vehicle; and generating, based at least in part on the updated mathematical model of the vehicle, a predicted future state of the vehicle, wherein the controlling the suspension system comprises controlling the suspension system based on the predicted future state of the vehicle. ### Claim 20 (depends on 17) The vehicle of claim 17, the actions further comprising: identifying, based at least in part on the one or more updated parameters being outside of the operational threshold, an anomalous condition for the vehicle.