Systems, methods, and computer-readable media for controlling suspension of vehicle

• US 8,731,774 B2
• Filed: 11/19/2012
• Issued: 05/20/2014
• Est. Priority Date: 04/02/2012
• Status: Active Grant

• Current Assignee: Hyundai Motor Company (Hyundai Motor Group)
• Original Assignee: Hyundai Motor Company (Hyundai Motor Group)
• Inventors: Yang, Woo Sung
• Primary Examiner(s): Cheung, Mary
• Assistant Examiner(s): WONG, YUEN H
• Application Number:
  US13/680,678
• Publication Number:
  US 20130261893A1
• Time in Patent Office: 547 Days
• Field of Search: 701/38, 701/40
• US Class Current: 701/38
• CPC Class Codes: B60G 17/016 characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input B60G17/017 takes precedence
• CPC Class Codes: B60G 17/0165 to an external condition, e.g. rough road surface, side wind
• CPC Class Codes: B60G 2500/10 Damping action or damper
• CPC Class Codes: B60G 2500/20 Spring action or springs

Abstract

The present invention provides systems, methods, and computer-readable media for controlling a suspension for a vehicle in which a platform model and a suspension model are provided and a first neural oscillator model and a second neural oscillator model that feed back an output to each model to acquire displacements of the platform model and the suspension model from displacements inputted into a tire, acquire feedbacks of the first neural oscillator model and the second neural oscillator model from the displacements of the platform model and the suspension model, and acquire pressing force required in the suspension for the vehicle from the feedback of the second neural oscillator model.

Claims

1. A system for controlling a suspension for a vehicle, the system comprising:

a controller programmed to implement;

a platform model;

a suspension model;

a first neural oscillator model; and

a second neural oscillator model;

wherein the platform model and the suspension model are expressed by virtual mass-spring-damper models;

wherein the first neural oscillator model and the second neural oscillator model feed back an output to each model to;

acquire displacements of the platform model and the suspension model from displacements inputted into a tire;

acquire feedbacks of the first neural oscillator model and the second neural oscillator model from the displacements of the platform model and the suspension model; and

acquire pressing force required in the suspension for the vehicle from the feedback of the second neural oscillator model,

wherein each of the first neural oscillator model and the second neural oscillator model feeds back a cyclic signal by an extensor neuron and a flexor neuron in accordance with equations

2. The system for controlling a suspension for a vehicle of claim 1, wherein the platform model is expressed by equation

m_t{umlaut over (x)}_t+c_t{dot over (x)}_t+(k_t+k_c)x_t−

k_cx_s=k_o1x_o1

and the suspension model is expressed by equation

m_s{umlaut over (x)}_s+c_s{dot over (x)}_s+k_cx_t−

(k_s+k_c)x_s=k_o2x_o2

where m_t, c_t, k_r, and x_t represent mass, a damping coefficient, a spring constant, and a displacement of the platform model, respectively, m_s, c_s, k_s, and x_s represent mass, a damping coefficient, a spring constant, and a displacement of the suspension model, respectively, x_o1 and x_o2 represent feedbacks of the first neural oscillator model and the second neural oscillator model, respectively, and k_o1, k_o2, and k represent predetermined coefficients for expressing the displacements as force.

3. The system for controlling a suspension for a vehicle of claim 1, wherein the displacement of the platform model is inputted into the first neural oscillator model, the feedback of the first neural oscillator model and the displacement of the suspension model are inputted into the second neural oscillator model, and the pressing force required for the suspension of the vehicle is acquired from the feedback of the second neural oscillator model.

4. The system for controlling a suspension for a vehicle of claim 3, wherein the feedback of the second neural oscillator model is inputted into the first neural oscillator model together with the displacement of the platform model.

5. A method for controlling a suspension for a vehicle, the method comprising:

acquiring displacements of a platform model and a suspension model from displacements inputted from the platform model with use of a controller and the suspension model expressed by virtual mass-spring-damper models into a tire;

inputting the displacement of the platform model into a first neural oscillator model with use of the controller that feeds back an output to the platform model;

inputting the feedback of the first neural oscillator model and the displacement of the suspension model to a second neural oscillator model that feeds back the output to the suspension model with use of the controller; and

acquiring pressing force required for the suspension of the vehicle from the feedback of the second neural oscillator model,

wherein each of the first neural oscillator model and the second neural oscillator model feeds back a cyclic signal by an extensor neuron and a flexor neuron in accordance with equations

6. The method for controlling a suspension for a vehicle of claim 5, wherein the step of acquiring pressing force further includes inputting the feedback of the second neural oscillator model into the first neural oscillator model together with the displacement of the platform model.

7. A non-transitory computer readable medium containing program instructions executed by a processor or controller, the computer readable medium comprising:

program instructions that acquire displacements of a platform model and a suspension model from displacements inputted from the platform model and the suspension model expressed by virtual mass-spring-damper models into a tire;

program instructions that input the displacement of the platform model into a first neural oscillator model that feeds back an output to the platform model;

program instructions that input the feedback of the first neural oscillator model and the displacement of the suspension model to a second neural oscillator model that feeds back the output to the suspension model; and

program instructions that acquire pressing force required for the suspension of the vehicle from the feedback of the second neural oscillator model,

wherein each of the first neural oscillator model and the second neural oscillator model feeds back a cyclic signal by an extensor neuron and a flexor neuron in accordance with equations