AI-Hydro-Pneumatic Suspension Control Development
Budget: ₹75,000 – ₹150,000 INR
Scope of Work
AI/ML-Based Predictive Active Hydro-Pneumatic Suspension Control Software Development
Project Overview
We are developing an Active Hydro-Pneumatic Suspension System for a heavy off-road defence/mining vehicle. The mechanical suspension, hydraulic system, proportional valves, ECU hardware, and sensors are being developed in-house.
The objective of this project is to develop an AI/ML-based suspension control algorithm capable of operating in two configurations:
Configuration A: Accelerometer-based Active Suspension Control.
Configuration B (Optional): LiDAR-assisted Predictive Active Suspension Control.
The software shall automatically control the proportional hydraulic valves to achieve optimum ride comfort, vehicle stability, and suspension performance.
Scope of Development
Phase 1 – Sensor Integration
Develop software interfaces for the following sensors:
Mandatory
3-axis Accelerometer
Gas Pressure Sensor (Hydro-Pneumatic Suspension)
Suspension Position / Displacement Sensor
Vehicle Speed Signal
Optional
Steering Angle Sensor
LiDAR Sensor (for predictive mode)
Note: Hydraulic pressure sensors, GPS and RGB cameras are not part of the present scope.
Phase 2 – Active Suspension Control
Develop the suspension controller capable of:
Processing accelerometer signals
Estimating road-induced vibration
Determining suspension response requirements
Controlling proportional hydraulic valves
Regulating valve opening and closing continuously
Optimizing damping characteristics
Minimizing vertical acceleration transmitted to the vehicle body
The controller shall support multiple terrain conditions while maintaining ride comfort and stability.
Phase 3 – Predictive Suspension (Optional)
If LiDAR is available:
Develop predictive algorithms capable of:
Reading terrain profile ahead of the vehicle
Predicting wheel impact
Pre-adjusting proportional valve opening
Optimizing damping before obstacle impact
The software architecture shall be modular so that LiDAR functionality can be enabled or disabled without affecting the accelerometer-based control strategy.
Phase 4 – AI/ML-Based Adaptive Learning
Develop machine learning algorithms capable of learning from:
Suspension displacement
Vehicle acceleration
Vehicle speed
Gas pressure
Driver inputs (if steering signal available)
The controller shall progressively improve suspension performance by adapting valve control parameters under varying operating conditions.
Phase 5 – Suspension Health Diagnostics
Develop intelligent diagnostic algorithms capable of detecting:
Gas leakage
Proportional valve degradation
Sensor failures
Abnormal damping characteristics
Seal wear
The system shall generate diagnostic warnings and maintenance recommendations based on observed suspension behaviour.
Phase 6 – Control Strategy
Develop software capable of:
Real-time proportional valve control
Adaptive damping control
Ride comfort optimization
Roll mitigation (using available sensors)
Pitch mitigation (using available sensors)
Fail-safe operation during sensor failure
Manual tuning of controller parameters
Phase 7 – User Interface
Develop a graphical interface displaying:
Vehicle acceleration
Suspension displacement
Gas pressure
Valve command
Suspension operating mode
Diagnostic status
Alarm messages
Historical performance trends
Deliverables
The selected developer/team shall provide:
Complete source code
AI/ML models
Control algorithms
Embedded software
GUI software
Documentation
Installation guide
Parameter tuning guide
Testing report
Support during integration with our hardware
Preferred Technical Skills
Vehicle Dynamics
Active Suspension Systems
Control Systems
Machine Learning
Signal Processing
Embedded Systems
Python
C/C++
MATLAB/Simulink (preferred)
CAN Communication
Real-Time Control Systems
Additional Recommendations
Preference will be given to AI/ML companies, university research groups, or multidisciplinary engineering teams with proven experience in:
Active or Semi-Active Suspension Systems
Vehicle Dynamics and Chassis Control
Automotive Control Algorithms
Heavy Off-Road Vehicles
Mining Equipment
Defence Mobility Platforms
Robotics and Autonomous Ground Vehicles
Real-Time Embedded Control Systems
AI/ML applications in mechatronic systems
Applicants should demonstrate previous work in control systems, embedded AI, or intelligent vehicle technologies. Experience in simulation tools such as MATLAB/Simulink, CarSim, AMESim, or similar vehicle dynamics software will be considered an advantage
AI/ML-Based Predictive Active Hydro-Pneumatic Suspension Control Software Development
Project Overview
We are developing an Active Hydro-Pneumatic Suspension System for a heavy off-road defence/mining vehicle. The mechanical suspension, hydraulic system, proportional valves, ECU hardware, and sensors are being developed in-house.
The objective of this project is to develop an AI/ML-based suspension control algorithm capable of operating in two configurations:
Configuration A: Accelerometer-based Active Suspension Control.
Configuration B (Optional): LiDAR-assisted Predictive Active Suspension Control.
The software shall automatically control the proportional hydraulic valves to achieve optimum ride comfort, vehicle stability, and suspension performance.
Scope of Development
Phase 1 – Sensor Integration
Develop software interfaces for the following sensors:
Mandatory
3-axis Accelerometer
Gas Pressure Sensor (Hydro-Pneumatic Suspension)
Suspension Position / Displacement Sensor
Vehicle Speed Signal
Optional
Steering Angle Sensor
LiDAR Sensor (for predictive mode)
Note: Hydraulic pressure sensors, GPS and RGB cameras are not part of the present scope.
Phase 2 – Active Suspension Control
Develop the suspension controller capable of:
Processing accelerometer signals
Estimating road-induced vibration
Determining suspension response requirements
Controlling proportional hydraulic valves
Regulating valve opening and closing continuously
Optimizing damping characteristics
Minimizing vertical acceleration transmitted to the vehicle body
The controller shall support multiple terrain conditions while maintaining ride comfort and stability.
Phase 3 – Predictive Suspension (Optional)
If LiDAR is available:
Develop predictive algorithms capable of:
Reading terrain profile ahead of the vehicle
Predicting wheel impact
Pre-adjusting proportional valve opening
Optimizing damping before obstacle impact
The software architecture shall be modular so that LiDAR functionality can be enabled or disabled without affecting the accelerometer-based control strategy.
Phase 4 – AI/ML-Based Adaptive Learning
Develop machine learning algorithms capable of learning from:
Suspension displacement
Vehicle acceleration
Vehicle speed
Gas pressure
Driver inputs (if steering signal available)
The controller shall progressively improve suspension performance by adapting valve control parameters under varying operating conditions.
Phase 5 – Suspension Health Diagnostics
Develop intelligent diagnostic algorithms capable of detecting:
Gas leakage
Proportional valve degradation
Sensor failures
Abnormal damping characteristics
Seal wear
The system shall generate diagnostic warnings and maintenance recommendations based on observed suspension behaviour.
Phase 6 – Control Strategy
Develop software capable of:
Real-time proportional valve control
Adaptive damping control
Ride comfort optimization
Roll mitigation (using available sensors)
Pitch mitigation (using available sensors)
Fail-safe operation during sensor failure
Manual tuning of controller parameters
Phase 7 – User Interface
Develop a graphical interface displaying:
Vehicle acceleration
Suspension displacement
Gas pressure
Valve command
Suspension operating mode
Diagnostic status
Alarm messages
Historical performance trends
Deliverables
The selected developer/team shall provide:
Complete source code
AI/ML models
Control algorithms
Embedded software
GUI software
Documentation
Installation guide
Parameter tuning guide
Testing report
Support during integration with our hardware
Preferred Technical Skills
Vehicle Dynamics
Active Suspension Systems
Control Systems
Machine Learning
Signal Processing
Embedded Systems
Python
C/C++
MATLAB/Simulink (preferred)
CAN Communication
Real-Time Control Systems
Additional Recommendations
Preference will be given to AI/ML companies, university research groups, or multidisciplinary engineering teams with proven experience in:
Active or Semi-Active Suspension Systems
Vehicle Dynamics and Chassis Control
Automotive Control Algorithms
Heavy Off-Road Vehicles
Mining Equipment
Defence Mobility Platforms
Robotics and Autonomous Ground Vehicles
Real-Time Embedded Control Systems
AI/ML applications in mechatronic systems
Applicants should demonstrate previous work in control systems, embedded AI, or intelligent vehicle technologies. Experience in simulation tools such as MATLAB/Simulink, CarSim, AMESim, or similar vehicle dynamics software will be considered an advantage