AI-Hydro-Pneumatic Suspension Control Development
Publicada el 2026-07-16
Descripción de la oferta
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
Skills
Fuente original: freelancer