Controlling Semi-Active Damper for Heavy Duty Trucks: A Comparison of Control Algorithms for Increasing Ride Comfort with Semi-Active Dampers

Abstract

This thesis evaluates semi-active suspension control for a heavy-duty 4x2 truck under varying payload conditions, with the aim of improving ride comfort while maintaining implementation feasibility. A rigid full-vehicle model was implemented in IPG TruckMaker and integrated with MATLAB/Simulink. Five damper configurations were compared: Passive, Load-Adaptive, Skyhook with estimated states, Ideal Skyhook, and a Reinforcement Learning controller based on TD3. The controllers were evaluated on a 1–5 Hz swept-frequency road and on stochastic road profiles generated according to ISO 8608 classes A–F, using unloaded, 5,000 kg, and 10,000 kg hitch load cases. Performance was assessed primarily through RMS of vertical acceleration, supported by frequency-domain analysis and power consumption. The results show that the benefit of semi-active control depends strongly on road roughness. On smooth roads, the passive damper remained highly competitive. On rougher roads, active controllers consistently reduced vertical acceleration relative to passive damping. Reinforcement Learning achieved the lowest RMS vertical acceleration in many test cases, but required higher power and showed degraded performance outside its training distribution. Skyhook with estimated states provided the best overall balance between comfort improvement, low power consumption, and implementation feasibility, while Ideal Skyhook offered strong performance at the cost of additional sensing requirements. The Load-Adaptive controller maintained the intended damping ratio across payloads and was the simplest active strategy to implement at the lowest computational cost. However, its comfort benefit was limited at high load while power consumption increased substantially, suggesting that disabling the load-adaptive logic in operating regions where passive damping already performs comparably would improve its energy efficiency.