The present study has focused on the comparison of MR damper dynamic models for the purpose of hardware in the loop simulation. A vehicle dynamic model for large-sized bus and a control logic for MR damper was built. Two typical MR damper models, viz. Bouc-Wen and hyperbolic tangent model have been considered in this study and the advantages and disadvantages of each model on the aspect of HILS system is discussed. We discussed the limitations of each model based on the analysis of the vehicle dynamic simulation. The results showed that the existing models are not suitable for HILS system. We proposed the modified hyperbolic tangent model by adopting low-pass filters. The results from the simulation showed the advantages of the modified model which were validated through HILS system.

Comparison of Dynamic Models of Mr Damper for Hardware in the Loop Simulation of Large-Sized Buses

Semi-active suspensions have much better performance than the passive suspensions along with the advantages such as low power consumption and cost. In order to control the semi-active suspension system, such as MR damper, sensory feedback is required. The skyhook control which is the most widely used algorithm admits the velocity of damper and vertical sprung mass. For this, additional attachment, such as a displacement sensor (e.g., an LVDT) or an accelerometer at the sprung mass is required. The present study proposes a method for estimating the damper speed from a pre-existing IMU sensor and a lower damper accelerometer considering the damper orientation in practice. It was validated that the performance of the skyhook-controlled suspension matches with the proposed algorithm.

Practical Damper Velocity Estimation for Semi-Active Suspension Control

Among the controllable suspension systems, the control of the semi-active suspension is mostly based on optimal control. Recently, deep reinforcement learning is widely used as a method to solve the optimal control problem. Control strategies developed using reinforcement learning have shown performance beyond conventional control algorithms in some fields. In the current study, we have proposed a near optimal semi-active suspension ride comfort controller using deep reinforcement learning. An algorithm suitable for a semi-active suspension control environment was selected based on deep reinforcement learning theory to increase convergence in training. Furthermore, a state normalization filter was designed to improve the generalization performance. When compared with the ride comfort oriented classical control algorithms, our trained controller showed the best performance in terms of ride comfort. Policy map comparison with mixed SH-ADD (Skyhook-Acceleration Driven Damping) algorithm suggested the direction to the design of the semi-active suspension control algorithm. 

Deep Reinforcement Learning of Semi-Active Suspension Controller for Vehicle Ride Comfort

The lateral vibration of high-speed train reduces the ride stability and passenger comfort. Semi-active suspension system is often adopted to suppress the lateral vibration of trains due to its low cost and low energy consumption in control, where magnetorheological (MR) damper is the main vibration absorber. In this paper, a full model of the controlled system that consists of the 3-degree of freedom train model and the hysteretic MR damper model is first built. Then, a second-order sliding mode (SOSM) controller is designed for the suppression of lateral vibration of train, where the hysteresis and non-hysteresis nonlinearities are addressed simultaneously and rigorously. Finally, comparative simulations are conducted under the excitation of the German low-interference track spectrum. The results show that the proposed controller can suppress the lateral vibration of high-speed train efficiently in the sense that, compared with the passive control, the internal model-based resonant control and repetitive learning control, the suspension ratio of root-mean-square of lateral acceleration is reduced by 96.98%, 35.70% and 10.54%, respectively.

Modeling and Second-Order Sliding Mode Control for Lateral Vibration of High-Speed Train With MR Dampers

This paper presents the evaluation of the vibration control on the performance of magneto-rheological fluid damper (MR damper in short) for gun recoil system. In this study, a magneto-rheological (MR) damper type RD-8040 manufactured by Lord Corporation is used. Then, characterization of MR damper is carried out by varying recoil energy and input currents to obtain its behavior. The MR damper is then modeled using adaptive neuro-fuzzy inference system (ANFIS) technique. The mathematical model of the gun recoil system (plant in control structure) is formulated, and a control strategy known as a hybrid skyhook active force control (H-SAFC) is proposed. Then, a current generator is designed using inverse ANFIS method to supply accurate amount of input currents to the MR damper coils. The simulation analysis of control strategy is conducted by varying the recoil energy and its performance compared with passive damper system. In order to improve the H-SAFC performance, an adaptive mechanism is developed for the adapting with various recoil energies. The performance of adaptive H-SAFC is evaluated by the simulation and experiment, and the performance criteria such as jerk, accelerations and forces of the gun recoil system are analyzed in the time domain.

Kyuyeol Koak

Papers

Comparison of Dynamic Models of Mr Damper for Hardware in the Loop Simulation of Large-Sized Buses

A Study on an MR Damper HILS System for a Bus