# A Review of Low-Frequency Active Vibration Control of Seat Suspension Systems
## 5 Our Contributions  
Our group has been working on the active vibration control of a seating system since 2016. A 5-DOF seat-occupant biodynamic model was developed and applied in vibration control simulation research [68]. A new active vibration controller for a seating system that combines a traditional rotating and a scissor-like structure was designed and built to reduce low-frequency vibration and increase ride comfort [57]. The PID and H-infinity controllers were designed and applied to the active vibration control seating system in [57; 68]. An analysis of the parameter sensitivity of a 5-DOF model-based Monte Carlo simulation was performed [68]. The measurement data and results recorded from four actual trucks in a field test were applied in the research to identify the 5-DOF system parameters of the human body seating system [68]. The relevant active vibration control of the seating system was simulated in the Simulink software [68].  
_Identified Research Gaps, Research Questions, and New Directions_  
In previous research, a number of innovations in terms of the active vibration control of a seating system have been proposed. Vibration control of a seating system has been well studied and analyzed in the laboratory. However, there are still some research areas that could be improved or gaps that need to be filled.  
In terms of active vehicle suspension control, there are at least four active control algorithms that have been applied to the active vibration control of seating systems. However, the vibration control performance has not been crosswise compared to these various controllers. Such a project may allow researchers to identify which control algorithm is best for vibration control performance.  
Due to the particularity of the human body, it is difficult to identify its model parameters in real-time. So, a real-time parameter identification method for human body biodynamic model parameters will help improve controller performance.  
Compared with traditional controllers, ANN control reduces many of the modelling and calculation steps and simplifies controller design. However, ANN training requires a large amount of ideal data for back-propagation training. Therefore, determining how to use an unsupervised learning algorithm in place of a traditional supervised learning algorithm is an important future research direction.  
An ANN control system has weak robustness due to sudden and unmanageable signals. Therefore, combining the ANN system with a traditional control system, using the control results of the traditional system to train the ANN controller, and enhancing the robustness of the system are also research areas worthy of consideration.  
Figure 28: A 5-DOF seat–occupant model [68].  
From the above-identified research gaps, the following research questions are raised:  
1. What kind of ANN algorithm can be used to improve the PID control algorithm for active vibration control seating systems?
2. How can an unsupervised deeper learning algorithm be used to improve the performance of vibration cancellation in active seating systems?
3. How can an ANN algorithm be used to improve the robustness of a control system?