# Optimal Control Method of Semi-Active Suspension System and Processor-in-the-Loop Verification
## 1 Introduction
[PERSON] et al. enhanced the vehicle's dynamic characteristics in the vertical direction with a PSO-optimized sliding mode surface controller in the SASS for the hydraulic adjustable damper [34]. [PERSON] et al. evaluated frequency-weighted RMS values with different optimization methods, such as PSO, cuckoo search, and genetic algorithm, for improving driving comfort in a commercial vehicle [35]. A genetic algorithm with a multi-objective function was used for SASS by [PERSON] and [PERSON]. They considered not only road holding and ride comfort characteristics but also the conflict on the vibration reduction effectivity and the dissipated energy [36]. The other issue that should be addressed in the control of SASS is verifying the control algorithm through real-time testing on the MR damper model. Challenges encountered in the design process can be solved swiftly and cost-effectively with the help of rapid prototyping methods [37]. Processor in the loop (PIL) is frequently employed for test and performance verification of the system to be controlled in various fields as reported in [38; 39; 40]. [PERSON] et al. verified the control performance of an autonomous vehicle with a PIL architecture [41]. The path-planning algorithm of an autonomous vehicle was verified using a combination of model-in-the-loop (MIL) and PIL architecture by [PERSON] et al. [42]. [PERSON] et al. obtained theoretical stability results for the control algorithm developed for an anti-lock braking system. They experimentally verified with the PIL method for different road conditions [43]. Verification and validation processes using model-based approaches also play an essential role in safety-critical systems. [PERSON] and [PERSON] validated the model predictive control algorithm used for assisted driving in the automotive industry with PIL [44]. Moreover, the PIL architecture is widely used in the verification of power electronics systems. [PERSON] et al. verified the maximum power point tracking algorithms using a V-cycle development process [45]. [PERSON] et al. tested a three-phase photo-voltaic fault-tolerant control scheme with the PIL method [46]. However, according to the author's review, research on the design and implementation of SASS with PIL verification for control algorithm validation is still missing in the literature. As mentioned before, road holding and ride comfort are trade-off parameters for suspension systems. Improving both parameters together is the main objective of SASS control. The motivation of this study is to optimize the controller parameters to minimize this conflicting behavior of the SASS. In this study, the characteristics of the MR damper in the shock absorber test device were modeled by training a feedforward ANN. Current, velocity, and displacement for the MR damper are the inputs of this ANN model. Furthermore, a classical controller with a robust optimization algorithm was employed on an experimental quarter car suspension system setup and obtained highly satisfying results. The verification of the controller algorithm was realized with the PIL approach on a real-time processor. The main contribution of this study is to implement a PSO-optimized and then PIL-verified PID controller on a real MR damper-based SASS. Three different bump inputs were applied to analyze the MR damper dynamic response. The performance parameters analyzed in this study are RMS values of sprung mass acceleration, sprung mass displacement, and suspension deflection for the experimental tests. Additionally, the dynamic load coefficient (DLC) is calculated for the simulation study with the random Type-C road profile. A significant improvement was achieved in all performance parameters. Consequently, the proposed controller could supply an enhancement of the conflict behavior of road holding and ride comfort.