# Control Design and Validation for Floating Piston Electro-Pneumatic Gearbox Actuator
## 5 Conclusions  
In this paper, two different types of position control design methods using electro-pneumatic gearbox actuators have been presented. The controllers are implemented in a MIL environment and tested on a gearbox bench under different supply pressures.  
The industrial application raises the trade-off between costs and feasibility. In this case, the sampling time of the sensors and the computational limitations of the automotive ECU requires model reduction. It is also shown that simplifications must be performed during the modeling of double-acting floating piston cylinders to generate an eight-state 4 th order qLPV model, which is handled by LTI control design methods.  
Using model-based controller development, it is possible to predict the key features of the implemented controllers (e.g., the failed Gear to Neutral shifts with LQR method) and to estimate the performances well. The controlling purpose is to minimize the solenoid valve activations while the inaccurate modeling of the hysteresis is negligible, but, at the same time, the communication delays of the measurement system are taken into consideration.  
Experimental results show that a gain-scheduled PID controller with adequate parameters and hysteresis achieves the position control of a pneumatic actuator with fast dynamics and unstable behavior. Although the LQR controller fails to meet the requirements in the case of Gear to Neutral shifts, it can be used effectively in pneumatic actuators without a floating piston.  
The main disadvantage of these methods is that the control signals must be converted into solenoid valve commands. In the case of the PID controller, there is only one signal to control both solenoid valves, thus this signal must be discretized to provide four different combinations of solenoid valve states. The LQR controller is slightly easier to adapt as it has two outputs, which are mass flow rates. A possible method to discretize its outputs is to determine the maximum mass flow rates achievable by the solenoid valve. If the control signal exceeds a given percentage of the maximum value, the solenoid valve is activated. Based on the experience it can be declared that this discretization is at least as important as the control algorithm itself.  
Further research will focus on LPV control design, which is an efficient method for handling of nonlinear systems and on neural network-based controllers for handling the environmental conditions.  
Conceptualization: A.S. and T.B.; Supervision: P.G.; Modeling: A.S.; Methodology and control design: P.G. and A.S.; Implementation and data collection: A.S.; Data evaluation and visualization: T.B.; Writing: A.S. and T.B.; Reviewing and editing: P.G. All authors have read and agreed to the published version of the manuscript.  
The research reported in this paper was supported by the Higher Education Excellence Program in the frame of Artificial Intelligence research area of Budapest University of Technology and Economics (BME FIKP-MI/FM).  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
ADASAdvanced Driver Assistance Systems  
ECUElectronic Control Unit  
H2 LHigh to Low  
H2 NHigh to Neutral  
L2 HLow to High  
L2 NLow to Neutral  
LPVLinear Parameter Varying  
LQRLinear Quadratic Regulator  
LTILinear Time InvariantMBD Model-Based Development  
MIL Model In the Loop  
MPC Model Predictive Control  
N2H Neutral to High  
N2L Neutral to Low  
PAM Pneumatic Artificial Muscles  
PID Proportional-Integral-Derivative  
PWM Pulse-Width Modulation  
qLPV quasi Linear Parameter Varying  
SMC Sliding Mode Control