Robust and efficient sub-optimal tracking of ideal piston trajectories for a hydrogen-fuelled resonating free piston generator

• New control approach is developed to track ideal piston trajectories. • State measurement noise and unknown fluctuations in combustion gas pressure assumed. • Target trajectory first split and represented by two unforced equivalent linear models. • Optimal control laws used by the nonlinear hardware or a corresponding simulation model to track target path. • Approach very effective and computationally efficient with high levels of additive Gaussian noise on state vector measurements. • Additionally controlling key generator parameter enables disturbances associated with unknown pressure fluctuation to be rejected. • Combined strategy tested using a nonlinear simulation model proving efficient and robust. • Proposed tracking approach only reduces generator efficiency by around 0.2% for a 14 kW machine. • New sub-optimal control approach developed to track ideal piston trajectories of hydrogen-fuelled resonating free piston engine which proves robust and more computationally efficient than NMPC. • Hydrogen-fuelled resonating free piston engines offer a low cost route to decarbonise heavy duty applications if they can be effficiently controlled. Here just such a control method is proposed. • Because the proposed tracking approach improves energy convertion efficiency which is one of the important focuses of the Journal. • I am the corresponding and sole author of the final submitted manuscript. A new control approach is developed to track ideal piston trajectories of a resonating free piston generator running on hydrogen in the presence of state measurement noise and unknown fluctuations in combustion gas pressure. Ideal piston trajectories are assumed to be available as a result of maximising efficiency, while meeting specified compression ratio and air–fuel ratio. A novel tracking approach is adopted which starts by making a double exchange involving the target trajectory being split into compression and expansion stages and represented by two unforced equivalent linear models which are used to construct optimal control laws. These control laws are then used by the nonlinear hardware (or a corresponding nonlinear simulation model) to track the target path. The proposed approach proves very effective and computationally efficient, even with high levels of additive Gaussian noise on the state vector measurements but it is not effective alone in handling a large unknown fluctuation in combustion gas pressure. By additionally controlling (during expansion) a key generator parameter in proportion to the path error and its integral, the disturbance associated with an unknown pressure fluctuation can also be very effectively rejected. The combined strategy is tested using a nonlinear simulation model of generator hardware showing the scheme to be computationally very efficient and highly robust. It is also predicted, for a 14 kW machine, that the proposed tracking approach will only reduce the overall generator efficiency by around 0.2% from an ideal trajectory efficiency of 55.42%.