Pneumatic pressure and position control using learning law

Two learning control algorithms have been proposed to improve the pressure and position control accuracies of a pneumatic actuator system. The proposed learning law is designed in such away that it updates the feedforward pressure or force using the previous cyclic data and hence it reduces the steady error when the next command cycle is performed Both computer simulation and laboratory experiment exercises have shown that these algorithms are valid as the steady-state pressure and position errors for step input commands are reduced significantly without any manual adjustment of the control gains. The associated convergence criteria and optimal learning gains have also been determined based on the linear system theory. These work very well for the nonlinear system as evident from computer simulation and experimental results.