Neural-Network-Based Adaptive Model Predictive Control for a Flexure-Based Roll-to-Roll Contact Printing System

High-precision contact force control is essential for continuous roll-to-roll contact printing via mechanical contact on flexible web substrates using stamps. Nonuniformly controlled stamp contact force will cause failures during printing, especially for large-area printing processes. Due to their high precision in positioning and force control, flexure mechanisms have been applied in roll-to-roll contact printing systems; however, conventional physical model-based control systems cannot manage the nonlinear effects that exist in flexure-based roll-to-roll contact printing systems. To achieve precise contact force control, we propose a neural-network-based adaptive model predictive control for a flexure-based roll-to-roll contact printing system. The nonlinearity of the flexure mechanism is learned and modeled by an artificial neural network. To eliminate the steady-state error caused by model mismatches and external disturbances, an online adaptive mechanism is designed via updating the biases of the output layer of the neural network model. Experimental results show that the root-mean-square error of the contact force can be controlled in the range of 0-0.075 N with balances on two ends of the print roller, outperforming a proportional-integral-derivative controller, a neural-network-based standard model predictive control (MPC) controller, and a neural-network-based robust MPC controller. The proposed control algorithm is implemented in a microcontact printing process that prints 45-mu m width gold patterns and achieves a variation of 0.3 mu m in the average gold line width at different locations on an 88.9-mm width flexible substrate. The uniform microscale printing results have shown the effectiveness of the proposed neural-network-based adaptive model predictive control in the applied printing process.