An Event-Triggered Iteration Learning Control Method for a Batch Distillation Process

An iteration learning control (ILC) method was employed for a nonlinear distillation process control system, which is one of the most useful processes in chemistry. This paper studied the integrated control problem with parameter uncertainties to improve the performance of batch process systems. A P-type event-triggered iterative learning control scheme based on Markov parameter identification which was minimized by the tracking error and cost function. The performance index for the next iteration was obtained, while the Markov parameter matrix was continually updated and iterated, constructing an adaptive iterative learning control scheme. A novel Lyapunov Adaptive Event Triggering Mechanism (LA ET) was proposed by combining the adaptive event triggering condition with the Lyapunov function. Convergence analysis was done mathematically for the constructed adaptive iterative learning control. As a result, the simulation experiments with the distillation process showed that the proposed algorithms effectively reduced the update frequency of control inputs, which increased the operating efficiency of the batch distillation system, and saved resources.