A computationally efficient policy optimization scheme in feedback iterative learning control for nonlinear batch process

In this paper, we propose a computationally efficient feedback iterative learning control (ILC) scheme for nonlinear batch processes. We present a structured framework that delineates the feedback ILC as a composite of two integral components: a state feedback controller and a conventional ILC mechanism. Within this framework, we employ policy search techniques to optimize the feedback component. In parallel, we tackle the feedforward aspect by formulating a stochastic optimal ILC problem. These two components are offline iteratively updated, thereby ensuring convergence under ideal conditions. To account for missing process models in practical scenarios, we incorporate Gaussian process (GP) modeling into our framework. By leveraging the GP model, we extend our iterative optimization approach to a GP-based feedback ILC optimization algorithm that guarantees tractability. We use two numerical examples to demonstrate the merits of our framework, including its fast convergence and effective rejection of disturbances.