Abstract — In this paper, a new control technique for nonlinear control based on hybrid modeling is proposed. The control system utilizes the well-known gradient descent, but the learning rate is adapted in each iteration step in order to accelerate the speed of convergence. It is shown that the selection of the learning rate results in stable training in the sense of Lyapunov. Advantages of this technique are illustrated by simulations where a continuous flow stirred biochemical reactor is chosen as a case study
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