LAUSR

In this article, a reinforcement learning-based drug dosage control strategy is developed for immune systems with input constraints and dynamic uncertainties to sustain the number of tumor and immune cells in an acceptable level. First of all, the state of the immune system and the desired number of tumor and immune cells are constructed into an augmented state to derive an augmented immune system. By designing a discounted non-quadratic performance index function, the robust tracking control problem of immune systems with uncertainties is transformed into an optimal tracking control problem of nominal immune systems and the drug dosage can be limited within the specified range. Hereafter, a reinforcement learning algorithm and a critic-only structure are adopted to acquire the approximate optimal drug dosage control strategy. Furthermore, theoretical proof reveals that the proposed reinforcement learning-based drug dosage control strategy ensures the number of tumor and immune cells reaches the preset level under limited drug dosages and model uncertainties. Finally, simulation study verifies the availability of the developed drug dosage control strategy in different growth models of tumor cell.