Deep hybrid model‐based predictive control with guarantees on domain of applicability

Abstract A hybrid model integrates a first‐principles model with a data‐driven model which predicts certain unknown dynamics of the process, resulting in higher accuracy than first‐principles model. Additionally, a hybrid model has better extrapolation capabilities compared with a data‐based model, which is useful for process control and optimization purposes. Nonetheless, the domain of applicability (DA) of a hybrid model is finite and should be taken into account when developing a hybrid model‐based predictive controller in order to maximize its performance. To this end, a Control Lyapunov–Barrier Function‐based model predictive controller (CLBF‐based MPC) is developed which utilizes a deep hybrid model (DHM), that is, a deep neural network (DNN) combined with a first‐principles model. Additionally, theoretical guarantees are provided on stability as well as on system states to stay within the DA of the DHM. The efficacy of the proposed control framework is demonstrated on a continuous stirred tank reactor.