Data-Driven Backstepping Control of Chemical Process

Modeling and control design of complex chemical processes are challenge tasks because of their multi-variable, time-delay and non-linear features. On the other hand, the plant dynamics are hard to characterize precisely on line when facing uncertain disturbance. In the light of this, this paper presents a data-driven backstepping control scheme for the nonlinear chemical process. Compared with other regular chemical process control schemes, the proposed scheme is independent of specific mathematical models, and free of decoupling operation, linearization, or off-line recognition and modeling. By constructing Lyapunov function and feedback control rate based on real-time data, the integral stability is guaranteed. Williams-Otto reactor example is provided to demonstrate the effectiveness and applicability of the scheme.