Discrete time fuzzy sliding mode control of a biochemical process

This paper presents a novel approach for designing discrete time fuzzy sliding mode controllers for a class of nonlinear systems. The motivation of the study is to explain how a sliding regime can be created in the behavior of a computer-controlled SISO nonlinear system, the details of the dynamic representations of which is unknown. The scheme exploits the regional information contained in the fuzzy quantification of the error vector. The bottom-up approach is proven to be computationally efficient for real time control applications requiring the handling of uncertainty and impreciseness and yielding a good tracking performance under the presence of time-varying plant parameters and observation noise. The analytical claims have been confirmed on a biochemical process model. The results obtained through simulations justify the theoretical claims.