A neural approach for control of nonlinear systems with feedback linearization

Several schemes for feedback linearization using neural networks have been investigated and compared. Then an approach to design a neurocontroller in the sense of feedback linearization is introduced. The contents include: (1) full input-output linearization when a system has relative degree n; (2) partial input-output linearization when a system has relative degree r (r<n); and (3) approximate linearization when the involutivity condition does not hold. Corresponding programs and examples are given to illustrate the proposed methodology