Most real-world control problems are nonlinear. Control system development for these problems has traditionally been based on Taylor-series-linearized system dynamics in conjunction with linear control techniques.
Since the system dynamics will behave differently in different parts of the state space, several controllers will have to be designed and "scheduled" with respect to the operating conditions to yield acceptable control system performance. In many situations, coming up with a satisfactory controller schedule can consume far more human resources than the linearization and linear system design tasks. Moreover, the stability of the resulting system cannot be guaranteed.
Nonlinear control methods take advantage of the given nonlinear system dynamics to generate high-performance designs. No Taylor series linearization or gain scheduling is required for their implementation. These features free the designer to focus on the control system design aspects of the problem, leaving tedious model manipulations to the software.