Reduced cost of sensors and increased computing power is enabling
the development and implementation of control systems that can
simultaneously regulate multiple variables and handle conflicting
objectives while maintaining stringent performance objectives. To
make this a reality, practical analysis and design tools must be developed
that allow the designer to trade-off conflicting objectives and
guarantee performance in the presence of uncertain system dynamics,
an uncertain environment, and over a wide range of operating
conditions. As a first step towards this goal, we organize and streamline
a promising robust control approach, Robust Linear Parameter
Varying control, which integrates three fields of control theory: Integral
Quadratic Constraints (IQC) to characterize uncertainty and
nonlinearities, Linear Parameter Varying systems (LPV) that formalizes
gain-scheduling, and convex optimization to solve the resulting
robust control Linear Matrix Inequalities (LMI).
To demonstrate the potential of this approach, it was applied to
the design of a robust linear parametrically varying controller for an
ecosystem with nonlinear predator-prey-hunter dynamics
