In general, these controllers are often designed at various operating points using linearized models of the system dynamics and are scheduled as a function of a parameter or parameters for operation at intermediate conditions.
A new paradigm is the linear parameter-varying (LPV) techniques which synthesize of automatically scheduled multivariable controller.
New methodologies such as Adaptive control based on Artificial Neural Networks (ANN), Fuzzy logic, Reinforcement Learning,[2][3][4] etc.
try to address such problems, the lack of proof of stability and performance of such approaches over entire operating parameter regime requires design of a parameter dependent controller with guaranteed properties for which, a Linear Parameter Varying controller could be an ideal candidate.
In general, LPV techniques provide a systematic design procedure for gain-scheduled multivariable controllers.
This methodology allows performance, robustness and bandwidth limitations to be incorporated into a unified framework.
In control engineering, a state-space representation is a mathematical model of a physical system as a set of input,
The controller is restricted to be a linear system, whose state-space entries depend causally on the parameter’s history.
This formulation constitutes a type of gain scheduling problem and contrast to classical gain scheduling, this approach address the effect of parameter variations with assured stability and performance.