Adaptive hierarchical tuning of fuzzy controllers

Fuzzy controller design includes both linear and non‐linear dynamic analysis. The knowledge base parameters associated within the fuzzy rule base influence the non‐linear control dynamics while the linear parameters associated within the fuzzy output signal influence the overall control dynamics. For distinct identification of tuning levels, an equivalent linear controller output and a normalized non‐linear controller output are defined. A linear proportional‐integral‐derivative (PID) controller analogy is used for determining the linear tuning parameters. Non‐linear tuning is derived from the locally defined control properties in the non‐linear fuzzy output. The non‐linearity in the fuzzy output is then represented in a graphical form for achieving the necessary non‐linear tuning. Three different tuning strategies are evaluated. The first strategy uses a genetic algorithm to simultaneously tune both linear and non‐linear parameters. In the second strategy the non‐linear parameters are initially selected on the basis of some desired non‐linear control characteristics and the linear tuning is then performed using a trial and error approach. In the third method the linear tuning is initially performed off‐line using an existing linear PID law and an adaptive non‐linear tuning is then performed online in a hierarchical fashion. The control performance of each design is compared against its corresponding linear PID system. The controllers based on the first two design methods show superior performance when they are implemented on the estimated process system. However, in the presence of process uncertainties and external disturbances these controllers fail to perform any better than linear controllers. In the hierarchical control architecture, the non‐linear fuzzy control method adapts to process uncertainties and disturbances to produce superior performance.