CONTROL-AFFINE NEURAL-NETWORK APPROACH FOR NON MINIMUM-PHASE NONLINEAR PROCESS-CONTROL

The design of controllers for nonlinear, nonminimum-phase processes is very challenging and remains as one of the more difficult control res earch problems. Most currently available control algorithms rely impli citly or explicitly upon an inverse of the process. Linear control met hods for nonminimum-phase processes are typically based on a decomposi tion of the process into a minimum-phase and a nonminimum-phase part, and subsequent inversion of the minimum-phase component. A similar sch eme for nonlinear systems is still an open problem. In this work, an i nternal model control strategy employing a minimum-phase model is prop osed. The minimum-phase model is first-order, minimum-phase and contro l-affine but statically equivalent to the original process. Because th e model is identified directly from input-output data, a first princip les model of the process is not required. The inverse of the process i s obtained through analytical inversion of the process model. The prop osed control scheme is applied to a van de Vusse reactor and a complex continuous stirred tank bioreactor.