The performance analysis of three advanced non linear controllers is the main focus of this paper. All three controllers are applied for the control of a batch polymerisation reactor which is defined by a very simple kinetic model for the polymerisation of styrene. This simple set of equations describing the polymerisation process is first solved using the sequential strategy i.e. Control Vector Parameterisation (CVP) technique within gPROMS to find optimal initial initiator concentrations and the reactor temperature trajectory necessary to yield desired polymer molecular properties (defined here as fixed values of monomer conversion and number average chain length) in minimum time. The sequential solution strategy has had limited application in solving optimisation problems for polymerisation in batch reactors, most researchers instead employing the Pontryagin's Maximum Principle (PMP) to solve optimal control problems involving these systems.The temperature trajectory obtained from the dynamic optimisation is used as the setpoint to be tracked by the three controllers: Dual Mode control with PID, which is representative of industrial practice, Generic Model Control (GMC) with Neural Networks as online heat release estimator, and Direct Inverse Control (DIC). Published work on the last two controllers as applied to the control of a batch polymerisation reactor is absent from the literature.When the performances of the different controllers are evaluated, it is seen that the GMC-NN controller performs better than the other two for the system under consideration.
A Gain-Scheduling PI Control Based on Neural Networks