Robust multivariable control of industrial production processes: A discrete-time multi-objective approach.

This thesis considers a number of important practical issues in the synthesis of discrete-time robust controllers for industrial processes. The work focuses on the control of an "unknown" SISO process (the IFAC 1993 benchmark), the design of robust model-based controllers for a MIMO industrial production process (a glass tube production process), and the design of robust MIMO controllers having integrated control and diagnostic capabilities. The industrial case studies presented are realistic in the sense that their control problems do frequently arise in engineering situations. Explicit state-space formulae for Hinfinity-based one degree-of-freedom (1-DOF) and two degrees-of-freedom (2-DOF) robust controllers are derived. They provide robust stability with respect to left coprime factor perturbations, and for the 2-DOF case, a degree of robust performance in the sense of making the closed-loop system follow a desired reference model. Robust controllers for the "unknown" plant are designed using H2 and Hinfinity optimization techniques. Explicit closed-loop performance is obtained by designing the weighting function parameters using numerical optimization techniques in the form of the method of inequalities. Methods for designing Hinfinity-based controllers that can be directly implemented in the Internal Model Control (IMG) scheme are presented. Explicit state-space formulae for Hinfinity-based IMG 1-DOF and 2-DOF robust controllers which provide robust stability and robust performance with respect to left coprime factor perturbations, axe derived. A technique for discrete-time model reduction is presented, with two illustrative examples. The technique is used in a detailed study of the identification and control of the glass tube production process. The production process, especially for large tube measures, is ill-conditioned and contains large time delays. The model of the process reflects the transfer of two process inputs (mandrel pressure and drawing speed) to the tube dimensions (wall thickness and diameter). The models obtained from advanced multivariable identification are used for the design of robust IMG controllers for the process. The robust performance of the controller is demonstrated and a comparison is made with the present control system. Finally, a framework for synthesizing robust controllers which have both control and actuator failure detection capabilities is presented. Simulation results for a MIMO design example are presented which demonstrate the feasibility of this integrated design approach.