A computer-aided design tool for robustness analysis and control-relevant identification of Horizon Predictive Control with application to a binary distillation column

This paper describes a MATLAB-based computer-aided design tool, IRA-HPC, which accomplishes integrated system identification and robustness analysis for Horizon Predictive Control (HPC), a model predictive control algorithm implemented on the Application Module of the Honeywell TDC 3000 distributed control system. The tool addresses lifecycle as well as functional aspects of the technology, with the goal of making advanced control principles more accessible to the practising control engineer. IRA-HPC systematically performs the various stages of system identification in a control-relevant framework (addressing input design, parameter estimation, and model validation from the standpoint of the final purpose of the model, which is control system design), followed by robust HPC controller tuning using the Structured Singular Value (μ) paradigm as a basis. The benefits of the tool are shown experimentally in the modelling and control of a methanol/isopropanol pilot-scale distillation column, interfaced to an industrial-scale real-time computing testbed. The example demonstrates the practical feasibility of this tool and its benefits in terms of simplifying the choices of design variables in integrated identification and control design.     

Robust controller synthesis for multivariable nonlinear systems with unmeasured disturbances

This work concerns robust controller synthesis using the differential geometric concepts for minimum phase nonlinear systems with unmeasurable disturbances. A pseudo-linearization of the disturbance model at the input-output linearization stage is applied to yield a linear subsystem for controller design. Based on this linear model, a multi-loop controller framework is implemented, whereby μ-synthesis is used to design off-line robust controller in the outer loop while state feedback is implemented in the inner loop. Through proper selection of weights, the outer robust controller is explicitly designed to address both uncertainty and disturbance rejection whereas the inner controller is used for on-line static state feedback. Numerical simulations are used to illustrate robustness of the controller for multi-input multi-output temperature control in two non-isothermal continuous stirred tank reactors in series.