Robust system identification and robust model predictive control with applications to chemical engineering processes

This thesis deals with problems in two closely related research areas, system identification (SYSID) and model predictive control (MPC), which are both important in industrial. process applications. The robustness of SYSID and MPC is the main concern of the thesis. The focus of the first part of the thesis is to develop Robust System Identification Strategies that yield effective and robust estimates of system model and characteristics under the uncertainty of a priori knowledge. The incorporation of the techniques taken from classical system identification approaches as well as robust identification design based on Wavelets density estimation will ensure that the estimation of system is “optimal” in all assumptions and supposed operation conditions considered of the process error. The conclusive result has been a robust and effective estimator that is in accordance to the industrial requirements, in regards to practical implementation of these strategies. This method is applied to most system models and estimation algorithms (especially on—line recursive algorithms) and takes the conventional ones as special cases. This approach has many innovative features, one being the design of robust estimator which is insensitive to deviation from the various assumptions about the prediction error. This characteristic makes a robust estimation to be suitable for most error distributions. This methodology would then be capable of solving a large number of difficult problems in relation to modeling a system using contaminated data facing in fields such as industry, science and economics. The second problem addressed in the thesis is to develop Robust Model Predictive Control Strategies that yield effective and robust controller under the mismatch between plant and the models employed to describe the system. A new robust MPC design method is derived from comparison and discussion of the similarity between parameter estimation and MPC. The incorporation of techniques taken from standard model predictive control approaches as well as the robust controller design will ensure that the controller has better performance regarding assumptions on the nominal model and assumed plant-model mismatch. The result will be a robust and effective controller that is in accordance to the industrial requirements. Some properties of the proposed robust MPC design such as closed loop stability and performance tuning are investigated. The innovative feature of the robust MPC design makes the controller insensitive to deviation from the various assumptions about the nominal model, and to be suitable for most plant-model mismatches. The proposed design can take into account the compromise between efficiency, robustness as well as computation load. It is shown that it is an expansion of ' conventional MPC. The practical implications of the proposed robust system identification and robust MPC design methods are shown by means of chemical engineering process examples