Application-oriented experiment design for industrial model predictive control

Sammanfattning: Advanced process control and its prevalent enabling technology, model predictive control (MPC), can today be regarded as the industry best practice for optimizing production. The strength of MPC comes from the ability to predict the impact of disturbances and counteract their effects with control actions, and from the ability to account for constraints. These capabilities come from the use of models of the controlled process. However, relying on a model is also a weakness of MPC.The model used by the controller needs to be kept up to date with changing process conditions for good MPC performance. In this thesis, the problem of closed-loop system identification of models intended to be used in MPC is considered.The design of the identification experiment influences the quality and properties of the estimated model. In the thesis, an application-oriented framework for designing the identification experiment is used. The specifics of experiment design for identification of models for MPC are discussed. In particular, including constraints in the controllerresults in a nonlinear control law, which complicates the experiment design.The application-oriented experiment design problem with time-domain constraints is formulated as an optimal control problem, which in general is diffcult to solve. Using Markov decision theory, the experiment design problem is formulated for finite state and action spaces and solved using an extension of existing linear programming techniques for constrained Markov decision processes. The method applies to general noise and disturbance structures but is computationally intensive. Two extensions of MPC with dual control properties which implement the application-oriented experiment design idea are developed. These controllers are limited to output error systems but require less computations. Furthermore, since the controllers are based on a common MPC technique, they can be used as extensions of already available MPC implementations. One of the developed controllers is tested in an extensive experimental validation campaign, which is the first time that MPC with dual propertiesis applied to a full scale industrial process during regular operation of the plant.Existing experiment design procedures are most often formulated in the frequency domain and the spectrum of the input is used as the design variable. Therefore, a realization of the signal with the right spectrum has to be generated. This is not straightforward for systems operating under constraints. In the thesis, a framework for generating signals, with prespecified spectral properties, that respect system constraints is developed. The framework uses ideas from stochastic MPC and scenario optimization. Convergence to the desired autocorrelation is proved for a special case and the merits of the algorithm are illustrated in a series of simulation examples.