High-Performance Model Predictive Control for Process Industry

Process industry requires now accurate, efficient and flexible operation of the plants. There is always a need for development of innovative technologies and integrated software tools for process modelling, dynamic trajectory optimization and high-performance industrial process control. Process dynamics tend to become too complex to be efficiently controlled by the current generation of control and optimization techniques. The main goal of research in this thesis was the development of advanced process control technology and its implementation in an integrated real-time software environment. The research was done as part of the international European project: “INtegration of process COntrol and plantwide OPtimization” (INCOOP). The only advanced control technology which made a significant impact on industrial control engineering is model predictive control (MPC). Specifically, the research in this thesis is focused on MPC for nonlinear processes. Nonlinear MPC optimizations become computationally expensive to be solved in realtime. This thesis presents various MPC algorithms for nonlinear plants using successive linearizations. The prediction equation is computed via nonlinear integration. Local linear approximation of the state equation is used to develop an optimal prediction of the future states. The output prediction is made linear with respect to the undecided control input moves, which allows to reduce the MPC optimization to a quadratic programming problem (QP). It is shown that the constrained QP problem can be solved in various ways. First of all, one can use the model equations to eliminate the states, thus reducing the number of variables in the optimization. However, this makes the problem formulation dense. Solving QPs with these methods typically requires a computational time that increases with the third power of the number of optimization variables. The constrained optimization programs tend to become too large to be solved in real-time when these standard QP solvers are used. Many industrial examples show that large-scale, usually stiff, nonlinear systems may require long prediction horizons to fulfill certain performance specifications. These requirements increase the number of variables in the optimization. Naive implementations of standard QP solvers could be inefficient for such MPC problems. A structured interior-point method (IPM) has been developed in this thesis to solve the MPC problem for large-scale nonlinear systems to reduce the computational complexity. The developed optimization algorithm explicitly takes the structure of the given problem into account such that the computational cost varies linearly with the number of optimization variables, compared with the cubic growth for the standard QP solvers. The algorithm also easily allows to introduce multiple linear models, thus making the control more flexible. The state elimination was not carried out and the structure given by the dynamics