High-performance Industrial Embedded Model Predictive Control: Efficient implementation of step response models and fast solvers
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The implementation of embedded Model Predictive Control (MPC) is treated in this thesis with focus on new and challenging applications such as subsea production and processing. The current technology for MPC in the petrochemical industry relies on lower-level controllers and dedicated safety-systems. As a consequence, the computer technology used for MPC and the software solutions do not necessarily meet the oil and gas industry’s standard for safety and reliability in stand-alone or highly automated operations. This thesis therefore aims at providing contributions that enable the use of MPC on ultra-reliable industrial computer system hardware such as programmable logic controllers. Since such industrial hardware typically have limited computational resources, efficient optimization algorithms for MPC form a natural part of this thesis. Furthermore, the industrial focus of this thesis has motivated the combination of academic advancements in the development of fast convex optimization problem solvers with MPC problem formulations that reflect industrial practice. In particular, the efficient implementation of step response model based MPC is the main focus, and the outcome is an embedded step response MPC scheme that has low computational complexity. A low complexity MPC scheme implies that accurate control is achieved with minimal computational effort. For this reason, strategies that enhance the computational efficiency and robustness of step response prediction models were considered, and an implementation based on the recursive representation of step response data was found to produce the most efficient and reliable computations. This thesis also facilitates the choice of a suitable solution method for the existing (or traditional) way of formulating the step response MPC problem, which relies on explicit output predictions. It is shown that both first-order and second-order solution methods (or solvers) are capable of solving the traditional step response MPC problem efficiently. However, the performance properties of different solvers are affected in different ways by the MPC problem formulation. Different levels of structure exploitation are achieved by different solver implementations, and careful tailoring of the solution method to the problem structure is identified as a key factor for high computational performance. Different tailored (sparse) solvers, implementing a primal-dual interior-point method and a preconditioned primal-dual first-order method, were used to solve the traditional step response MPC problem efficiently. However, for problem setups with relatively small number of inputs, the results suggest the use of condensing techniques to produce a dense problem, suitable for dense solvers. In addition, the explicit prediction strategy used in the traditional step response MPC formulation is identified as the main limitation that rules out the possibility of using some well established solution methods and highly efficient solver implementation techniques. This limitation is removed by introducing a new multistage framework for step response MPC. As a result, tailored Riccati and condensing algorithms for interior-point methods are also proposed and discussed in this thesis.