Olav Slupphaug

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Optimal feedback solutions to the in nite horizon LQR problem with state and input constraints based on receding horizon real-time quadratic programming are well known. In this paper we develop an explicit solution to the same problem, eliminating the need for realtime optimization. A suboptimal strategy, based on a suboptimal choice of a nite horizon and(More)
All practical MPC implementations should have a means to recover from infeasibility. We propose an algorithm designed for linear state-space MPC which transforms an infeasible MPC optimization problem into a feasible one. The algorithm handles possible prioritizations among the constraints explicitly. Prioritized constraints can be seen as an intuitive and(More)
A mathematical program for finding the optimal oil production rates in an oil production system is developed. Each well may be manipulated by injecting lift gas and adjusting a production choke. The oil production from the wells may be restricted with multiple constraints in the maximum oil flow rate, water flow rate, liquid flow rate, and gas flow rate.(More)
Optimal feedback solutions to the in nite-horizon LQR problem with state and input constraints based on receding horizon real-time quadratic programming are well known. In this paper we develop an explicit solution to the same problem, eliminating the need for real-time optimization. A suboptimal strategy, based on a suboptimal choice of a nite horizon and(More)
In this paper a method for nonlinear robust stabilization based on solving a bilinear matrix inequality (BMI) feasibility problem is developed. Robustness against model uncertainty is handled. In different non-overlapping regions of the state-space known as clusters the plant is assumed to be an element in a polytope which vertices (local models) are aane(More)
under the supervision of professor Michael Branicky. Acknowledgments First and foremost, I would like to thank my advisor professor Tor A. Jo-hansen for his rich source of ideas, practical insight and support throughout the entire research. I am grateful for having been given the opportunity to work on an industrial problem within the framework of a(More)
All practical MPC implementations should have a means to recover from infeasibility. We propose an algorithm designed for linear state-space MPC which optimally relaxes an infeasible prioritized MPC optimization problem into a feasible one by solving only one LP on-line in addition to the standard MPC optimization problem. By optimal it is meant that the(More)
All practical MPC implementations should have a means to recover from infeasibility. We present a recently developed infeasibility handler which computes optimal relaxations of the relaxable constraints subject to a user-de ned prioritization, by solving only a single linear program on-line in addition to the standard quadratic programming problem on-line.(More)