Cybernetica (Norway)

Combined cycle gas turbine plants (CCGTs) fulfill an important role in emission reduction of offshore power systems as the bottoming cycle (BC) produces additional power from exhaust heat of the gas turbines (GTs). With increasing integration of wind turbines, CCGTs offshore must be flexible and provide variation management to the offshore energy system across multiple time scales. This work proposes a model predictive controller (MPC) sending setpoints to the CCGT to satisfy demand in the offshore power system under fluctuating wind power. A high-speed surrogate model suitable for optimizing in an MPC is identified. A linear MPC using a quadratic cost function with process constraints is formulated. The model-based control structure is then validated in simulation for satisfying a constant power demand under disturbances introduced by fluctuating wind power.

Inner-loop control algorithms in state-of-the-art autopilots for fixed-wing unmanned aerial vehicles (UAVs) are typically designed using linear control theory, to operate in relatively conservative flight envelopes. In the Autofly project, we seek to extend the flight envelopes of fixed-wing UAVs to allow for more aggressive maneuvering and operation in a wider range of weather conditions. Throughout the last few years, we have successfully flight tested several inner-loop attitude controllers for fixed-wing UAVs using advanced nonlinear control methods, including nonlinear model predictive control (NMPC), deep reinforcement learning (DRL), and geometric attitude control. To achieve this, we have developed a flexible embedded platform, capable of running computationally demanding low-level controllers that require direct actuator control. For safe operation and rapid development cycles, this platform can be deployed in tandem with well-tested standard autopilots. In this paper, we summarize the challenges and lessons learned, and document the system architecture of our experimental platform in a best-practice manner. This lowers the threshold for other researchers and engineers to employ new low-level control algorithms for fixed-wing UAVs. Case studies from outdoor field experiments are provided to demonstrate the efficacy of our research platform.

SummaryThis article addresses the problem of designing a sensor fault‐tolerant controller for an observation process where a primary, controlled system observes, through a set of measurements, an exogenous system to estimate the state of this system. We consider sensor faults captured by a change in a set of sensor parameters affecting the measurements. Using this parametrization, we present a nonlinear model predictive control (NMPC) scheme to control the observation process and actively detect and estimate possible sensor faults, with adaptive controller reconfiguration to optimize the use of the remaining sensing capabilities. A key feature of the proposed scheme is the design of observability indices for the NMPC stage cost to improve the observability of both the state of the exogenous system and the sensor fault parameters. The effectiveness of the proposed scheme is illustrated through numerical simulations.

We present the results of research of limiting adversarial budget in attack games, and, in particular, in the failure-free attack tree models presented by Buldas-Stepanenko in 2012 and improved in 2013 by Buldas and Lenin. In the previously presented models attacker’s budget was assumed to be unlimited. It is natural to assume that the adversarial budget is limited and such an assumption would allow us to model the adversarial decision making more close to the one that might happen in real life. We analyze three atomic cases – the single atomic case, the atomic AND, and the atomic OR. Even these elementary cases become quite complex, at the same time, limiting adversarial budget does not seem to provide any better or more precise results compared to the failure-free models. For the limited model analysis results to be reliable, it is required that the adversarial reward is estimated with high precision, probably not achievable by providing expert estimations for the quantitative annotations on the attack steps, such as the cost or the success probability. It is doubtful that it is reasonable to face this complexity, as the failure-free model provides reliable upper bounds, being at the same time computationally less complex.

Well placement and control optimization in oil field development are commonly performed in a sequential manner. In this work, we propose a joint approach that embeds well control optimization within the search for optimum well placement configurations. We solve for well placement using derivative-free methods based on pattern search. Control optimization is solved by sequential quadratic programming using gradients efficiently computed through adjoints. Joint optimization yields a significant increase, of up to 20% in net present value, when compared to reasonable sequential approaches. The joint approach does, however, require about an order of magnitude increase in the number of objective function evaluations compared to sequential procedures. This increase is somewhat mitigated by the parallel implementation of some of the pattern-search algorithms used in this work. Two pattern-search algorithms using eight and 20 computing cores yield speedup factors of 4.1 and 6.4, respectively. A third pattern-search procedure based on a serial evaluation of the objective function is less efficient in terms of clock time, but the optimized cost function value obtained with this scheme is marginally better.