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Flight Simulator X: Acceleration Fully ##TOP## Cracked Key Generator

A controller for critical vehicle maneuvering is proposed that avoids obstacles and keeps the vehicle on the road while achieving heavy braking. It operates at the limit of friction and is structured in two main steps: a motion-planning step based on receding-horizon planning to obtain acceleration-vector references, and a low-level controller for following these acceleration references and transforming them into actuator commands. The controller is evaluated in a number of challenging scenarios and results in a well behaved vehicle with respect to, e.g., the steering angle, the body slip, and the path. It is also demonstrated that the controller successfully balances braking and avoidance such that it really takes advantage of the braking possibilities. Specifically, for a moving obstacle, it makes use of a widening gap to perform more braking, which is a clear advantage of the online replanning capability if the obstacle should be a moving human or animal. Finally, real-time capabilities are demonstrated. In conclusion, the controller performs well, both from a functional perspective and from a real-time perspective.

Flight Simulator X: Acceleration Fully Cracked Key Generator


International shipping has been reported to account for 13% of global NOx emissions and 2.1% of global green house gas emissions. Recent restrictions of NOx emissions from marine vessels have led to the development of exhaust gas recirculation (EGR) for large two-stroke diesel engines. Meanwhile, the same engines have been downsized and derated to optimize fuel efficiency. The smaller engines reduce the possible vessel acceleration, and to counteract this, the engine controller must be improved to fully utilize the physical potential of the engine. A fuel index limiter based on air/fuel ratio was recently developed (Turbo, 2016), but as it does not account for EGR, accelerations lead to excessive exhaust smoke formation which could damage the engine when recirculated. This paper presents two methods for extending a fuel index limiter function to EGR engines. The methods are validated through simulations with a mean-value engine model and on a vessel operating at sea. Validation tests compare combinations of the two index limiter methods, using either traditional PI control for the EGR loop or the recently developed fast adaptive feedforward EGR control (Nielsen et al., 2017a). The experiments show that the extended limiters reduce exhaust smoke formation during acceleration to a minimum, and that the suggested limiter, combined with adaptive feedforward EGR control, is able to maintain full engine acceleration capability. Sea tests with engine speed steps from 35 to 50 RPM, made peak exhaust opacity increase by only 5% points when using the proposed limiter, whereas it increased 70% points without the limiter.

Plug-in Hybrid Electric Vehicles (PHEV) provide a promising way of achieving the benefits of the electric vehicle without being limited by the electric range, but they increase the importance of the supervisory control to fully utilize the potential of the powertrain. The winning contribution in the PHEV Benchmark organized by IFP Energies nouvelles is described and evaluated. The control is an adaptive strategy based on a map-based Equivalent Consumption Minimization Strategy (ECMS) approach, developed and implemented in the simulator provided for the PHEV Benchmark. The implemented control strives to be as blended as possible, whilst still ensuring that all electric energy is used in the driving mission. The controller is adaptive to reduce the importance of correct initial values, but since the initial values affect the consumption, a method is developed to estimate the optimal initial value for the controller based on driving cycle information. This works well for most driving cycles with promising consumption results. The controller performs well in the benchmark; however, the driving cycles used show potential for improvement. A robustness built into the controller affects the consumption more than necessary, and in the case of altitude variations the control does not make use of all the energy available. The control is therefore extended to also make use of topography information that could be provided by a GPS which shows a potential further decrease in fuel consumption.

A benchmark control problem was developed for a special session of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 12), held in Rueil-Malmaison, France, in October 2012. The online energy management of a plug-in hybrid-electric vehicle was to be developed by the benchmark participants. The simulator, provided by the benchmark organizers, implements a model of the GM Voltec powertrain. Each solution was evaluated according to several metrics, comprising of energy and fuel economy on two driving profiles unknown to the participants, acceleration and braking performance, computational performance. The nine solutions received are analyzed in terms of the control technique adopted (heuristic rule-based energy management vs. equivalent consumption minimization strategies, ECMS), battery discharge strategy (charge depleting-charge sustaining vs. blended mode), ECMS implementation (vector-based vs. map-based), ways to improve the implementation and improve the computational performance. The solution having achieved the best combined score is compared with a global optimal solution calculated offline using the Pontryagins minimum principle-derived optimization tool HOT.

This paper considers the problem of selecting a set of residual generators for inclusion in a model-based diagnosis system, while fulfilling fault isolability requirements and minimizing the number of residual generators. Two novel algorithms for solving the selection problem are proposed. The first algorithm provides an exact solution fulfilling both requirements and is suitable for small problems. The second algorithm, which constitutes the main contribution, is suitable for large problems and provides an approximate solution by means of a greedy heuristic and by relaxing the minimal cardinality requirement. The foundation for the algorithms is a novel formulation of the selection problem which enables an efficient reduction of the search-space by taking into account realizability properties, with respect to the considered residual generation method. Both algorithms are general in the sense that they are aimed at supporting any computerized residual generation method. In a case study the greedy selection algorithm is successfully applied in an industrial sized automotive engine system.

A minimum-time lane change maneuver is executed under friction-limited conditions using (1) the Modified Hamiltonian Algorithm (MHA) suitable for real-time control and (2) numerical optimization for comparison. A key variable is the switching time of the acceleration reference in MHA. Considering that MHA is based on an approximate vehicle model to target real-time control, it cannot exactly match the ideal reference as obtained from offline optimization; this paper shows that incorporation of a limited-jerk condition successfully predicts the switching time and that the desired lane position is reached in near minimum time.

Structural approaches have shown to be useful for analyzing and designing diagnosis systems for industrial systems. In simulation and estimation literature, related theories about differential index have been developed and, also there, structural methods have been successfully applied for simulating large-scale differential algebraic models. A main contribution of this paper is to connect those theories and thus making the tools from simulation and estimation literature available for model based diagnosis design. A key step in the unification is an extension of the notion of differential index of exactly determined systems of equations to overdetermined systems of equations. A second main contribution is how differential-index can be used in diagnosability analysis and also in the design stage where an exponentially sized search space is significantly reduced. This allows focusing on residual generators where basic design techniques, such as standard state-observation techniques and sequential residual generation are directly applicable. The developed theory has a direct industrial relevance, which is illustrated with discussions on an automotive engine example. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.


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