This article presents a methodology to apply Model-Based Design to develop and automatically optimize vehicle stability control systems. Such systems are employed to improve the dynamic rollover stability of Sport Utility Vehicles (SUVs). A non-linear vehicle model, representative of a midsize SUV, was built in CarSim®. This vehicle model is used in Simulink® to design a control system that reduces the risk of rollover. Optimization methods are then used to automatically adjust controller parameters to meet the system specifications that ensure the stability of the vehicle. Cosimulation between the two software packages enables rapid design and verification of control algorithms in a virtual environment. The results of the simulation experiments can be visualized through a 3-D animation of vehicle motion. The control system is adapted for the specific vehicle model, enabling it to remain stable under standard test conditions. The National Highway Traffic Safety Administrations' (NHTSA) fishhook maneuver was used to estimate dynamic rollover stability of the vehicle and benchmark the performance of the SUV both with and without the optimized controller.
In this paper the load optimization of a combined cycle power plant under consideration of the real cost of lifetime usage is accomplished by exploiting hybrid systems, i.e., systems evolving according to continuous dynamics, discrete dynamics, and logic rules. The possibility of turning on/off the gas and steam turbines, the operating constraints (minimum up and down times) and the different types of start up of the turbines characterize the hybrid behavior of a combined cycle power plant. In order to model both the continuous/discrete dynamics and the switching between different operating conditions we use the framework of Mixed Logic Dynamical systems. Next, we recast the economic optimization problem as a Model Predictive Control (MPC) problem, that allows us to optimize the plant operations by taking into account the time variability of both prices and electricity/steam demands. Because of the presence of integer variables, the MPC scheme is formulated as a mixed integer linear program that can be solved in an efficient way by using commercial solvers.
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