The principle of a large gap magnetic drive system was used to achieve control of an axial-flow blood pump. A dynamic model of the start-up process of the axial-flow blood pump was established. It was analyzed and simulated. An acceleration control method for the blood pump was proposed based on the start-up process dynamic model. A corresponding parameter measurement test system was set up, and experimental data were compared with the results of the theoretical simulation. Results indicated that the experimental values obtained for the blood pump outlet pressure and flow rate changed similarly with the values obtained using theoretical simulation. These changes occurred simultaneously with the change in speed of the blood pump over time, and the driving control target value was reached within 4 seconds.
Based on the startup dynamics model axial flow blood pump system driven by large gap magnetic force, a method that realized optimal control in the blood pump acceleration by using VB programming was brought forward. In this method, Runge-Kutta algorithm was used to solve and analysis the model and the result that speed, flow and net head etc. time-domain curves were obtained; further more, suitable stable points in the speed curve could be found by setting threshold, and finally Hermite interpolation was used to discrete speed curve in conditions to get time constant and steps etc. speed control parameter. Relative to the old method for the speed control of blood pump, it is more accurate and controllability condition.
A minimum-energy consumption control algorithm is applied to motion control of a four-wheel drive omni-directional mobile robot (FDOMR) in actual application environment. After establishing the robot’s dynamic equations with motor model, we have chosen a practical cost function as the total energy drawn from the batteries. Considering the translation and rotation, we have found out the velocity curve by optimal control theory. Various simulations are performed and the consumed energy is compared to the normal control method that without minimum energy consumption control. Simulation results reveal that the energy saving is much more compared to the traditional control, the operational time of the FDOMR with given batteries is lengthened and the efficiency of battery is improved.
This paper presents a study of energy-efficient operation of vehicles with electrified powertrains leveraging route information, such as road grades, to adjust the speed trajectory. First, Pontryagin’s Maximum Principle (PMP) is applied to derive necessary conditions and to determine the possible operating modes. The analysis shows that only 5 modes are required to achieve minimum energy consumption; full propulsion, cruising, coasting, full regeneration, and full regeneration with conventional braking. The minimum energy consumption problem is reformulated and solved in the distance domain using Dynamic Programming to optimize speed profiles. A case study is shown for a light weight military robot including road grades. For this system, a tradeoff between energy consumption and trip time was found. The optimal cycle uses 20% less energy for the same trip duration, or could reduce the travel time by 14% with the same energy consumption compared to the baseline operation.
Optimization of Energy-Efficient Speed Profile for Electrified Vehicles