CAD enabled trajectory optimization and accurate motion control for repetitive tasks

As machine users generally only define the start and end point of the movement, a large trajectory optimization potential rises for single axis mechanisms performing repetitive tasks. However, a descriptive mathematical model of the mecha- nism needs to be defined in order to apply existing optimization techniques. This is usually done with complex methods like virtual work or Lagrange equations. In this paper, a generic technique is presented to optimize the design of point-to-point trajectories by extracting position dependent properties with CAD motion simulations. The optimization problem is solved by a genetic algorithm. Nevertheless, the potential savings will only be achieved if the machine is capable of accurately following the optimized trajectory. Therefore, a feedforward motion controller is derived from the generic model allowing to use the controller for various settings and position profiles. Moreover, the theoretical savings are compared with experimental data from a physical set-up. The results quantitatively show that the savings potential is effectively achieved thanks to advanced torque feedforward with a reduction of the maximum torque by 12.6% compared with a standard 1/3-profil

Towards a generic optimal co-design of hardware architecture and control configuration for interacting subsystems

In plants consisting of multiple interacting subsystems, the decision on how to optimally select and place actuators and sensors and the accompanying question on how to control the overall plant is a challenging task. Since there is no theoretical framework describing the impact of sensor and actuator placement on performance, an optimization method exploring the possible configurations is introduced in this paper to find a trade-off between implementation cost and achievable performance. Moreover, a novel model-based procedure is presented to simultaneously co-design the optimal number, type and location of actuators and sensors and to determine the corresponding optimal control architecture and accompanying control parameters. This paper adds the optimization of the control architecture to the current state-of-the-art. As an optimization output, a Pareto front is presented, providing insights on the optimal total plant performance related to the hardware and control design implementation cost. The proposed algorithm is not focused on one particular application or a specific optimization problem, but is instead a generally applicable method and can be applied to a wide range of applications (e.g., mechatronic, electrical, thermal). In this paper, the co-design approach is validated on a mechanical setup