CAD Enabled Trajectory optimization and Accurate Motion Control for Repetitive Tasks

Oosterwyck, Nick Van; Vanbecelaere, Foeke; Haemers, Michiel; Ceulemans, David; Stockman, Kurt; Derammelaere, Stijn

doi:10.1109/icca.2019.8899728

Cited by 19 publications

(28 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The papers mentioned above applied the Genetic Algorithm only to determine the optimal feedback gains. A Genetic Algorithm can also be used to optimize the trajectory for single-axis mechanisms performing repetitive tasks [40].…”

Section: A Genetic Algorithm Meets the Optimization Problem Requirementsmentioning

confidence: 99%

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

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

show abstract

Section: A Genetic Algorithm Meets the Optimization Problem Requirementsmentioning

confidence: 99%

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Awareness about the influence of machine components geometry on energy consumption has recently attracted attention [6,7,8]. Mechanism models [9,10] replace the prototyping, allowing computational evaluation of multiple designs with limited costs. A coronaventilator is used as a validation case within this study.…”

Section: Introductionmentioning

confidence: 99%

CAD Based Design Optimization of Four-bar Mechanisms: a coronaventilator case study

Yahya¹,

Oosterwyck²,

Knaepkens³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Design optimization of mechanisms is a promising research area as it results in more energy-efficient machines without compromising performance. However, machine builders do not actually use the design methods described in the literature as these algorithms require too much theoretical analysis. Moreover, the design synthesis approaches in the literature predominantly utilize heuristic optimizers leading to suboptimal local minima.This research introduces a convenient optimization workflow allowing wide industrial adoption, while guaranteeing to reveal the global optimum. To guarantee that we find the global optimum, a mathematical expression of the constraints describing the feasible region of possible designs is of great importance. Therefore, kinematic analysis of the point-to-point (PTP) planar four-bar mechanism is discussed to obtain the static and dynamic constraints. Within the feasible region, objective value samples are generated through CAD multi-body software. These motion simulations determine the required torque to fulfill the movement for a certain combination of design parameters.

show abstract

“…Genetic algorithms have previously been implemented for many applications, for instance, to determine the optimal controller tuning parameters using a Linear Quadratic Regulator (LQR) [15], H ∞ control [16], or proportional integral (PI) control [17]. Another field of application of Genetic Algorithms is the motion profile optimization for repetitive machines [18].…”

Section: Introductionmentioning

confidence: 99%

Optimal Hardware and Control Co-Design Applied to an Active Car Suspension Setup

et al. 2021

Self Cite

View full text Add to dashboard Cite

For complex systems, it is not easy to obtain optimal designs for the hardware architecture and control configurations. Every design aspect influences the final performance, and often the interactions of the different components cannot be clearly determined in advance. In this work, a novel co-design optimization method was applied that allows the optimal placement and selection of actuators and sensors to be performed simultaneously with the determination of the control architecture and associated controller tuning parameters. This novel co-design method was applied to a state-space model of a downscaled active car suspension laboratory setup. This setup mimics a car driving over a specific road surface while active components in the suspension have to increase the driver’s comfort by counteracting unwanted vibrations. The result of this co-design optimization methodology is a Pareto front that graphically represents the trade-off between the maximum performance and the total implementation cost; the co-design results were validated with measurements of the physical active car suspension setup. The obtained controller tuning parameters are compared herein with existing controller tuning methods to demonstrate that the co-design method is able to determine optimal controller tuning parameters.

show abstract

CAD Enabled Trajectory optimization and Accurate Motion Control for Repetitive Tasks

Cited by 19 publications

References 22 publications

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

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

CAD Based Design Optimization of Four-bar Mechanisms: a coronaventilator case study

Optimal Hardware and Control Co-Design Applied to an Active Car Suspension Setup

Contact Info

Product

Resources

About