Research team

Constrained Systems Lab (CoSys-Lab)

Expertise

As the control algorithms directly drive the actuators, the controllers have a significant influence on the productivity, energy usage, quality and robustness requirements. To optimize the performance of the control towards these requirements, the research of professor Derammelaere considers four essential topics. First of all, his attention goes to the control algorithm and more important control architecture. Secondly, research on the optimal selection, optimal placement of sensors and implementation of feedback estimation guarantees cost-efficient control. Furthermore, next to control optimizations, trajectory optimization improves the energy-usage and output of the machine. Finally, all these issues; control architecture, feedback optimization and estimation and path planning deliver the best results if a reliable dynamic system model is available.

Nexor - Cyber-Physical Systems for the Industry 4.0 era 01/01/2021 - 31/12/2026

Abstract

The fourth industrial revolution (Industry 4.0 as it is commonly referred to) is driven by extreme digitalization, enabled by tremendous computing capacity, smart collaborating machines and wireless computer networks. In the last six years, Nexor — a multi-disciplinary research consortium blending expertise from four Antwerp research labs — has built up a solid track record therein. We are currently strengthening the consortium in order to establish our position in the European eco-system. This project proposal specifies our 2021 - 2026 roadmap, with the explicit aim to empower industrial partners to tackle their industry 4.0 challenges. We follow a demand driven approach, convincing industrial partners to pick up our innovative research ideas, either by means of joint research projects (TRL 5—7) or via technology licenses.

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Project website

Dotation for the structural collaboration with Flanders Make. 01/01/2021 - 31/12/2021

Abstract

Flanders Make's mission is to strengthen the international competitiveness of the Flemish manufacturing industry on the long term through industry-driven, precompetitive, excellent research in the field of mechatronics, product development methods and advanced production technologies and by maximizing valorisation in these areas.

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Sparse interpolation potential in multidimensional optimisations. 01/10/2020 - 30/09/2024

Abstract

In engineering, simulations access the impact of design changes applied by engineers. Based on the simulation output, the engineer improves the design. Recently the engineer is replaced by an optimisation algorithm which adapts the design parameters to ensure optimal performance. Literature mentions case where applying these optimisation techniques leads to performance improvements up to 33%. However, current state-of-the-art very often relies on heuristic optimisation techniques. While they are easily applicable and suited for many applications, they cannot at all guarantee to find the global optimum. In contrast to local optima, the global optimum is the unique design which guarantees the best performance. Using optimisers which cannot guarantee the global optimum very often result in an untapped potential of up to 18%. Nevertheless, heuristic optimisers are still preferred in engineering as they do not require an exact mathematical model of the objective function. We propose to identify the mathematical model based on simulations standard in engineering practice. These simulations can deliver samples of the objective function based on specific settings for the design parameters. However, if the number of design parameters increases, providing an accurate grid of objective function samples becomes inconceivable as the number of necessary simulations explodes. Recent advances, obtained by the co-promotor, in multidimensional sparse interpolation will be a game-changer as it would reduce the number of necessary samples to an absolute minimum. By doing so, the underlying mathematical model we identify the objective function. This enables exact global optimisation. The research in this proposal will result in a generic workflow starting from commonly used simulations leading to a model via multidimensional sparse interpolation. The fact that such a model will enable global optimisation of engineering problems with multiple design parameters will be a genuinely fundamental novelty for the mechatronic state of the art.

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Towards optimal design, trajectories and control for repetitive movements. 01/11/2019 - 30/10/2021

Abstract

There is a strong desire to maximize the efficiency or speed of industrial machinery. Designers of machines, performing repetitive motions, often only define the position start- and endpoint of a movement and not the exact position function. This flexibility opens the opportunity to optimize the trajectory of the mechanism. Moreover, for the machine design itself, machine builders often rely on standard components and dimensions. The effect of the geometric design on the optimal trajectory and energy need of the system is very often neglected. The literature mentions cases where ad-hoc optimizations reduce energy usage up to 39% thanks to trajectory and geometric optimization. This project will use available CAD models and sparse interpolation to extract a closed mathematical system property description. This will enable using an interval optimization technique which can guarantee to find the one true global optimal geometric design and trajectory. The knowledge of the system properties will be used to design a robust controller to ensure the machine follows the desired trajectory. Finally, any mismatch between the virtual and real model will be detected with online tracking techniques to assure the machine operation remains optimal. The potential impact for machine builders is high as this project enables them to construct machines with a reduced total cost of ownership or allow them to perform a task as fast as possible purely based on their readily available CAD models.

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Optimal machine design by means of virtual engineering (OPTIMOTION) 01/10/2019 - 30/09/2021

Abstract

The overarching objective of this project is to develop tools and train the members of the target group in their usage. These tools should allow the straightforward optimisation of the mechanical design of machines and their drive system. Optimizing the placement of essential machine parts and optimising the selection of drive components can minimize the drive torque required for a machine. This optimisation potential can be fully exploited if the motion controller that drives the whole is properly tuned. All Flemish companies involved in mechanical engineering operate in a competitive market and are under pressure to optimise their machine design. More specifically, this project is aimed at machine builders, engineering consultants and suppliers of drive components and CAD software.

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Development of a priori and online trajectory optimisation for repetitive motions. 01/10/2018 - 30/09/2022

Abstract

As global energy demand will continue to rise and man's negative impact on global warming is known to be a fact, there is a strong desire to minimise the energy usage of industrial machinery. A significant opportunity lies in optimisations which do not require any adaptations or investments in installed hardware such as trajectory optimisation. Machine builders and users often only define the time to move from one point to another and the position of start- and endpoint. The exact position as a function of the time, or position function, in between these two points is very often not an issue for machine users. This flexibility opens the opportunity to optimise the position function. The literature mentions cases where ad-hoc optimisations reduce the energy usage of machinery used for repetitive tasks up to 50% by choosing optimised trajectories over the usual standard movement profiles. However, there is no scientific consensus on a computationally efficient technique which can guarantee to find the global optimum for systems with position varying mechanical load properties. Therefore, this project will assess the use and implementation of direct calculus optimisation. Applying this pure mathematical technique based on symbolic methods of trajectory optimisation would be a genuinely fundamental novelty, especially for machines with position varying dynamics. For one thing, this would eliminate the necessity of time-consuming iterative optimisations. On the contrary, direct calculus methods would lead to closed mathematical functions for the position function. To enable the use of this direct calculus methods, closed mathematical equations, describing the position-varying mechanical load properties, will be necessary. Obtaining such functions can be done theoretically based on Lagrange formulations. However, such an approach is not feasible in practice where the complexity of the machinery hampers analytical analysis. On the other hand, machine builders increasingly rely on CAD multibody software to design their machines. The promotor has expertise in extracting data by applying specific simulations on these virtual CAD models. The sampled data, obtained in this way, can be translated to explicit formulas, based on the expertise of the co-promotor. Developing such a technique to transform the sampled data to closed mathematical equations will be a core challenge of the project and the major enabler to apply direct calculus optimisation. Furthermore, to guarantee the machine still operates at its optimum if machine behaviour changes during operation, an online tracking method is necessary. For this purpose, the knowledge of the promotor on tracking the position dependency of machine parameters online in the frequency domain is essential. The data samples obtained in this way will again be translated to a mathematical description to allow a re-optimisation of the trajectory. For this purpose, the direct calculus optimisation method will be advantageous as it defines the optimised path as a function of position varying parameters. This definition enables direct re-optimisation. Moreover, where the current state of the art focusses on offline a priori or online optimisation, facilitating online re-optimisation based on a priori offline determined information will be another fundamental novelty of this project.

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Opticharge. 01/12/2018 - 30/11/2020

Abstract

In the Opticharge project existing technological solutions for the automation of loading and unloading operations will be analysed, as well as innovative solutions that are being developed by technology providers, research institutions and universities. In a next step, these technologies will be linked to the needs of the companies participating in the project. These needs are examined by the Flemish Institute for Logistics. The project also includes the development of a tool, presented in a matrix, in order to calculate the ROI of the different technological solutions.

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High-efficiency Sensorless Control of a BLDC Motor using Sinusoidal Currents. 01/07/2018 - 31/12/2019

Abstract

A Brushless DC Machine (BLDC) is the optimal motor to use in applications where a more or less constant, controlled, high rotational speed is required. Typical examples include driving: the compressor of a cooling system including refrigerators and air-conditioning, the propellers of a drone, fans and pumps in general, …. The BLDC is responsible for the lion share energy usage of these applications. Moreover, cooling systems consume a lot of energy worldwide because of their ubiquitous presence. On the other hand, for battery fed systems such as drones there is strong desire for increased autonomy. This means there is a strong desire to reduce the energy usage of BLDC driven systems. BLDC motors are typically driven with a square wave current. On the other hand, using sine wave currents could result in an energy efficiency increase of 10%. However, typical BLDC algorithms lack feedback to drive the machine with sine waves. Using an encoder to obtain this position feedback would increase the cost and complexity of the drive system and can be impossible due to limited mounting space. Therefore, so-called sensorless algorithms which estimate feedback signals based on easily measurable voltage and current signals, are of interest. Consequently, the central research question of this STIMPRO is formulated as: Develop and implement a sensorless algorithm to provide feedback for a BLDC drive algorithm using sinusoidal current waveforms and validate its energy saving potential. As a starting point this STIMPRO will consider an estimation algorithm, developed by the promotor, for stepping motors, to use in BLDC drives. This STIMPRO will be used as a kick-start to initiate electrical motor control research at UAntwerp. This project will serve as leverage to move the activities off the promotor in motor control, who started at ZAP at UAntwerp the 1st of September 2018, previously established at UGent to UAntwerp. To do so, the STIMPRO will be used to hire a researcher who will submit an FWO SB proposal. However, if FWO funding is rejected we will not finish this project empty handed. Given the work plan defined in the STIMPRO, and the experience of the promotor the project will certainly result in publications, a test bench, added experience for the hired researcher and the exploration of possible bilateral collaboration with Flemish companies on the subject. The work done in this STIMPRO will be beneficial for the Op3Mech research group as adding research on electrical motors is a vital in the broader robotics research. Moreover, the education on drivelines at the Faculty of Applied Engineering is currently not supported by academic research. Therefore, the research activities initiated in this STIMPRO are vital to continue education on these topics.

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